Information Technology for Enterprise Asset Management: An ...

Information Technology for Enterprise Asset Management

An Assessment Guide

EPRI Project Manager J. Bloom

ELECTRIC POWER RESEARCH INSTITUTE 3420 Hillview Avenue, Palo Alto, California 94304-1338 • PO Box 10412, Palo Alto, California 94303-0813 • USA

800.313.3774 • 650.855.2121 • [email protected] • www.epri.com

Information Technology for Enterprise Asset Management An Assessment Guide

1012527

Final Report, March 2007

DISCLAIMER OF WARRANTIES AND LIMITATION OF LIABILITIES

THIS DOCUMENT WAS PREPARED BY THE ORGANIZATION(S) NAMED BELOW AS AN ACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER RESEARCH INSTITUTE, INC. (EPRI). NEITHER EPRI, ANY MEMBER OF EPRI, ANY COSPONSOR, THE ORGANIZATION(S) BELOW, NOR ANY PERSON ACTING ON BEHALF OF ANY OF THEM:

(A) MAKES ANY WARRANTY OR REPRESENTATION WHATSOEVER, EXPRESS OR IMPLIED, (I) WITH RESPECT TO THE USE OF ANY INFORMATION, APPARATUS, METHOD, PROCESS, OR SIMILAR ITEM DISCLOSED IN THIS DOCUMENT, INCLUDING MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, OR (II) THAT SUCH USE DOES NOT INFRINGE ON OR INTERFERE WITH PRIVATELY OWNED RIGHTS, INCLUDING ANY PARTY'S INTELLECTUAL PROPERTY, OR (III) THAT THIS DOCUMENT IS SUITABLE TO ANY PARTICULAR USER'S CIRCUMSTANCE; OR

(B) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY WHATSOEVER (INCLUDING ANY CONSEQUENTIAL DAMAGES, EVEN IF EPRI OR ANY EPRI REPRESENTATIVE HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES) RESULTING FROM YOUR SELECTION OR USE OF THIS DOCUMENT OR ANY INFORMATION, APPARATUS, METHOD, PROCESS, OR SIMILAR ITEM DISCLOSED IN THIS DOCUMENT.

ORGANIZATION(S) THAT PREPARED THIS DOCUMENT

B. Parkinson, Consultant

NOTE

For further information about EPRI, call the EPRI Customer Assistance Center at 800.313.3774 or e-mail [email protected].

Electric Power Research Institute, EPRI, and TOGETHER SHAPING THE FUTURE OF ELECTRICITY are registered service marks of the Electric Power Research Institute, Inc.

Copyright © 2007 Electric Power Research Institute, Inc. All rights reserved.

mailto:[email protected]

iii

CITATIONS

This report was prepared by

B. Parkinson, Consultant 4151 Baker Ave. Palo Alto, CA 94306

Principal Investigator B. Parkinson

This report describes research sponsored by the Electric Power Research Institute (EPRI).

The report is a corporate document that should be cited in the literature in the following manner:

Information Technology for Enterprise Asset Management: An Assessment Guide. EPRI, Palo Alto, CA: 2007. 1012527.

v

REPORT SUMMARY

Integration of business applications across the electric utility enterprise is a high priority in the electric utility industry to reduce information technology (IT) costs and realize a range of business benefits. Asset management, a business process that integrates with most other utility business processes, is particularly enhanced by an integrated information technology solution.

Background An integrated and automated solution for asset management—from data acquisition, to calculations, to reports that exploit a utility’s current IT system portfolio of enterprise systems from both commercial vendors and EPRI—can reduce the number of applications that need to be implemented and supported. Enabling efficient integration of applications across business units can greatly reduce information technology costs and yield a range of business benefits. Similarly, improved data collection using those systems can substantially reduce the number of evaluation hours for asset managers and increase the timeliness of information for decision makers.

Objectives To assist utility IT and asset managers in addressing the unique and particularly challenging needs for asset management and to focus on data collection, use of existing enterprise systems such as work management systems, and the integration and use of decision support systems for specific problems such as managing aging assets and monitoring asset health.

Approach The project focuses not only on the elements of an information technology plan, but also on the progression of its development. By describing in detail certain aspects of asset management program development, the project provides the context in which the information technology infrastructure must operate and grow as well as the asset management capabilities that result.

The project raises the bar for asset management considerably. While many companies can and should be happy with reaching an “organizing” level of maturity, the project lays out the potential for further growth beyond that point. This growth is within reach because many of these programmatic improvements not only have and are occurring in more competitive industries like manufacturing, but also because performance in the nuclear generation industry has improved dramatically, with some of that improvement attributable to process and information technology improvements that are described in this report. Some electric utilities have already begun to transfer nuclear power’s business improvements to other business units.

The project draws heavily from a number of EPRI asset management reports in both the power delivery and nuclear generation sectors. Each has a strong asset management research program at EPRI, characterized in particular by a broad degree of utility involvement.

vi

Results The report discusses critical topics for developing an asset management information technology infrastructure. Because the needs and capabilities of an enterprise IT depend strongly on its asset management program, the report provides a self-assessment method to help either an IT professional or an asset manager determine the current maturity level of their program. The method classifies asset management programs into five different levels of maturity using seven attributes with six sub-attributes to create a table of over 130 criteria for measuring development of an asset management program.

The report discusses over twenty critical topics for asset management information technology infrastructure, grouped by maturity level into three sections. Each topic contains a set of recommended steps for improving asset management information technology infrastructure and program that, when combined with the criteria, allow readers to develop a roadmap for program improvement based on the results of the self-assessment. Because of the breadth and depth of participation and experience in EPRI’s work on asset management in both the power delivery and nuclear sectors, this report represents a corresponding breadth and depth of asset management issues in an enterprise program.

Finally, the report highlights important current advances in information technology, especially related to the topic of service-oriented architecture, which seems particularly amenable to advanced forms of enterprise asset management. The importance of this evolving information technology discipline is demonstrated by the substantial investment made by vendors of enterprise asset management and enterprise resource planning systems. The report shows how EPRI’s asset management technology can facilitate such an architecture and how ongoing EPRI research can lead to even further possibilities for advances in asset management.

EPRI Perspective Much has been accomplished in asset management in the last ten years in terms of improved equipment reliability, reductions in cost, and increases in business process productivity. Advances have been made across the board in data, decision support, and results visualization. Asset management lessons learned abound in information integration and application. The collective experience of the industry is substantial, and as the report’s self-assessment method asserts, such experience lays the foundation for raising the bar for asset management to a new and even more effective level.

Keywords Asset management Asset manager Maintenance program Root cause analysis Performance monitoring Risk management PM basis database Reliability Information technology

vii

ACKNOWLEDGMENTS

The author would like to acknowledge the contributions of the many people who contributed to this effort either by contributing directly to the report’s concepts or by contributing to the EPRI asset management reports and programs upon which a good portion of this report is based:

Vince Gilbert of Model Performance, LLC and Mitch Baughman of Duke whose leadership in Nuclear Asset Management community of practice enabled the nuclear asset management process description to be written with all its attendant benefits to asset management capabilities.

Maureen Coveney, of OSIsoft, who introduced the author to the true potential of Service Oriented Architecture and participated in the development of a number of the principles described in this report.

Serge Hugonnard-Bruyere of EdF, who contributed a number of concepts in this report including in particular the modularization method described in Chapter 5.

Jeremy Bloom of EPRI, who immediately understood the value and applicability of the PM Basis and who contributed descriptions of corporate value models and risk management.

Mike Lebow of Coplaner Consulting, the prime contractor for developing the Power Delivery Asset Management guidelines and process models, the source of many insights as well as some notable words in this report.

David Worledge of Asset Performance Technologies, Inc., the prime source of material and the creative force behind the failure data models upon which important portions of this proposed approach is based.

John Gaertner of EPRI, whose continued support and encouragement and sage advise enabled many of these ideas to formulate into EPRI research.

Rick Grantom, Drew Richards, Ernie Kee and Alice Sun of South Texas Project and Jose Gomez Moreno and Luis Adriano Gerez Martin of Iberinco for their comments and contributions to asset management requirements in general and to visualizations of asset management results in particular.

Tina Taylor, Ron King and Nikki Delse of EPRI, whose many discussions and cooperative work inspired a number of important developments.

ix

CONTENTS

1 INTRODUCTION ....................................................................................................................1-1

2 ASSESSING MATURITY OF INFORMATION TECHNOLOGY FOR ENTERPRISE ASSET MANAGEMENT............................................................................................................2-1

Asset Management Program Maturity Levels .......................................................................2-2 Reacting ...........................................................................................................................2-3 Awakening ........................................................................................................................2-3 Organizing ........................................................................................................................2-4 Processing........................................................................................................................2-4 Continuously Improving ....................................................................................................2-5

The Attributes of an Enterprise Asset Management Program...............................................2-6 Data Collection and Use...................................................................................................2-6 Performance Monitoring ...................................................................................................2-8 Decision Support Capability – General.............................................................................2-8 Decision Support Capability – Investments ....................................................................2-10 Decision Support Capability – Risk Management ..........................................................2-11 Business Process Documentation and Modeling ...........................................................2-11 Information Technology Infrastructure – General ...........................................................2-12 Information Technology Infrastructure – Knowledge Management ................................2-12 Business Analysis Role ..................................................................................................2-13 Management Support .....................................................................................................2-13

The Maturity Index Table of Criteria ....................................................................................2-14

3 FIRST STEPS FOR NEWCOMERS TO ENTERPRISE ASSET MANAGEMENT.................3-1 Developing a Good Asset Inventory......................................................................................3-2 Collecting Asset Equipment Data..........................................................................................3-5 Developing and Employing Analytical Models.......................................................................3-9 Employing Decision Support Tools .....................................................................................3-15

x

4 BREAKING OUT TOWARD A MATURE ENTERPRISE ASSET MANAGEMENT PROGRAM ................................................................................................................................4-1

Increasing Sophistication of Decision Support Tools ............................................................4-2 Ranking and Screening ....................................................................................................4-2

Ranking........................................................................................................................4-2 Screening.....................................................................................................................4-4

Systems-Level Thinking ...................................................................................................4-7 Project Prioritization and Investment Tools ......................................................................4-8

Improving Data Quality and Integration...............................................................................4-11 Developing a Data Strategy............................................................................................4-12 Ensuring Efficiency of Data Collection and Management...............................................4-15 Ensuring Data Quality.....................................................................................................4-16 Providing For Data Access .............................................................................................4-16

Employing Critical Systems in the Information Technology Infrastructure ..........................4-17 Asset Management Technology Ownership...................................................................4-18 Data Mining Capability....................................................................................................4-19

Data Acquisition Layer...............................................................................................4-20 Algorithms and Applications Layer.............................................................................4-21 Reporting Layer .........................................................................................................4-23

5 ADVANCED INFORMATION TECHNOLOGY CONCEPTS FOR ENTERPRISE ASSET MANAGEMENT............................................................................................................5-1

Information Technology Infrastructure for Advanced EAM....................................................5-1 Use of a Service Oriented Architecture in Enterprise Asset Management .......................5-2

The Natural Role of SOA in Asset Management .........................................................5-2 Business Processes and Models .................................................................................5-4 Enterprise Asset Management Repository of Services................................................5-6

Library of Algorithms and Calculations....................................................................5-7 Modular Approach to Asset Management...............................................................5-7 Visualization Services ...........................................................................................5-10

Automating Service Implementation – Re-usable Analytics ......................................5-16 Methodology..........................................................................................................5-17 Architecture for Re-usable Analytics .....................................................................5-18 Applicability to Asset Management .......................................................................5-20 Proof-of-Concept...................................................................................................5-20

xi

Knowledge Management................................................................................................5-22 Advanced Concepts in Data and Decision Support Tools...................................................5-24

Accurate and Useful Cost Data ......................................................................................5-25 Advanced Decision Support Tools .................................................................................5-26

Long-Range Planning ................................................................................................5-27 Use of Simulation in Asset Management ...................................................................5-28 Opportunities for Estimating Failure Rates in Real Time...........................................5-29 Use of Risk Management...........................................................................................5-30

Risk Management for the Asset Manager.............................................................5-31 Risk Management for Asset Operator...................................................................5-31 Information Technology Requirements for Risk Management ..............................5-32 Illustrative Example for Risk Management............................................................5-33

6 CONCLUSIONS AND RECOMMENDATIONS ......................................................................6-1

7 REFERENCES .......................................................................................................................7-1

A SURVEY OF EXISTING ASSET MANAGEMENT CAPABILITY ......................................... A-1 List of Asset Management Capabilities ................................................................................ A-1

xiii

LIST OF FIGURES

Figure 3-1 EPRI PM Basis Database: Equipment Model Example............................................3-7 Figure 4-1 Three Layer Architecture for Asset Management and Performance Monitoring.....4-20 Figure 5-1 Asset Management Module Interaction ....................................................................5-8 Figure 5-2 Example Calculation Chain Decomposition..............................................................5-9 Figure 5-3 Example Reliability-Profitability Phase Plane Graph ..............................................5-11 Figure 5-4 Example Cumulative Benefit NPV Versus Cumulative Investment NPV Graph.....5-12 Figure 5-5 Example Project Uncertainty Comparison Graph...................................................5-13 Figure 5-6 Example IRR Versus Risk of Loss Graph...............................................................5-14 Figure 5-7 Example IRR Uncertainty Histogram......................................................................5-15 Figure 5-8 General Analysis Flow of a Calculation ..................................................................5-17 Figure 5-9 General Architecture of Re-Usable Acquisition Component...................................5-19 Figure 5-10 General Analytic/Calculation ................................................................................5-19 Figure 5-11 Circuit Breaker Measurements .............................................................................5-21 Figure 5-12 Influence Diagram for Tree Trimming Example....................................................5-35 Figure 5-13 Tornado Diagram Displaying the Sensitivity Analysis for Tree Trimming

Example ...........................................................................................................................5-36 Figure 5-14 Ten-Year SAIDI Scatter Plot Illustrates Useful Historical Data for Risk

Assessment......................................................................................................................5-37 Figure 5-15 An Analytical Risk Model (Curves) Shows the Results of a Probabilistic

Analysis, Compared with the Deterministic Expected Values (Vertical Lines) .................5-38 Figure 5-16 Portfolio Risk Trade-Off (Efficient Frontier) ..........................................................5-39

xv

LIST OF TABLES

Table 2-1 The Maturity Index Table .........................................................................................2-15 Table 4-1 Comparative Characteristics of Asset Management Analysis Levels 1, 2, and 3 ......4-5 Table 5-1 Information Available from Circuit Breaker Counts and Related Parameter

Data..................................................................................................................................5-21

1-1

1 INTRODUCTION

Integration of business applications across the electric utility enterprise has become a high priority in the electric utility industry to reduce information technology costs and realize a range of business benefits. Asset management, a business process that integrates with most other utility business processes, is particularly enhanced by integrated information technology (IT) solutions. Asset management is also a discipline that is developing a variety of new business applications to facilitate decision-making, and it depends on those applications integrating with enterprise systems such as work management, operations data historians, and financial systems.

Integrating and automating solutions to asset management (from data acquisition, to calculations, to reports) that exploit a utility’s current IT system portfolio of enterprise systems from both commercial vendors and EPRI can reduce the number of applications. Enabling efficient integration of applications across business units can greatly reduce information technology costs and yield a range of business benefits. Similarly, improved data collection using those systems can substantially reduce the number of evaluation hours for asset managers and increase the timeliness of information for decision makers.

The propose of this report is to provide guidance to assist utility information technology managers and asset managers alike in addressing the unique and particularly challenging IT needs for asset management. This project devotes particular emphasis to data collection, use of existing enterprise systems such as work management systems, as well as the integration and use of decision support systems for specific problems such as managing aging assets and monitoring asset health.

This report focuses not only on the elements of an information technology plan but also on the progression of their development. By describing in detail certain aspects of asset management program development, the report provides the context in which the information technology infrastructure must operate and grow as well as the asset management capabilities that will result.

This report also raises the bar for asset management considerably. While many companies can and will be satisfied with reaching a certain level of maturity in asset management, described here as “Organizing,” this report lays out the potential for further growth beyond that point. This growth is within reach because many of these programmatic improvements have and are occurring in more competitive industries like manufacturing. But further evidence in their attainability lies in the improvements demonstrated in the nuclear generation sector. Performance in that sector has increased dramatically, and some of that increase certainly can be attributed to the process improvements and information technology improvements that are described in this report. Some electric utilities have already begun to transfer nuclear power’s business improvements to other business units.

Introduction

1-2

Chapter 2 of this report provides an approach for determining the maturity of asset management programs in an electric utility and their associated information technology needs. Performing a self-assessment against this maturity index will help an electric utility determine how to develop information technology for an enterprise asset management program.

In Chapters 3 through 5, this report discusses important topics related to the improvement (or maturation) of an asset management program and its associated information technology infrastructure. At the end of each topic, the report summarizes the discussion in terms of a bulleted list of steps or concepts. Together with the criteria in the Maturation Index Table discussed in Chapter 2, the report attempts to provide a concise description amenable to self-assessment and developing an asset management information technology program.

This report has drawn heavily from a number of EPRI asset management reports cited in the References, Chapter 7. These reports were developed in both power delivery and nuclear generation sectors. Each of those sectors has strong asset management research programs at EPRI, characterized in particular by a broad degree of utility involvement.

2-1

2 ASSESSING MATURITY OF INFORMATION TECHNOLOGY FOR ENTERPRISE ASSET MANAGEMENT

This chapter lays out an approach for determining the maturity of asset management programs in an electric utility. Performing a self-assessment against this maturity index will help an electric utility determine how to develop information technology for an enterprise asset management program.

This maturity index approach is built upon the concept that an asset management program and its supporting information technology program must develop in concert. If one develops faster than the other, inefficiencies will result. Either asset managers will be unable to get the information they need, or they will spend time feeding information systems that they are incapable of using.

The maturity index also provides insight into the risks of programmatic setbacks that are inevitable in a business transformation of the kind entailed in asset management. Accepting setbacks and turning them into opportunities for organizational growth is an important precondition to successful enterprise asset management. In this regard, the maturity index also includes criteria related to management support and the role played by business analysts in the company. Both of these organizational factors are important to business transformation.

The following maturity index is not a quantitative, percentile-based approach for self-assessment common to many utility benchmarking efforts. Few companies will fit in the highest “continuously improving” category. More than a quartile may fit in the lowest “reacting” category. Currently, there are no studies which have systematically evaluated the maturity of electric utility asset management practices upon which a quantitative quartile approach could be based.

Nevertheless, EPRI research programs provide sufficient experience upon which to base a useful maturity index and self-assessment approach for enterprise asset management. EPRI research reaches both member and non-member asset management practices across the full gamut of electric utility business units. Additionally, the Nuclear Asset Management industry working group, operated under the auspices of the Nuclear Energy Institute and actively supported by EPRI, provides excellent access to the practices and lessons learned of that portion of the electric power industry. It is upon this broad base of experience that this maturity index has been built.

The five-category maturity index is adapted from a similar approach developed by the Department of Transportation for asset management of roadways [1]. The DOT indices are in turn based on maturity indices for general application to software development [2-5].

Assessing Maturity of Information Technology for Enterprise Asset Management

2-2

However, there are two important changes:

1. the criteria of the indices in this report are specific to the electric utility industry, and

2. the criteria reflect recent developments in enterprise level information technology.

Finally, the criteria reflect the experiences of the author in developing asset management and associated information technology programs with a large number of generation and power delivery business units as well as the abovementioned EPRI and industry groups.

In this effort for enterprise asset management, we do not attempt to reach an exhaustive level of detail in the individual criteria. Largely, this choice reflects the fact that different business units at a utility often have uneven levels of experience with self-assessment. That said, this work is not intended to address the lowest common denominator. Rather, it is intended to be practical and effective in bringing multiple business units together to facilitate a successful enterprise asset management program. The level of detail is judged to be within reach of those with little institutional experience in self-assessment and yet still a valuable guideline for those at the high end of the self-assessment scale.

The remainder of this report chapter describes the levels of maturity and their implication to enterprise asset management and information technology development. We start with the important attributes of enterprise asset management programs, especially as they relate to information technology. Finally, we present the Maturity Index Table which provides criteria or representative characteristics for each attribute at each level.

In subsequent chapters of the report, we delve further into topics related to these criteria. Those topics are arranged in a manner to provide assistance in developing a roadmap for a more mature enterprise asset management program. While that discussion is at the insight level, the report makes reference to a variety of EPRI reports and other sources where the reader can look for further detail.

Asset Management Program Maturity Levels

This approach defines five levels of maturity for asset management programs:

1. Reacting

2. Awakening

3. Organizing

4. Processing

5. Continuously Improving

Essentially, these levels vary from no asset management program to a living asset management program.


2-3

In general, a company will have to move through each of the intermediate levels to reach a higher state. As with any approach to a difficult problem, it is important to take the long view on developing asset management capabilities. This long view does not mean that every step should be a small step. Rather, the long view means that every step should be measured, followed by an evaluation of results and possibly an adjustment to the overall plan. Employees are often skeptical of organization change, and asset management programs should be undertaken with the same management attention and determination given to other organization change initiatives.

The levels of maturity of an asset management program are most influenced by management interest and support in asset management. Because effective asset management is an integral part of good business management practice, asset management maturity is also strongly influenced by the organizational roles of business analysts and by management adoption and employee acceptance of a philosophy of continuous process improvement.

Information technology plays an equally important role in maturing asset management programs, over and above the fact that it is a focus of this report. The importance to asset management of accessing data and communicating information make information standards and data mining capabilities the veritable nervous system of a living AM program. Even more relevant are the work flows and business processes, the muscle behind turning AM concepts into productive work. These two parts of the information technology infrastructure are directed by decision support applications. These applications reside in the brain of AM. Not only do they perform the sometimes simple and sometimes sophisticated calculations that identify the best actions to take, but they also simulate the outcome of those actions.

The following paragraphs describe each of the maturity levels in business and information technology terms. Subsequently, the report describes more detailed attributes of maturity and displays them for each level in tabular form.

Reacting

A company in the “reacting” state is really not doing asset management. Executive management may well be actively skeptical about asset management techniques. Regulatory requirements dominate decision making. Consequently, few if any employees are willing to risk moving beyond strict adherence to regulatory requirements and manufacturer recommendations. If an infrequent, but high consequence event occurs, such as a major equipment outage, it will likely catch the company by surprise, and the recovery of company performance, and share price will be extended. Investments in information technology for asset management likely will fail or have little impact at the enterprise level. But, if the experience is maintained within the corporate culture, it will nevertheless be valuable when the company “awakens”.

Awakening

A company in the awakening phase has management that is aware of asset management goals and techniques. Such a company is much more likely to have risk-taking employees who undertake asset management initiatives at the “local” level. But a company that is “awakening” is also more likely to have initiatives that fail. Failure generally occurs because it is difficult for


2-4

a risk-taking employee to sustain asset management initiatives when they cross organizational boundaries. Capturing the lessons learned from the asset management activities undertaken by risk-taking employees is an important step in getting to the next level of maturity. If risk taking is discouraged by management, these failures could result in a fall back to the reacting phase.

At the awakening level, management and employees are getting used to the role of performance indicators in process improvement. However, because these indicators are typically incomplete and sometimes difficult to use, a dangerous skepticism to asset management can result if the indicators are not improved. Web based performance indicator systems are likely, but they probably lack associated drill down and analysis capability. These capabilities are probably provided by power users of such generic tools as Microsoft Office. Valuable asset management experience is starting to be gained with the use of equipment condition information, a broadening of the role of business analysts, and the development of business processes centered on use of Enterprise Asset Management (EAM) and Enterprise Resource Planning (ERP) systems. Information technology investments in asset management are still risky, but building blocks like EAM and ERP systems are starting to be exploited.

Organizing

An organized company is well along on the road to success for enterprise asset management. For the most part though, they have succeeded with power users employing isolated tools, by improving data access for a broad set of users, and as a result of increased management acceptance. The organized company has made one very important step toward a high level of maturity in enterprise asset management. They have overcome the inertia inherent in large EAM and ERP installations and have begun to establish a long-range plan for information technology improvement. Geographic Information Systems (GIS) and mobility data systems are being installed and integrated. The long-range plan includes a continuous improvement effort that encourages widespread input from employees. Because of the abovementioned successes, investments in asset management information technology are much more likely to generate benefits, even if the results are not always the exact ones that were initially planned.

A change in management can still threaten the asset management culture, because it is mostly leaders and power users who are exploiting the value of asset management techniques and the associated information technology investments. Nevertheless, benefits from asset management are accruing, and the foundation exists for a substantial improvement in asset management capabilities.

Processing

The processing company has recognized that a good asset management program involves not only enjoying successes in business improvements, but institutionally overcoming failures, including damaging ones. This attribute is in evidence in a number of ways. First, important pilot efforts are underway to improve both the information technology and the business processes surrounding and encompassing asset management. Second, cost data is created, collected, and analyzed with little or no angst or retribution. Third, organizations share information effortlessly, even while they compete internally to show the most improvement.


2-5

Specific to asset management information technology, data systems are rapidly maturing with costs dropping and benefits increasing. Part of this is improvements in technology, but a process approach to data that emphasizes quality is being built upon the experience gained from the use of condition and cost data and the associated lessons learned gained during the “organizing” phase. Performance monitoring and decision support tools are much better understood and relied upon. Simultaneous improvements in business process modeling, software integration, and knowledge management lay the foundation for further information technology improvements. But most importantly, the active support of executive management and the team work between business analysts and line management is reaping benefits and setting the stage for further improvement.

Continuously Improving

The continuously improving company is robust and can withstand and even benefit from changes in the marketplace and regulations as well as the occurrence of an infrequent adverse event. The strength of the company is in part due to the flexibility of its plans as well as completeness of its strategy.

Performance monitoring, analysis, and improvement are a way of life. Performance goals are set based on value, and their relationship to stakeholder goals is clearly specified, explicitly modeled, and measurable. Quality assurance and corrective action processes are widely embraced. Suboptimal performance is seen as a step toward optimal performance as opposed to a reason to start over.

Cost information is collected, verified, and analyzed at the individual asset and business process level. Condition information is integrated with degradation models and used in a wide variety of decision support tools. Optimization and simulation techniques are employed, as are risk management techniques.

Information standards and a Service Oriented Architecture (discussed in Chapter 5) are used to facilitate information technology that supports enterprise asset management. Business processes are not only modeled, but intimately associated with the IT infrastructure. Users can specify the asset management processes and techniques they want automated in their own terms, and the IT department can readily and accurately develop the corresponding services and integrate them into existing applications. Knowledge management techniques and tools are used systematically and in conjunction with decision making and decision support tools.

Executive and line management has ensured that asset management is part of the corporate culture. Business analysis is consistently and routinely done by many employees. Management understands uncertainty and risk, and investments are made accordingly. Managers of financial assets and managers of physical assets speak the same language and work together to find the right balance between the constraints of the market and the constraints of the physical plant.

As can be seen from the descriptions of the five maturity levels, investment in information technology for enterprise asset management is not without its challenges. The road from “reacting” to “continuously improving” can be a long one. Certainly setbacks will occur during


2-6

the trip. But if an incremental approach is taken and lessons are learned along the way, continuously improving will become the natural destination.

The Attributes of an Enterprise Asset Management Program

This report employs seven different attributes to describe important aspects of maturity in an enterprise asset management program. One attribute addresses the information technology infrastructure directly. Four attributes address enterprise asset management capabilities which require critical support from information technology. Two attributes address organizational factors, e.g., management support, which are critical in to the success of information technology investments. Of the seven total attributes, three have additional sub-attributes that allow the criteria to focus in on key capabilities.

The seven attributes and their corresponding sub-attributes are:

Data Collection and Use

– General Capability

– Condition Data

– Cost Data

Performance Monitoring

Decision Support Capability

– General

– Investments

– Risk Management

Business Process Documentation and Modeling

Information Technology Infrastructure

– General

– Knowledge Management

Business Analysis Role

Management Support

Data Collection and Use

Data collection and use is a critical attribute for an enterprise asset management program. Data needed for asset management is often input or collected by others in the organization. Maintenance workers record labor hours and describe the condition of equipment. Operators record log entries and the number of times equipment has operated. Reliability engineers report equipment failures. Most times these collectors of data have different uses for the data than asset managers. Because data is often not integrated between applications, basic things like equipment names often differ, yielding inconsistencies and making it difficult to merge data from different


2-7

sources. These problems are well known, even in the reacting or awakening organization, but they are difficult to solve without organizational willpower.

Often it is the installation of EAM, ERP, GIS, or mobility systems – the backbone of asset management information technology – that brings these problems to the fore. Those companies that let these difficulties diminish the value of information technology will not mature. Those companies which develop work processes to ensure data consistency and accuracy, which break down organizational barriers between collectors and users, and which increasingly expose the data to use by decision support tools will improve the value of data and mature their asset management programs. Standardized data mining tools often help to contribute to success. Equally important is a high quality asset inventory that is used in multiple applications. Being able to find a variety of sources of data for a specific piece or type of equipment will naturally expose issues that need resolution, as well as dramatically increase the value of the data that already exists or is being created in the backbone systems mentioned above.

Two kinds of data, asset condition information and cost information, are particularly important to the maturity of asset management programs, largely because developing them is difficult. Consequently, the maturity index drills down on these types of data.

Condition data, information that exposes the health or condition of a piece of equipment, naturally develops out of an improving maintenance program. Typically maintenance programs focus on equipment manufacturers’ recommendations or historical rules of thumb, often causing maintenance to be done a time intervals that are independent of the frequency and severity of use. Generally, maintenance planning will be the first asset management application of condition data. But condition data plays an important role in life-cycle planning as well. The most mature programs combine condition data with degradation models to predict asset performance.

Cost data poses other challenges. Electric utility cost data is typically driven by two considerations. Initially, cost data is collected primarily for regulatory performance reasons. As a result, costs of individual assets and business processes are difficult to determine. With the installation of EAM and ERP systems, more and better information becomes available. Yet the quality of cost data is still limited because of employee time charging practices. Generally, EAM systems are populated only with labor hours for maintenance personnel. Engineering and management charges remain as indirect costs. But since engineering and management time is often spent on problems or special projects, their costs are underestimated, and the net costs and benefits of such programs are inaccurately estimated. Another typical problem is that contracted charges are usually reported as lump sums rather than broken down by task, which can provide similar misleading indications.

An important step to maturity occurs not only when these problems are solved, but when costs are estimated by activity. Activity Based Costing (ABC) is a well-known and well-described discipline that can address this need. Another important process improvement is the use of cost estimating and cost control techniques.


2-8

Performance Monitoring

Performance monitoring, while typically done for other reasons, is an important discipline in the asset management process. Equally important, the information technology infrastructure to support it often has ancillary benefits to asset management functions, e.g., portals and data mining tools. But performance monitoring can also be an important barrier to maturity. Many performance indicators, even those developed with improving techniques such as Balanced Scorecard, can be developed without a clear and measurable link to value.

The first indication of maturity in performance monitoring occurs when more time is spent analyzing the meaning of the indicators than manually inputting data and calculating them. Once the real meanings of indicators are revealed by a record of analysis, three things can happen. First, more employees see the value of indicators and more are developed. Second, indicators become a part of a process for business improvement.

Finally, and perhaps ultimately most importantly to a mature enterprise asset management program, models of value develop. The most desirable model is a corporate value model (see EPRI reports 1001877, 1012954, 1012501) that is hierarchical in nature. Such a corporate model is based at the top on models of stakeholder goals and at the bottom on models of the performance of assets, e.g., degradation models. In the middle, the corporate value model must be clearly related to the model used in decision support tools for investments. Even if these models develop independently in different organizations, it is important that they develop. But eventually those models will have to be consistent across the enterprise.

From an enterprise perspective, there are two good opportunities for developing consistent modeling disciplines across business units. More and more executive management teams are comparing asset investment strategies across the enterprise, looking at investments as diverse as call centers for distribution in comparison to generating capacity increases at nuclear power plants. Another opportunity exists for maintenance and engineering departments in generation and transmission. Some of the highest valued assets in each are large transformers. Consistency here can save significant costs because monitoring these high valued assets can be expensive and the lessons learned from their use even more so.

Decision Support Capability – General

Decision support capabilities pose some of the most difficult problems in asset management, both from the information technology perspective and from the organizational perspective. From the information technology perspective, decision support capabilities provide intelligence or logic that should be embedded in EAM systems. From the organizational perspective, decisions on maintaining and replacing assets are often simple rules of thumb, typically based on time since the last maintenance activity or age of the equipment. Sometimes these rules of thumb are based on failures, e.g., replace a cable after two failures. These rules of thumb are easy to understand and easy to load into EAM systems.

Moving behind this stage requires overcoming barriers of management and employee understanding. Skepticism of new techniques that are difficult to understand is one aspect of the problem. But, many times the developers and users of decision support capability are insensitive


2-9

to the importance of the role played by gaining widespread understanding to achieving acceptance and use of those tools. Successful decision support capabilities often “communicate” in new, but understandable rules of thumb.

On the information technology side, many decision support tools desire or require integration with diverse sources of data, e.g., data beyond that included in EAM and ERP systems. Data stored in image or text form in design documents is a good example. Eventually, as decision support tools are applied to an increasing number of assets and as they increase their need for the amount and type of data, software integration becomes desirable. Only recently have EAM and ERP systems begun to open up their integration capabilities. Even if a utility has procured an open brand of EAM/ERP system, they still have to upgrade to obtain the capability. Consequently, the maturity of decision support capability eventually becomes linked to the maturity of information technology.

Another important concept behind the maturity of decision support tools is the maturity of the models upon which they are based. Maturity in models is best assessed by their sophistication and their documentation.

Typically models first develop in the awakening or organizing phase and are based on judgment of employees and facilitators. Such models have several distinguishing characteristics and corresponding limitations. First, the models tend to be qualitative and the basis for the relationship between the observed value and condition is often not clearly documented. Second, observed conditions are often limited to what is readily knowable rather than what is needed to be known. Third, when the models do have a number of concerns being represented, the criteria used to represent the concerns may overlap or even be contradictory. Fourth, the models may not be tied to specific actions corresponding to the conditions observed. Basically these types of models lack the rigor of an integrated and comprehensive view.

Maintenance planning tools often contain models with these types of limitations. The most common approach uses a “stop light” in which green, yellow, and red represent okay, watch, and act. The conditions that determine the light’s color in the maintenance planning tool are often related to the existence of one or more indications of component degradation. (A quantitative approach would estimate failure rate or remaining life based on observed equipment condition and a degradation model.) More than one negative indication typically produces a red light. (But these indications can easily overlap. For example, consider a case with two negative conditions, one caused by exceeding a code condition and one caused by a concern about personnel safety. If the basis of the code condition is also personnel safety, the same concern is counted twice.) A red light tells the maintenance planner that maintaining the component high priority. Because the component is high priority, the scheduled maintenance activities for that component are high priority. (An alternative is triggering the maintenance activities corresponding to the observed degradation mechanisms.)

However, use of models with limitations is part of a maturing asset management program. Gaining experience with these simple models and gaining management and employee acceptance are usually a precondition for obtaining the resources to develop more sophisticated models. A maturing asset management program must overcome organizational barriers like employee and management acceptance. Learning how to implement an approach with limitations is much more


2-10

valuable organizationally than learning nothing at all. Equally important, the “sub-optimal” approach is almost always better than the traditional approach based on rules of thumb.

Utilities will find it difficult to mature beyond the organizing level if they do not make a concerted effort to document the bases of their models. Sometimes this documentation can be developed as part of a knowledge management effort, including efforts to capture knowledge from an aging workforce. As the documentation improves, models used throughout the organization become more consistent and often times the same or similar models can be used for maintenance planning, project prioritization (investment), and even operations risk. When models become consistent with information standards and when they become compatible among finance, business processes, and equipment, a truly mature asset management program exists.

The organizational growth from simple to complex models follows the progression of decision support tools based on ranking to those based on optimization techniques. The simple to complex path also lends itself to the application of screening techniques. Screening techniques are critical because they allow simple models to be applied to simple problems, thereby reducing the cost and turnaround time for overall evaluations. Two or three levels of evaluation are probably sufficient with the lowest level being a bounding calculation and the highest level including detailed measures of uncertainty and risk.

Decision Support Capability – Investments

Decision support tools to evaluate investments, sometimes called project prioritization tools, are one of the most important aspects of asset management maturity. Project funding often starts based on regulatory requirements and capacity additions and, when additional capital is available, possibly some limited modernization. As maturity increases, regulatory projects no longer become sacrosanct but instead are evaluated more rigorously. The temptation to add project scope to beyond regulatory requirements often overwhelms the reacting and awakening company. But as projects become more precisely evaluated and broken into smaller parts with a variety of alternatives, it becomes organizationally easier to increase the sophistication of investment tools. Developing alternatives is important, but maintaining them in the investment decision process until final budgeting is even more crucial.

Another characteristic of a maturing asset management program is the increasing precision of long-range plans. Long-range plans help avoid surprises in increased expenditures and reduced levels of service because of the need to replace large assets. They also provide a repository for unfunded projects which may be good investments in other years. Long-range plans also provide a framework to monitor changes in technology and to create a vision for its incorporation into an electric utility asset base well known for its long life time. In a mature asset management organization, long-range plans include contingency plans for dealing with uncertainties.

Lastly, as companies mature, their focus on capital begins to extend to O&M. The extension first appears in large, infrequently scheduled O&M activities, like major overhauls or refurbishments. Since these do not necessarily occur in a levelized manner, it is inefficient to include them in a routine maintenance budget. Often O&M budgets are the same as or a percentage different from prior years. Said another way, they are not strictly based on need. But employees are less fungible than capital, so often there is logic to small swings in O&M budgets.


2-11

But companies with the flexibility to move people from O&M projects to capital projects, from one large asset to another or even from one department to another will have the greatest capability to take full advantage of asset management techniques. In a mature asset management organization, capital costs trade-off against O&M costs in a lifecycle cost analysis.

Decision Support Capability – Risk Management

Risk management is becoming an ever increasing expectation in our world. Shareholders want to know that needless risks are avoided and that unavoidable risks are being hedged or mitigated. Investors and regulators have seen that companies with risk management programs often run more efficiently. The public expects interruptions in service and sudden price increases to be minimized.

Risk management tends to enter the asset management program in higher levels of maturity. The data and modeling techniques needed are often the most complex of the tools in the asset manager’s toolbox. Similar to condition information and decision support tools in general, there is a logical progression from simpler and qualitative to more sophisticated and quantitative analysis of risks.

What is most important though is the understanding that uncertainties in investments are a risk that can be managed, often using the same basic concepts that financial asset managers use. Once a company can understand the level of risk it wishes to accept, with effort it can find an investment portfolio that matches that risk. Often it can identify hedges for the risks as well, ranging from traditional fuel futures contracts to additional research in new technology. Measuring the options value of projects, especially additional generation, is becoming more commonplace and is more often expected by investors and regulators alike.

Another aspect of risk is preparing for and, if possible, designing against so-called low probability, high consequence events. Often traditional engineering does not explicitly address these types of risks, whether they are natural events, such as hurricanes, or attributes of complex technological systems, such as our increasingly interconnected grid. Often these events lead to very high losses and dislocations, when prudent, low cost investments would have been available to prevent or mitigate them, had they only been increased in priority. Other times, prevention requires substantial investment; risk management techniques will help to make an informed decision on raising the capital necessary to implement them.


For the most part business process documentation and modeling has ranged from a capability many managers think is routinely done to an esoteric capability that management has little desire to sustain. But two important developments are changing that perspective. First, business process modeling has more and more been included in benchmarking initiatives that have led to important improvements in efficiency and consistency in company business practices. As utilities grow larger through mergers and acquisitions, common business processes are seen as a way to increase significantly economies of scale. More and more, utilities are realizing that modest and determined investments in business process modeling can yield improvements in


2-12

generating capacity and service levels as well as capturing knowledge from an aging workforce and demonstrating prudence to regulators.

The second important development is the increased use of business process models in the development of effective information technology infrastructures. Technologies like workflow have been particularly important to improving the effectiveness of EAM systems. New initiatives like Service Oriented Architectures (SOAs) are also dependent on good business process models. In this latter approach, described more fully in Chapter 5, business processes become the specification for information technology services like asset management. Because business processes can be described by asset managers, the “I’ll know if you’ve provided it when I see the software” syndrome can be better managed or hopefully even avoided altogether.

Information Technology Infrastructure – General

Of course, this chapter must turn directly to the infrastructure upon which the other asset management technology is based. Perhaps the best indicator of maturity of an asset management infrastructure is the success of investments in IT. That does not mean that more investment is necessarily better. Rather, it means that getting effective results out of the information technology infrastructure is a strong indicator of the readiness of that infrastructure to support the complexities associated with mature asset management programs.

Because of this strong relationship, this report recommends (see Chapter 6) that electric utilities strongly consider using asset management business processes as the test cases for some information technology infrastructure improvements, including for example Service Oriented Architecture, Knowledge Management, and Mobility Systems.

The first indication of maturing asset management programs is the successful installation of EAM and ERP systems. Here we stress the word successful. These installations are difficult and sometimes just getting them done is a success. But as we have mentioned above, seeing the installation progress to the point that good data is loaded and that new data is both valuable and accessible is equally important. For distributed assets, GIS capability becomes equally important. Mobility systems are likewise an indicator of maturity, as long as they are accompanied by a business process which ensures the data needed by asset managers is captured.

When moving into the processing and continuously improving phases of asset management programs, information standards facilitate a flexible architecture and go hand in hand with SOA and knowledge management. Similarly, improvements in integration techniques characterize the higher states of maturity. Finally, we have already pointed out the potential gains from information technology infrastructure when it is accompanied by business process modeling.

Information Technology Infrastructure – Knowledge Management

Knowledge management is important to asset management over and above the critical aging workforce concerns that the electric industry faces. Knowledge management also is an important enabler of model development and business process modeling, largely because capturing knowledge from people in a systematic way is so important to those efforts.


2-13

In this regard, it is important to truly recognize the amount of undocumented information that resides in employees heads about how business is done and how equipment functions and fails. Besides the areas we mentioned already, qualitative approaches to condition assessment and to risk management are often based on expert judgment. The more that information is captured when the organization is in the organizing phase, the easier it will be for the company to progress to higher levels of maturity. The reason is that knowledge capture techniques would first be piloted on simpler models and decisions, yet those decisions would be sufficiently business critical to gain organizational interest initially and organizational appreciation when the pilots are completed.

In the most mature asset management programs, knowledge is captured as a matter of course with rigorous methods. Similarly, such knowledge is easily accessed, verified, and modified by interested parties. Because that knowledge (otherwise known as “bases” for models and processes) is captured effectively, decision support tools can grow in complexity while at the same time remain understandable to managers and people who participate in the process.

Business Analysis Role

The last two attributes are truly organizational in nature. While they do not involve information technology directly, they do affect the likelihood that asset management information technology will be used effectively. This attribute relates to the organizational role played by business analysts. In less mature organizations, business analysts play a more traditional role involving reacting to management and line organizations. As organizations mature, business analysts work more closely with line organizations until they even become embedded directly in them. In the most mature asset management organization, the principals of value creation and costs are so well understood by employees as a whole that it almost seems like each employee is a part time business analyst.

Another aspect of the role of business analysis in a mature organization is reflected in changes to the budgeting process. Budgets move from annual to quarterly. Risks of exceeding the budget are known, and the conditions or events that underlie those risks are known and monitored.

Management Support

The second organizational attribute and quite probably the most important attribute in asset management is management support. Management benefits most from asset management techniques and associated information technology, largely because the type of information they need to do their jobs is provided in a more timely and accurate manner. But it is more than that. Management can better explain to shareholders and regulators why decisions were made as they were and the difference between bad luck and bad management can be distinguished. Another important benefit to management is realizing the value they receive from the investments they are making in the EAM and ERP systems that form the foundation of the enterprise asset management information technology infrastructure. Seeing that investment bear fruit is a critical part of their job performance.


2-14

Consequently, management support is a natural outcome as well as a natural precondition to asset management success. Asset management matures as management takes interest in the topic, whether it be because competitors use it or because they need to justify large IT investments. As management works through the organizational transformations in asset management, they become a critical part of the process, and executive sponsorship is necessary. In the highest level of maturity, executives, managers, and employees all become asset managers, and asset management processes themselves appear seamless.

The Maturity Index Table of Criteria

A company’s progression along these seven attributes will necessarily vary with management interest, employee capabilities, and information technology. It is expected, for example, that cost data collection and use may progress slower than general data collection capabilities or that risk management might lag other decision support capabilities. In part these differences in maturity can be due to different conditions or management priorities. For example, sometimes a moderate or high risk event must occur before management aggressively pursues risk management. A utility with financially oriented management may move earlier to improve cost data. Engineering oriented management may move more quickly with condition data.

This subsection provides a table of criteria to help measure what level of maturity exists for each attribute and sub-attribute. The criteria are intended to be useful for both self-assessment and guidance for improvement. The criteria were developed based on a variety of sources (1, 6) as well as previously mentioned experience of the author and the institutional experience of EPRI.

In subsequent chapters of this report, the report drills down on important topics organizing them loosely by level of maturity. Some other topics are described separately, in part because they are ubiquitous and in part because to avoid distraction from the more important topics. Chapter 3 describes first steps for those who fit the reacting and awakening maturity levels. For example, Chapter 3 focuses in on data collection and use and initial steps with decision support tools and performance monitoring.

Chapter 4 focuses on what is often the pivotal level of maturity, namely Organizing. At this level, the foundation of asset management information technology infrastructure is in place, namely the EAM and ERP systems. But the use of ranking techniques and condition information has also opened up new interest in improving the management of assets and greater understanding of the benefits and challenges involved.

Not many years ago, the Organizing level was close to the pinnacle of asset management maturity. But with the advent of improvements in management techniques and information technology, most of which are particularly pertinent to asset management, new opportunities exist for even more mature asset management programs. Chapter 5 talks about some of these improvements, the most important of which may be the Service Oriented Architecture.

All of the above mentioned topics in Chapters 3 through 5 are addressed by the criteria in the Maturity Index Table. The table puts them in perspective with how they fit into the bigger picture offered by all the seven attributes.


2-15

Table 2-1 The Maturity Index Table

Maturity Index Attributes

Reacting Awakening Organizing Processing Continuously Improving

Data Collection and Use - General Capability

Data collection and processing, including equipment and process failures, serves primarily regulatory requirements.

Inconsistent and unreliable data is the norm.

Investment made in data collection and processing capabilities often has limited influence beyond the immediate user and many investments fall short of their original goals.

Data collection capabilities include mobility data.

GIS capability, e.g., map-making capability, is in use.

Some success has been achieved in multi-organizational initiatives, e.g., operations and engineering.

Data mining tools begin to be standardized across business units

A well-thought-out and verified asset inventory, developed perhaps for EAM and ERP systems but used in multiple applications, helps to standardize access to data.

Data integration efforts have eliminated many inconsistencies and reduced data collection costs

Equipment data collection is primarily automated

Data collection and use is treated as a process, with data quality and feedback being key objectives.

Data collection activities increasingly include business processes.

Both the collectors and the users of data collection and processing systems are involved in system specification, development, use, and continued improvement.

Information collection standards play an important role in streamlining and improving data collection.


2-16

Table 2-1 (continued) The Maturity Index Table



Data Collection and Use - Condition Data

Little if any condition data is collected.

Condition data has limited and haphazard use in maintenance or operations and is infrequently used in engineering or lifecycle decisions.

Asset managers are aware of the variety of available condition information and have begun to tap that information for high valued assets and business processes.

A significant fraction of condition data is input by experts, but the associated processes are typically ad hoc.

Condition data is increasingly used with degradation models to predict asset performance.

Condition data and degradation models are well-linked and validated.

Use of expert opinion to generate condition information is well-controlled and well-understood

Data Collection and Use - Cost Data

Cost data is collected primarily for regulatory purposes.

Cost information is available in EAM/ERP systems but is not validated.

Cost data is not widely used, but may be used by selected departments or individual power users.

Cost information from EAM/ERP systems is mined and evaluated, but expenses like contractor, management, and engineering costs are not detailed.

Life-cycle costing is beginning to be done effectively.

Activity Based Costing is implemented and deficiencies in cost data collection are identified and evaluated for improvements.

Time charging by asset and business process is initiated.

Cost control techniques are routinely employed.

Variability in indirect costs is understood.

Cost information is collected, verified, and analyzed at the individual asset and business process level.

Time charging by asset and business process is company policy, and its importance is understood by employees.


2-17




Performance Monitoring Performance monitoring serves primarily regulatory requirements.

Performance Indicators exist, e.g., Balanced Scorecard, but few if any indicators are based on models and analysis of historical data.

Data processing to maintain the indicators often takes more time than is spent evaluating the indicator results.

Portals and data mining capabilities support performance monitoring and allow access to a much greater amount of data, but that data is primarily used by power users.

Performance monitoring is widely accepted.

Employees show individual initiative in developing and exploiting their own indicators

Performance indicator models become more commonplace.

Performance monitoring, analysis, and improvement are a way of life throughout the company.

Performance goals are set based on value and their relationship to stakeholder goals is clearly specified, explicitly modeled, and measurable.

Performance monitoring and resource allocation are clearly related.

Indicators of risk are monitored.


2-18




Decision Support Capability - General

Regulatory requirements tend to dominate decision making.

Rules of thumb and manufacturer recommendations are the basis for most asset repair and replacement decisions.

Staff skill and experience is the basis for most decisions and that knowledge is neither managed nor captured.

Decision support tools begin to be used by individual innovators. However, accurate input data and management acceptance lag.

Decision support capabilities include ranking and what-if analysis.

Screening or importance analysis techniques are used to determine the level of detail for decision support analysis.

Management awareness of decision support is increasing.

Ranking techniques are used extensively in maintenance planning.

Decision support tools are used widely, and managers and executives understand their bases and results.

Decision support tools for asset replacement and maintenance planning use condition information and degradation models.

Probabilistic information is beginning to be developed and used.

Data collection for ranking applications is increasingly automated, and management has confidence in the results.

Decision support capability includes optimization and simulation tools.

Models are used routinely for finance and business processes, as well as for individual assets and systems. These models are well documented for ease of understanding and update.

Staff skill and experience is incorporated in models and decision making as a matter of established process.


2-19




Decision Support Capability – Investments

Regulatory requirements dominate capital investment decisions for existing major assets.

O&M budgets are based primarily on prior year spending.

A process for evaluating capital investments exists, but the resulting investments are still strongly influenced by management preferences.

A robust ranking process emerges for capital investment.

Large O&M expenditures are separated from O&M budgets and ranked.

A robust set of alternatives are generated as projects are defined.

The strategic planning, project prioritization, and budgeting processes are well integrated, and decision support tools are used throughout.

Long-range plans for capital investment are developed and comprised of individual projects.

Alternatives are considered throughout the decision process.

Project cost and performance are measured and compared against original estimates.

Lifecycle costing is used to trade off capital and O&M expenses.

All major assets and asset types have long-range plans.

Investment risks and returns are evaluated and balanced.

O&M budgets are increasingly flexible in responding to changes in investment strategy.

The hidden costs, risks, and benefits of new technology are well understood.

Optimization techniques are employed in determining the project investment portfolio, including the selection of alternatives.


2-20




Decision Support Capability - Risk Management

Regulatory requirements, such a prudence, tend to dominate any use of risk management techniques.

Risk management techniques may be used by selected individuals and by safety and loss control groups.

Risk management is beginning to evolve beyond traditional loss control.

Project control groups estimate and monitor risk of project failure.

Qualitative techniques based on expert judgment are used selectively for ranking and managing important risks.

Risks may be systematically identified for some processes or business critical functions

Management is becoming familiar with the use of risk management techniques, including quantitative risk estimates.

Asset performance data, e.g., reliability and failure data, are incorporated into risk evaluations and other decisions.

Some qualitative risk techniques are improved with quantitative data and/or automation.

Decision support tools bring predictive and risk management capabilities to high risk operations.

Risk management techniques and tools are used routinely.

Quantitative risks are estimated for all business critical projects and threats.

Techniques to measure, model, and manage uncertainty are employed.

All business critical assets have risk management plans.

Risk mitigation and hedging are routinely used for large risks.


2-21





Regulatory requirements drive business process documentation.

No formal modeling is done.

Documentation of business processes is growing, but it is not done uniformly.

Most business processes are documented in procedures or equivalent documents.

Benchmarking is taking on growing importance as a business improvement technique.

Business processes are specified, modeled, and well-described for general understanding by employees.

Business process models are used for benchmarking as well as process improvements.

Business processes are thoroughly modeled for business critical functions.

Business process models conform to information standards for Business Process Modeling (BPM).


2-22




Information Technology Infrastructure - General

Information technology investments focus on large, capital-intensive systems and recurring maintenance costs to keep them functioning.

EAM and ERP systems have been installed, but their features and limitations drive business processes, procedures, and reports.

EAM and ERP systems have been installed, and systematic efforts have been made to capture data and performance information in them.

An integration strategy exists for decision support tools as well as for mobility and/or GIS systems, but the cost of integration causes this to occur on a case-by-case basis.

Software integration techniques based on services and information standards have been piloted.

Management recognizes the link between business process modeling and the information technology infrastructure.

Search techniques allow broad and effective access to information, not just data.

Ownership of asset management information technology is well established

An asset management information technology strategy exists and has been approved by management.

Information standards and a Service Oriented Architecture are used to facilitate IT support of EAM.

Business processes are intimately associated with the IT infrastructure.

Users can specify the asset management processes and techniques they want automated in their own terms, and the IT department can readily and accurately develop the corresponding services and integrate them into existing applications.

Management and asset managers can specify and IT can build visualizations of asset management results that enhance the use and usefulness of asset management decision support tools.


2-23




Information Technology Infrastructure - Knowledge Management

Staff skill and experience, despite being the basis for many decisions, is neither managed nor captured.

Staff skill and experience is sometimes captured in procedures or decision guides, but managing and maintaining the information occurs only as a result of individual initiative.

Knowledge management techniques and tools have been successfully piloted.

Aging workforce knowledge is being captured on a selective basis.

A plan is being actively implemented to capture aging workforce knowledge.

Captured aging workforce knowledge is employed in equipment performance data, decision support models, and business processes used in asset management.

Knowledge management techniques and tools are used systematically and in conjunction with decision-making and decision-support tools.

Staff skill and experience is captured as a matter of established process.

Business Analysis Role Business analysts remain largely within the confines of the finance department.

Business analysts audit various functions and are an active part of evaluating capital investments.

Business analysts work closely with line organizations to ensure quality and consistency in both capital and O&M budgeting.

Budget analysts are embedded in line organizations and viewed by employees as part of the implementation team.

Business analysis is consistently and routinely done by many employees.

Budgets are updated regularly and vary with conditions and events.

Management Support Management has little interest in asset management, preferring more traditional business practices.

Management is aware of asset management techniques in use by peer companies.

Management has an expectation that EAM and ERP and certain other IT investments will improve asset management.

Management follows through on EAM and ERP investments to ensure the associated business process improvements occur.

Executive management actively supports asset management.

An executive sponsor for asset management is responsible for improving and expediting its use.

Executive and line management has ensured that asset management is part of the corporate culture.

Asset management is seamlessly integrated into the appropriate business processes.

3-1

3 FIRST STEPS FOR NEWCOMERS TO ENTERPRISE ASSET MANAGEMENT

This chapter describes some of the critical issues that need to be addressed for Reacting and Awakening Asset Management programs. In the least mature asset management programs, collecting and using data is typically the single greatest constraint on asset managers. Asset management data feeds models, which in turn are employed in decision support tools. Sometimes the models are part of performance indicator programs; sometimes they stand alone in the decision support tools. The maturing of an asset management program requires growth to occur on all three topics in some organized and integrated manner.

One logical forum for integrating data, models, and decision support tools are the EAM and ERP systems, the backbone of the asset management information technology system. While these systems have their own objectives and needs in supporting a wide variety of core business processes, such as maintenance, design engineering, procurement, and others, they also are the repository of the asset inventory. The asset inventory is the data base that provides some of the key attributes of an asset, including its name, location, manufacturer, model, and size. Many data collection and use systems operate off the data structure of the asset inventory.

In this chapter, we will examine the following topics:

Developing a good asset inventory system, upon which an asset management program can be developed

Collecting asset equipment data (reliability and condition) and doing so in a manner than supports immediate use in asset management, as well as future use as the asset management program matures

Developing and employing performance indicators, including using models and integrating with data sources

The best way to start out with decision support tools

Beginning to exploit the EAM and ERP systems and their role in the enabling the above efforts

In this following discussion, we describe the most important aspects of these topics as they relate to information technology. References for further details in other EPRI reports are provided as well.

First Steps for Newcomers to Enterprise Asset Management

3-2

Developing a Good Asset Inventory

An asset inventory is essentially a list of assets owned by the company (in this context, we refer to the physical assets of the company, plant and equipment, rather than its financial assets). What would seem like an easy concept is made more difficult because: such lists often appear in multiple, different information technology systems, the recorded characteristics of assets vary according to different user needs, assets are comprised of other assets, e.g., subcomponents, and some assets change location at various times.

The first step in defining a good asset inventory is to identify the variety of databases and applications which have an asset inventory. The starting point is the EAM system. The EAM system will usually contain the largest list of assets, as well as the most general list of asset characteristics. But there can be many other applications which have inventories. At one end of the spectrum are spreadsheets used by engineers to track specific types of equipment, e.g., air-operated valves or large transformers. At the other end could be new large information technology systems such as GIS or mobility data systems. Any asset management decision support tools would also be of interest.

Assembling these sources will provide important insight into the desirable structure of the asset inventory, e.g., by exposing types of user needs and asset characteristics that the asset inventory needs to support. Also, it will provide a roadmap for configuration control of these databases. For example, a design parameter may need to be maintained for a particular piece of equipment. The parameter might currently be stored in a spreadsheet maintained by the corresponding equipment engineer. If the decision is made to maintain the spreadsheet separate from the asset inventory, then the requirement for synchronizing the two can be established in a documented business process.

Additional asset information that may be required by an asset management program include items such as:

ratings

size

principal material

test values

The amount and type of additional information will vary by asset type. For example, transformers, because of their value, importance, and the assessment tools that are used to measure their condition, will often have more characteristics than other assets. Some of this information may be stored outside the actual asset inventory and related by a key field such as asset type code.

It is important to note that not all assets may be currently in the asset registry. Typically “passive” equipment are the ones omitted. Examples include internal structures like fire barriers in generating plants or poles in the power delivery system. If important assets are not in the inventory, the effort to include these assets in the inventory is often substantial. Consideration should be given to incorporating (and possibly even labeling) such assets as part of other


3-3

business processes, e.g., inspections, installation, maintenance, etc. Assets not in the asset inventory may pose some problems for asset management programs as they mature. For example, certain asset management tools integrated with an EAM system may function better if the assets are in the inventory. However, the substantial effort in data collection and processing to include these assets in the EAM typically is not warranted for less mature asset management programs.

The above discussion points out that the true asset inventory may really be virtual and comprised of a set of databases. One typical setback that can occur in the least mature asset management programs is that management or IT sets an expectation that all information will be stored in the new EAM system. Typically one of two things happen: Either the asset inventory becomes large and unwieldy, or users such as equipment specialists must maintain separate systems. This setback can be avoided by developing a good understanding of all the asset inventories and the application needs that drive them and then making a conservative decision about what to include in the core asset inventory. As the asset management information technology system matures, decisions can continue to be made to consolidate and improve the asset inventory, thereby reducing the need for maintaining separate databases and increasing the accessibility of the information in those separate databases.

Perhaps the next most important aspect of an asset inventory is to establish a logical hierarchy that addresses the concept of assets that are composed of other assets. Important components like relays and check valves are often piece parts of larger components such as pumps, diesel generators, and transformers. Nevertheless, important business processes like maintenance, testing, and asset replacement may occur at the subcomponent level. Typically an EAM system will provide the required capability. The IT group, however, must ensure that other capabilities, such as data mining and performance monitoring, support asset managers and others who need to evaluate components that might appear at either level.

A logical hierarchy also includes the concepts of systems. The typical generating plant has its systems pretty well defined, and the power delivery system has its circuits. The concept of “systems thinking” is described in the next chapter. We mention systems here because it is another concept that requires a hierarchical asset inventory.

Another challenge to developing asset inventories results because the location of the asset can be important. First, it is important that assets can change locations. An asset such as a circuit breaker may be removed from service, refurbished, and then put back in service in a different location. If the performance history of the asset is lost because of inadequacies of the asset inventory, then asset managers may misinterpret the information they get when analyzing the component. Second, locations may influence the performance of an asset. In geographically distributed systems like power delivery systems, this concept is better understood and can supported by GIS capability. But even in generating facilities, hotter or more humid conditions can affect equipment life in ways the asset manager must be aware of.

Asset inventories typically contain text fields that can be important. One such field is the so-called “noun name.” The noun name may contain information like the type of component or its rating. Consequently, the more consistently text information is input, the more useful it will be when searches are performed to find groups of related equipment.


3-4

The same is true even of fields that are not text fields, of course. Data input from different organizations or different groups may use different abbreviations, different units, or different terms. While it is impractical to make databases perfectly consistent, it is appropriate to attempt to develop a process for upgrading data of this sort when it begins to negatively influence the capabilities of asset managers and other users. Typically, a data update process for the asset inventory will accompany the installation of an EAM system. Incorporating lessons learned into the EAM data update process is a logical way to mature asset management information technology at a reasonable and sustainable pace.

While adoption of information standards often comes later in a maturing asset management information technology program, information standards do provide immediate insight and value to the problem of creating an asset inventory. Information standards cannot function without a standardized approach to asset inventories. Hence, a standard like the Common Information Model [7] can be quite helpful to defining an asset inventory, even if the company is not ready to commit to the broader uses of information standards. Equally important, a use such as this provides a cost effective means of increased training and understanding of information standards.

In summary, the following steps or considerations should be given in developing a good asset inventory:

Identify the variety of databases and applications (including asset management decision support tools) that contain asset inventories.

Make an informed decision about how to consolidate this information into an asset inventory (even a virtual one). A central role for the asset inventory is the EAM system.

Document what assets are not in the inventory and identify any opportunities to include them as part of other planned or anticipated business processes, e.g., pole inspections.

Give careful consideration to a hierarchical structure that supports subcomponents and systems.

Look to information standards as a potential source for a good asset inventory data structure, regardless of whether information standards are being used in software integration.

Pay careful attention to how location information will be handled in the asset inventory and give consideration to future enhancements such as GIS capability.

Remember that information in general and text information in particular is more valuable when it is consistent among databases.

Establish a configuration control process for maintaining the asset inventory data in the EAM. Use the process for the virtual asset inventory and use lessons learned from application in the EAM to improve the process so that the consistency and quality of data improves as asset management matures in the company.


3-5

Collecting Asset Equipment Data

Much of the data collected today that asset managers need is difficult to use. Data is often collected in forms that are not electronic. And the data that is available electronically is often stored in text or narrative forms, which are difficult to evaluate. While some Enterprise Asset Management systems provide the means of collecting data more efficiently, the current implementations of these systems often suffer from lack of anticipation of asset managers’ needs. These needs include easy access to asset performance data, such as reliability estimates, return to service times, equipment health, and cost information. The result is that analyzing existing data to determine equipment failure rates and equipment condition can be a costly proposition.

Good asset equipment data starts with a good asset inventory. Equipment data must be grouped to provide truly meaningful information. Since equipment fails only occasionally, equipment failure information must be grouped by component type. Since equipment condition comes from a variety of sources, information from different applications must be able to be brought together by a common equipment name.

But ultimately it is the structuring of the data which allows the most information to be derived from failure and condition information. The data structure needs to include the subcomponents or piece parts of the asset and also the causes of equipment failure and degradation.

Unfortunately, there are no consensus information standards upon which to turn. Standards such as the CIM have barely touched this topic or have not involved equipment failure specialists. Industry database structures that do exist vary, even within disciplines of the same electric utility sector, e.g., maintenance and risk analysis, and even more so between sectors, e.g., power delivery and generation. For the most part, such data structures remain the domain of a variety of consultants.

The consequences to asset management are twofold. Within a particular sector, it is difficult to compare collected data to industry-wide or generic data. For the enterprise asset management program, it is difficult to share data between business units. Fossil generation data typically differs from nuclear generation data and both differ from power delivery system data, even when the equipment is the same. Disparities in data narrow the scope of asset replacement and maintenance improvement studies to individual business units as well as hindering opportunities to collectively improve industry performance. The disparities reduce the opportunities for enterprise wide asset management actions.

Despite these problems, asset management programs have much to gain from good structuring of equipment failure and condition data. Even early in the maturity of an asset management program, a well-structured database can support the development of strong and stable condition-based maintenance programs. As asset management matures further, more selective asset replacement strategies become possible. Moving early on data structures is sufficiently straightforward that it can be done in anticipation of asset management program growth. Doing so will also speed the maturation process in this and other areas such as decision support models and information standards.


3-6

The following describes a technique developed by EPRI for structuring equipment failure and condition data. This technique has been proven in its ability to support condition-based maintenance programs ranging from simple to sophisticated. The technique has also been validated against generic industry failure rates, demonstrating its consistency with failure data collection methods. Finally, the technique has been shown to be amenable to growing with the sophistication of asset management decision support systems. In Chapter 5, this report briefly discusses a more complex implementation of the program that supports integrating condition data with degradation models. Such an approach is within the reach of an asset management program at the Processing or Continuously Improving level of maturity.

The Preventive Maintenance Basis Database (PMBD) has been in ongoing development in EPRI’s Nuclear Power Sector since 1996 and has become a widely-used tool and information resource in the nuclear power industry. Over time, small groups of subject matter experts drawn from operating nuclear power plants and vendors have developed preventive maintenance (PM) tasks and task intervals. The PMBD was created as an information resource to supply recommendations on PM based on this disparate set of knowledge and experience.

Recently, these subject matter experts have included representatives of power delivery and fossil generation business sectors. The “nuclear only” database has recently expanded rapidly from approximately 80 major components to an “enterprise” database of more than 120 components. Additional components continue to be added to the database.

For each major component type, the PMBD provides a detailed description of failure mechanisms, a recommended program of PM tasks, a synopsis of the task content and intervals, and the reasons why these choices are technically valid in a variety of circumstances. Various industry bodies have conducted ongoing reviews and updates of the data in the PMBD. As a result, the PMBD is now approaching the status of a consensus repository of industry PM experience and expert judgment.

Over the same period, the PMBD’s capabilities as an analytic decision tool have been greatly enhanced. These improvements have focused continually on answering the question “What is the quantitative effect of PM on equipment failure rates?” No other existing maintenance or reliability application provides such an answer to this question.

In the PM Basis Database data structure, each asset type, e.g., a substation transformer, is subdivided into a list of Failure Locations (e.g., core, desiccant, electrical connections, fans, etc.), which are basically its subcomponent parts (see Figure 3-1). Each of these is assigned Degradation Mechanisms (e.g., loose condition, loss of core ground, multiple core grounds, etc.). The latter are the processes by which the subcomponents degrade with use and the passage of time. Each degradation mechanism is further described by one or more Degradation Influences (e.g., assembly or shipping error, vibration, over-excitation or arcing, etc.). These are the stressors that initiate the degradation process and affect how rapidly degradation develops.

Figure 3-1 also lists corresponding Discovery Methods for each degradation influence. These methods are ways to detect the degradation (e.g., dissolved gas analysis, vibration analysis, core ground testing, thermography, etc.).


3-7

Asset ModelsTransformer - Substation –No LTC

Quantify failure rate

How to detect degradation

What can fail?

How can it fail?

What determines rate?

Figure 3-1 EPRI PM Basis Database: Equipment Model Example

The terms failure location, degradation mechanism, and degradation influence are approximately equivalent to similar terms in typical industry reliability data bases, e.g., subcomponent, proximate cause, and root cause, respectively. A unique combination of values for these data fields forms one row or record in the component data table. This unique combination can be referred to informally as the failure cause or failure mechanism. All other information specific to that unique combination (e.g., discovery methods) are contained in the same row of the table.

Note that a failure location may have multiple degradation mechanisms. For example, the core may suffer from a loose condition, loss of core ground, multiple core grounds, etc. Similarly, a degradation mechanism may have multiple degradation influences. For example, the loose core condition may be influenced by an assembly or shipping error or by vibration.

These failure mechanisms may not always represent complete failure of the component functions. They more accurately correspond to the occurrence of clearly degraded states to which a maintenance professional would not wish to subject critical equipment.

The data structure shown in Figure 3-1 lends itself equally well to the description of the full range of assets from complex active components to passive components. Proof of this assertion lies in the fact that the same approach has already been used with equal success for a broad range of components, including heat exchangers, feedwater heaters, DC power supplies, battery charger and inverters, 18 types of wooden utility poles, and ten types of underground power cables.


3-8

The PMBD is based on a modeling approach that is initially driven by the input of expert knowledge, but it is then amenable to refinement based on the input of event data collected using its data structure. Because the PMBD is fundamentally a degradation modeling approach, it allows asset managers to look forward – evaluating possible future scenarios in an effort to recommend the most reliable and economic course of action. Its initial reliance on the knowledge of maintenance, operations, and engineering personnel draws on the expertise and career-long experience of these power industry professionals. Such reliance brings the greatest possible experience and equipment understanding to the data structure as well as a consensus upon which information standards are traditionally based.

The PMBD data structures and degradation models can be used in conjunction with the other sources of data both inside and outside the company:

Generic failure rate data from various published sources.

Industry-wide databases of individual events collected by participating utilities using some level of standard data structures.

Records and estimates of planned and forced outages and related reliability measures.

Individual equipment studies which might report numbers and types of failures as well as recommendations on maintenance programs and asset replacement strategies.

More information and further references on this approach can be found in:

Asset Performance Database, A Recommended Approach for Data Modeling to Facilitate Power Delivery Asset Management, 1008553, EPRI, Palo Alto, CA: 2005

Information on how equipment fails is often rudimentary at best. The legendary “broke, fixed” entry is all too common in a maintenance record. One benefit of early implementation of all or part of the equipment model is to build a process in which failure and condition information improve in quality as the asset management information technology capabilities mature.

Return-to-service times may be important for some assets. Often return-to-service times include a variety of different contributors, other than equipment repair, including for example time to reach the asset, isolation/tag-out times, times for obtaining parts, and time for returning the system to service. Since these contributions can vary widely for the same asset types, it is often important that return-to-service times be specified clearly.

At the Awakening level of maturity, there will still be risk in many information technology investments. The risk stems not from technology, but from the organization failing in its efforts to adopt the technology. But improving maintenance programs with condition-based approaches often yield substantial benefits, and improving data collection lays the foundation for many benefits from asset management. Because the application of equipment modeling, discussed above, begins as a maintenance improvement, there is less organizational risk than trying to drive a similar approach from asset management into the rest of the organization. With lower organizational risk and high benefit, this approach to structuring asset failure and condition data is more likely to yield benefits in the early stages of an asset management program.


3-9

To conclude, the following steps should be considered when collecting asset equipment data:

Start by developing a good asset inventory.

When collecting equipment failure and equipment condition information, select a data structure that has room to grow with the asset management program.

Give consideration to, but avoid being driven by, the structure of industry-wide databases. Monitor information standards developments in the areas of failure locations (subcomponents) and failure mechanisms.

Incorporate the equipment model data structure in the EAM, even if it is done one level at a time.

Work to improve the information collected in the EAM, including in particular information related to the cause and condition of equipment failures and to the various contributions to return to service times.

Developing and Employing Analytical Models

The discussion above highlighted the importance of data in developing the foundation for a maturing asset management information technology program. In many ways, data structures are some of the most straightforward aspects of information technology, because information technology providers and users understand their importance. The same cannot be said for analytical models; such models are inherent to many of the decision processes of asset management, yet they are often an afterthought in the IT implementation process.

The previous discussion is a case in point. If data is collected consistent with a model for equipment degradation, the information in that model can be more quickly evaluated. Many asset management information technology systems support evaluations or decisions for components (e.g. power transformers), but it is the actual subcomponent (e.g. the transformer windings) that most often determines the maintenance procedure executed. Furthermore, it is the degradation mechanism that helps engineering determine if the maintenance program is adequate and the design is appropriate.

Initially, asset management systems too often short shrift models. The resulting asset management decision process becomes more ad hoc, and its success more dependent on staff skill and experience. This problem is often most evident in performance monitoring, where performance criteria might be selected based on what is already measured by regulators or by what other companies measure.

The following discusses the use of analytical models in performance monitoring, but the basic concept applies to the full range of decision support tools available to the asset manager. This report focuses this discussion on performance monitoring because success in that discipline is a key indicator of success in asset management and because successful performance monitoring requires a good information technology infrastructure.

As utilities focus more tightly on improving performance while holding the line on costs, establishing linkages between corporate objectives and key indicators of performance has


3-10

become increasingly important1. The stakeholders’ goals of an electric utility are diverse, often requiring trade-offs to effect a good asset management program. Increasingly corporations are developing models of stakeholder values and using them to prioritize investments.

Similarly, corporations are looking to metrics and key performance indicators to drive performance improvements throughout their organizations. The widely used Balanced Scorecard approach to performance monitoring has led to many business process improvements. But the method itself does not inherently guarantee good performance indicators. Studies of the usefulness of performance indicators have shown significant differences in business improvement among companies that based their performance indicators on formal models of value.

However, prioritizing investments and monitoring performance are often not implemented together or consistently. The effectiveness of asset management programs suffer when the model for determining investment strategy differs from the model for measuring performance. Perhaps more importantly, many corporate value models are not validated, nor do they connect objectives with decisions the company can make. For these reasons, EPRI has embarked on research to use value modeling techniques in the development of performance indicators. Work to date indicates that the corporate value model developed as part of an asset management program can be aligned with performance indicators. The result of such an alignment is an organizational “line of sight” from the stakeholders and the corporate officers who lay out goals and objectives to the supervisors and personnel who design, maintain, and operate the assets.

The purpose of a corporate value model is to quantify value, so that the value of various activities, such as capital investments, maintenance programs, and the like, can be measured and compared. Some kinds of value are readily measurable; for instance, the value of a kWh of electric production is simply its price. Other kinds of value are more elusive; for example, what is the value of “reliable electric service?” Nevertheless, it is a fundamental principle of economics that value can be assigned to any tangible good or service.

In general, three difficulties arise in trying to assign value: precision, preference, and consensus. Precision means that one must describe precisely what one is trying to value. For example, what does “reliable electric service” mean precisely? Answering this question leads naturally to a breakdown of this attribute into various subattributes, such as “duration of outages” and “frequency of outages” and also to specifying the means of measuring them. Preference means that one must be able to distinguish different levels of value for a particular attribute. For instance, clearly fewer outages are preferred to more outages, but how much more valuable are, say, two outages per year versus three? Consensus means that among any group of decision-makers, preferences are likely to vary in ways that usually cannot be resolved objectively; thus, to have a credible tool to guide decision-making, the relevant people must reach a consensus about the value model. These difficulties can be overcome using a systematic process for defining a value model.

1 See Value Modeling and Measuring Key Performance Indices for Power Delivery. EPRI, Palo Alto, CA: 2007. 1012502.


3-11

The following set of characteristics represents objectives in the design of a corporate value model.2

Level playing field. The value model should allow fair evaluation of all activities. The only reason to undertake an activity is its contribution to overall corporate objectives. No other characteristics should influence the choice of whether or not to do it. All activities are evaluated on the same basis, and the relevant people should be able to agree on the measurement of value provided by the activity.

Resolve differences of opinion rationally. The value model should provide a system for resolving differences of opinion as well as determining which differences matter. The value analysis of all activities should focus on the attributes that provide value, the corporate objectives, and the structure of the portfolio of activities.

Defensible logic for peer review. The value model should make it possible to explain in detail why a particular activity or portfolio of activities is undertaken. Reviews, like differences of opinion, should be based on attributes, objectives, and portfolio structure.

Transparent analysis. Not only should the specification of all activities be clear, but the evaluation criteria should also be readily apparent. It should be possible to explain why an activity has been undertaken. Further, it should also be possible to observe how changing the specification of an activity results in different evaluation. The value model should attempt to eliminate all ambiguity regarding decisions whether or not to undertake activities.

Completeness with respect to performance measures. The value model should encompass multiple performance measures for multiple objectives. It should be possible to compare value with respect to different or competing objectives.

Bias- and error-free. The value model should minimize the effect of individual biases and to eliminate, as far as possible, any cognitive errors. It is difficult to eliminate deliberate misreporting or misassessments, but the transparency of the data and the analysis should tend to prevent such deliberate misstatements from going undetected.

Practically applicable with respect to cost and time. If development or use of the value model is cumbersome and time-consuming, it will not be used. The development process usually requires significant efforts, but subsequent analyses should be relatively simple and timely. For example, having too many attributes or not having readily available data to describe an attribute will prevent simple and timely application.

Compatible with existing business practices. The value model should support existing processes for capital and maintenance prioritization and performance monitoring.

2 Adapted from Project Prioritization System: Methodology Summary, EPRI, Palo Alto, CA: 2001. 1001877


3-12

In general, a value model consists of three major components:

A set of attributes of value representing the potential ways activities can contribute to corporate value.

A set of scales to measure the value of each of the attributes.

A set of weights that enable one to compare and trade-off value among the various attributes.

The foundation of value modeling is a set of attributes. Attributes of value generally fall into three categories:

Financial attributes, such as revenue, earnings, share price, etc.

Quantitative, non-financial attributes, such as reliability

Qualitative attributes, such as corporate reputation.

Since high-level objectives, such as reliable electric service, are usually not defined precisely enough to measure, the attributes usually must be refined by defining component subattributes. Thus, the attributes of value form a hierarchy, with the high-level corporate goals at the top. Successive levels in the hierarchy represent increasing specificity, until, at the bottom level, the attributes are readily observable and fundamentally measurable. The hierarchy defines each component of value that an activity may contribute and establishes the relationship between that value attribute and the overarching corporate goals. Development of the hierarchy requires definition of each value attribute, including as necessary, additional levels of sub-attribute definition to adequately capture all unique sources of value.

Usually, such attributes have readily observable natural units, but such units do not necessarily represent value, so a means of translating the natural units into units of value must be devised. The means of translation is the scale.

How does one create such a scale? Generally, a scale is developed through a group process relying on the judgment of the individuals involved. A series of comparisons are posed of the form “Is it more valuable to reduce outage time from a to b or from b to 0?” or “At what point x between a and 0 would it be equally valuable to go to either from a to x or from x to 0?” By successive refinements, the group can fill out the entire scale.

The process does not have to rely entirely on judgment, although that is a perfectly valid way to establish preferences. For instance, in establishing a scale for customer outage minutes, many utilities have customer satisfaction data from surveys that can estimate customers’ value of reliability.3

3 See. Customer Needs for Electric Power Reliability and Power Quality: EPRI White Paper. EPRI, Palo Alto, CA: 2000. 1000428


3-13

Developing scales for qualitative attributes presents further challenges, since a qualitative attribute has no “natural units.” In this case, a scale has to be described by descriptive statements indicating the various levels of value, and there is no direct way to determine the value of changing levels. Nevertheless, it is possible to assign value to changes in a qualitative scale. These attributes use “anchored scales,” which are numerical scales with verbal "anchor points" at various values. “Anchored” means the qualitative rating statements for any given factor are sufficiently well-defined that, given the same input data, different raters would usually select the same rating. Rating statements are associated with numerical values between 0 and 10, producing the desired quantitative value.

While scales measure the value of changing an individual attribute, they say nothing about the relative value among different attributes. To compare value between two activities that affect multiple attributes, it is necessary to have a way to compare value among the attributes. For instance, one might ask “How much is moving electric service reliability from 14.4 million customer outage minutes to 0 worth compared with addressing a safety issue that significantly contributes to resolution of multiple regulatory issues?”

The weighting process starts at the lowest levels of the attribute tree. At each step of the process, people are asked to compare the relative importance of changing two or more attributes. When all the subattributes that roll up to a particular attribute have been weighted, the process moves to the next set of attributes, and when all of the attributes at a particular level of the tree have been weighted the process moves up to next level. The trick to eliciting accurate weights is to structure a process that enables people to compare a small number of attributes at a time and to always look at specific examples of the value impacts to anchor the comparisons in concrete terms.

While the above discussion often describes how expert opinion and judgment can be used to create value models, the same process can be applied to more quantitative sources of information and to existing models. The EPRI references provide further examples of incorporating quantitative data and models.

Throughout this information technology guide, we place heavy emphasis on consistency of data structures and models used in the asset management process. While not in itself an information technology attribute, consistency in these areas allows information technology to function more effectively and at lower cost. For example, consistency facilitates integration because information in one application can be readily compared and interchanged with information in another application.

One important place for consistency is the equipment model mentioned in the previous subsection. The equipment model, generated by experts and then validated against experience, can and should be used in performance monitoring and project prioritization.

In a project prioritization application, the corporate value model described here would help to identify the value of a particular asset or set of assets. The equipment model would help to identify the value that a particular action would create in terms of improving the reliability and maintainability of the equipment. Such a model is invaluable to making decisions regarding whether to change maintenance practices or change equipment designs.


3-14

In a performance monitoring application, the corporate value model would help to identify the most important assets to monitor. The equipment model would identify the most important degradation mechanisms to monitor as well as identify the maintenance activities that provide information about degradation or renew subcomponents and reduce degradation.

By these two illustrations, this report describes how data, models, decision support tools for project prioritization and critical asset management technology like performance monitoring can all integrate to produce a self-consistent asset management program operating with a consistent and straightforward information technology infrastructure.

But knowing that a linkage exists between the asset inventory, the equipment data model, and the corporate value model is insufficient. Developing and maintaining the links and applying the model consistently takes planning, organizational effort, and persistence by management and asset managers to ensure that these models are applied. When the models are found to be deficient, they must be improved. When new decision support tools are acquired or used, their models should be derived from the corporate value model. When business conditions or stakeholder values change, the model may need to be restructured at the top.

While this might seem like a daunting task, in reality all those things and the accompanying work happens now. It just happens in different ways in different business units and at different levels in the company. The decision making more often than not happens in an ad hoc manner which in turn results in management energy to prevent inconsistent investment strategies at the business unit level.

An additional benefit of this approach is that it is much easier to define requirements for the asset management information technology infrastructure. Applications such as EAM, data mining, business intelligence, strategic planning, and performance monitoring should all be evaluated to ensure that they can support the corporate value model and equipment model described here.

Finally, it is beneficial to have a short note on knowledge management. The models we have just described are accompanied by a tremendous amount of implicit knowledge. The more the knowledge that forms the basis for the model can be captured, the easier maintaining and enhancing the model will be. The importance of knowledge management is discussed further in Chapter 5.

The approach proposed in this subsection is not without its challenges, but it does pave the way for an Awakening asset management company to mature. To conclude, the following steps should be considered when developing and employing analytical models:

Identify the various models that exist explicitly and implicitly in performance indicators and project prioritization. Also identify models in other asset management decision support processes.

Evaluate the identified models according to the above guidance, examining the overall desirable characteristics of models and the specific aspects of attributes, scales, and weights. Consult EPRI references for further details.


3-15

Merge the models into a corporate value model. Let corporate and stakeholder goals drive the upper portions of the model hierarchy. Use the guidelines above and in EPRI references to ensure the model has the characteristics that will support asset management decision support tools in a comprehensive and consistent manner.

Understand that the asset equipment data structure for collecting data is also an equipment model that can be linked at the asset level to the corporate value model.

Lay out an overall plan for developing, maintaining, and using corporate value models and individual asset models in asset management decision support tools.

As information technology is acquired, implemented, and enhanced, ensure that the information technology can readily support incorporation of corporate value and individual asset models.

As knowledge management techniques and information technology is adopted, ensure that the basis for the corporate value model and individual asset models are captured in as robust a manner as practical (see also Chapter 5).

Employing Decision Support Tools

The previous subsections described how to set up a data and model infrastructure that enables asset management information technology in both the immediate- and long-term and that supports the application of decision support tools. In the Awakening phase of program development, decision support tools are still in their infancy. But decision support tools become much more important in the Organizing phase. The logical question is, how does this transition occur?

Initially decision support tools are applied by individual innovators. Often they might be applied to a critical problem for which traditional techniques have not been as effective as desired. Nevertheless, because the tools are often very different than what has been used in the past, the asset manager should not underestimate the difficulties in successful use.

Access to the data that is needed will often be a struggle. If the abovementioned data structures are being implemented in the EAM system, the process will be easier, but it is still likely that most information will be in a legacy system, a paper or microfiche file, or someone’s head. Persistence and hard work will usually overcome these problems while an improved data system is being developed. Lessons learned should be captured and shared with management.

But the real challenges lie elsewhere. The asset manager (and innovator) must focus on how to communicate to management and peers, including in particular, those that are interested but skeptical. The asset manager must be able to describe:

Describe where the information came from and why the input data is sufficiently accurate and pertinent for the problem at hand

Describe the solution in terms compatible with the current approach to the problem

Describe what is the basis behind the decision support tools


3-16

Describe why the proposed solution makes sense

Show that the solution can be practically implemented

The question about input data will be largely related to the “garbage in, garbage out” concern. Because the decision support tool will largely be seen as a black box, suspicion in the data will translate into suspicion in the result.

Probably the most important communication issue is to describe the problem in understandable terms. Typically asset management decision support tools provide lots of numbers. While numbers might be important, it is much better to describe the results in words. For example, an asset replacement strategy might be to replace an underground cable after twenty years. The decision support tool result should be described in a similar manner, e.g., to replace an underground cable after two failures or thirty years whichever occurs first. The numerical basis and value of the change can be explained subsequently. If the initial statement is not intuitive, no amount of numbers will give management confidence.

The basis behind the decision support tools should be explained. If it is an economic calculation, it should be explained simply. If it is an engineering tool, what design margins or design weaknesses the tool assesses should be described.

Why the proposed solution makes sense should naturally follow from the simple description of the result and the simple description of what the decision support tool evaluates. If it is not, the result should probably have been thoroughly checked already.

Finally, a solution which cannot be practically implemented is not very useful. Management of a Reacting or an Awakening company will probably be skeptical of change and will probably have experience in which change will have failed in implementation. Having prior discussion with those in the company who will have to implement the change and showing how the change will be done will go a long way in encouraging management to take the risk associated with a new approach.

The above discussion has little to do with information technology. Yet, gaining management support for asset management has everything to do with gaining the organizational and financial support to implement the information technology for asset management. A decision support tool that does not make sense on its own will never be integrated into the information technology infrastructure.

To conclude, the following steps should be considered when employing decision support tools:

Focus on explaining the results of asset management within the logical framework that decisions are currently made in the company. If that includes traditional rules of thumb, explain the change suggested by asset management in those terms.

Present numerical results, but primarily as justification for what should appear to be natural conclusions.

Be prepared to address the garbage in garbage out concern about input data.


3-17

Ensure that the suggested changes are not too complex to practically implement.

Collect and document lessons learned from the process of communicating results from these early uses of asset management decision support tools. Factor the lessons learned into future applications.

4-1

4 BREAKING OUT TOWARD A MATURE ENTERPRISE ASSET MANAGEMENT PROGRAM

This chapter describes some of the critical issues that need to be addressed for asset management programs in the Organizing stage. The Organizing program is mature and stable, and it will be showing significant benefits to the company for the effort and resources invested.

In the least mature asset management programs, collecting and using data is typically the single greatest constraint on asset managers. Asset management data feeds models which in turn are employed in decision support tools. Sometimes the models are part of performance indicator programs, and sometimes they stand alone in the decision support tools. The maturing of an asset management program requires growth to occur on all three topics in some organized and integrated manner.

The company whose asset management program falls in the Organizing category has made and is continuing to make significant improvements. One of the most important areas of ongoing improvement is in the increasing sophistication of decision support tools and their use. As in the last chapter, the emphasis is not only on the tool itself, but how the tool is effectively used and how the tool contributes to the decision making process. Because project prioritization and investment tools are often a keystone of the development of an asset management program, this chapter discusses them in further detail.

Another important attribute of how the Organizing company is improving in the use of asset management is what this report calls “systems-level thinking.” While treatment of individual assets is important, often it is a collection of assets that must be acted upon. Systems-level thinking looks at groups of assets and balances improving single assets against the operation of the whole. Replacing one part of a system and causing a planned outage is an opportunity to replace other parts of the system without the costs of an additional planned outage.

Improving the capability of a part of the system with new technology may not have significant benefit if other weak points of the system control the system’s overall functionality. On the other hand, adding new technology to a power delivery system may add IED (Intelligent Electronic Devices) that can be used to monitor older, un-instrumented portions of the circuit. The IEDs might in turn enable a wide range of new asset management techniques, improving what would otherwise have to be a run to failure strategy.

Breaking Out Toward a Mature Enterprise Asset Management Program

4-2

An Organizing company is also beginning to overcome the inertia of installing large information technology systems and is making better use of not only EAM and ERP systems but also of other important systems such as Graphical Information Systems (GIS), mobility systems, and Business Information systems (BI). Implicit in this infrastructure of systems is an advanced data mining capability which might be part of one of those systems, part of a real-time data system that also supports operations, or a completely stand-alone system.

In the previous Chapter, the report described how to establish an asset inventory and data structure that lays the foundation for continued growth in an asset management information technology infrastructure. The Organizing company moves further to systematically improve data quality and data integration. The latter could include the selective or comprehensive adoption of information standards.

Finally the Organizing company should be taking steps to enable a smooth transition to higher levels of asset management. In this regard, knowledge management tools are probably being piloted. Business process modeling and documentation as well as benchmarking is also probably growing in importance. While these topics are key subjects in Chapter 6, we note that the initial steps are natural extensions of the topics we discuss in this chapter. Specifically, improving data quality through audits and feedback is the same type of process that could be applied to a variety of business processes and in the Organizing company, it probably is being applied.

Increasing Sophistication of Decision Support Tools

As decision support tools become more sophisticated, the resources needed to apply them increase as well. Sometimes this is due to increasing need for data, either greater detail or greater breath. With good data mining capabilities (described in the next subsection) and good data (described in the previous chapter), these problems are often time consuming, but are otherwise straightforward to solve. A good relationship between the IT group and the asset manager is a critical enabling step. Good management support facilitates such a relationship.

Ranking and Screening

However, the more difficult stride for the Organizing company to undertake are the concepts of screening and ranking. Screening is the process by which an asset management decision is analyzed at a level of detail commensurate with the result and its importance. Ranking is a process by which all actions are ordered in priority.

Ranking

Ranking is a process that has widespread benefit to asset management processes, but nevertheless falls well short of financial analysis of all actions. The concept of ranking is not to address the investment value of every action, but instead to rank the actions from highest to lowest value. It is then assumed that limitations in budgets will eliminate the lowest value actions and thereby prevent bad investments from being made.


4-3

Ranking is a natural decision support method for an Organizing company. Ranking is often heavily dependent on data and in certain applications requires automated data access. An Organizing company has typically progressed in its ability to provide accessible data of reasonable quality. For example, being able to mine data from the EAM system and to automate simple calculations is typically sufficient to develop a system to rank maintenance activities. Because maintenance activities are typically the ones under the most financial pressure in a company, a ranking process makes good economic sense for a company maturing in asset management.

Ranking also has the advantage of easier organizational acceptance than more explicit financial analysis. Once members of the organization accept the ranking criteria as appropriate, the process and results become easier to accept. The ranking criteria can incorporate traditional decision criteria as well as criteria more consistent with asset management principals. Using the maintenance as an example, a traditional criteria might be the factor by which the PM activity has been delayed beyond its traditionally scheduled date. The asset management criteria might be a measure of the value of asset that the PM will be performed upon.

More sophisticated ranking processes might present the values of multiple criteria as well as the value of some combined criteria. The company would then impose a decision logic that would determine the final decision as to whether (in this example) to perform maintenance or not.

Employing ranking processes, particularly for maintenance, is recommended. Many vendors and consultants offer them, and many utilities have used them. Consequently, the Organizing company can implement the process, benefit from it, and also encourage organizational learning from the those who perform work, to those who plan work, to those who approve and budget the work.

The information technology requirements for ranking are not onerous, and the lessons learned from automating a ranking process will be beneficial to future asset management information technology infrastructure investments. In particular, ranking maintenance activities requires that data be mined from EAM and other systems and that the resulting actions be implemented within the EAM system. The latter process is not trivial, but it is crucial to the maturation of an asset management program. If information technology struggles in the support of acting on asset management recommendations, asset management programs will not mature further and may even fall back.

However, for a company to mature beyond the Organizing stage, it is important to remember that ranking processes are limited by their very nature. As mentioned above, a ranking process tends to presume a certain budget level. Consequently, it avoids the concept of what budget makes sense. Ranking systems which incorporate traditional decision processes often compromise the effectiveness of the system. It is the weakness of traditional maintenance scheduling (e.g., time-based rather than equipment-condition-based) that causes the stress on maintenance budgets and allows imprecise methods to be used without much risk of degrading performance. Finally, the ranking process is often not organizationally diverse. Ranking is useful for the maintenance department, but it keeps separate and un-integrated the decision of whether new technology or equipment replacement would help solve the maintenance resource problem ranking is intended to address.


4-4

To conclude, the following steps should be considered when implementing ranking techniques:

Look for an opportunity to apply ranking techniques to maintenance planning or other business processes in which resources are tight.

Understand the basic limitations of ranking and of the qualitative tools that the process often depends on.

Find a co-owner in the business process of interest and work together to find domain experts who can help define good ranking criteria that will gain employee acceptance and yet have the fewest limitations.

Avoid using ranking criteria that are data intensive or beyond the capabilities of readily available information technology capabilities. Get IT involved early.

Implement the ranking technology with a continuous improvement philosophy. Collect lessons learned and success stories and communicate them to business process owners and management.

Screening

Somewhat related to ranking is the concept of screening. An Organizing company recognizes that a detailed analysis of all asset management decisions is counterproductive. In the ranking process, this issue is addressed by holding all analyses at a lower level. In the screening process, the level of analysis varies.

EPRI research has found that the asset management process can be applied with three levels of increasing complexity, thereby conserving analysis resources. The following discussion illustrates such a screening process with an example for project prioritization. Project prioritization is one of the first decision support activities to be developed in a maturing asset management program. It is also one that can involve a lot of analysis resources.

Many potential investments are not effective and can be quickly analyzed and eliminated. Basically, the three level screening approach uses the same overall analysis thought process in each level. The principal changes in analysis are the increasing degrees of sophistication for inputs and/or for the calculation approach. Using a three level screening process, a project that “fails” in either of the first two levels is screened out and the proposed action is not taken or not funded. Projects that “pass” warrant the most detailed analysis, a level which in this example addresses uncertainty explicitly.

This linear screening process can also be made slightly more sophisticated. First, if the second level of analysis shows that the project is a clear winner regardless of uncertainty, no further analysis may be needed and the third level of analysis can be avoided. Instead of avoiding uncertainty analysis completely, its analysis might be simpler and focused instead on how to reduce it or exploit its option value. Another process improvement is to send a screened project back to its sponsors for improvement. The screening analysis will help expose a project’s faults, and the sponsors might develop alternatives that are valid investments.


4-5

Now we look at the three levels of analysis in further detail (see Table 4-1). The level 1 analysis bounds benefits and operating costs, determining whether a proposed change is profitable under the most optimistic conditions. The method/tool determines the most optimistic effect of the proposed investment on reducing the present value of operating costs and increasing the present value of revenues. Optimism in performance might be reflected by assuming an asset’s reliability can be improved to perfection. The resulting benefit is then divided by an optimistically low estimate of project cost (i.e., the investment). The project is screened out (or revamped) if this benefit-to-cost ratio (also called “Benefit Over Investment” or BOI) is not significantly greater than a certain “hurdle” value. If the project is not screened out by this criterion, a level 2 analysis is performed.

Table 4-1 Comparative Characteristics of Asset Management Analysis Levels 1, 2, and 3

Level 1 Level 2 Level 3

Main feature “An optimistic assessment to rapidly reject clear losers”

“A realistic point value assessment to reject clear losers and accept clear winners”

“An uncertainty analysis to quantify up- and down-side risks for decision makers”

Inputs

Optimistic Point Values Realistic Point Values

Expert judgment probability distributions for parameters that drive the decision

Models Simple More sophisticated Most sophisticated

Main Outputs

Optimistic Point Value

Best-estimate Point Value

(sensitivity studies can provide point estimates of high/low what-ifs)

Risk profiles

= uncertainty distributions and risk measures (such as confidence intervals, probability of regret)

Decision

Reject if BOI<1

Results can help revamp proposed project

Accept project if very beneficial (e.g. BOI>2)

Reject project if very bad (e.g. BOI<0.8)

Otherwise, perform level 3 analysis


Decision based on quantitative estimates of risk



4-6

Level 2 analysis employs “best estimate” assumptions of expected improvement effects for the investment. This provides a “realistic” forecast of profitability, operating cost, and project cost, recognizing that either an optimistic or pessimistic forecast can lead to a wrong decision. If the point-value benefit-to-cost ratio is significantly less than one, the project is rejected (or revamped). If the benefit-to-cost ratio is significantly greater than one, the project is selected. In this case, further probabilistic analysis in level 3 would not be warranted unless a portfolio evaluation considering uncertainty and optionality is desired. A level 3 analysis should be performed if the level 2 point value analysis predicted a benefit-to-cost ratio near or somewhat greater than one. An uncertainty analysis would alert the decision maker to the risk of a failed project and the potential sources of that risk.

A level 3 analysis is a detailed analysis of distributions of benefit, costs, and profitability and assists in facilitating risk management. The first step in a level 3 uncertainty analysis might be to perform a sensitivity analysis using the level 2 model to examine the effect on project profitability of varying one parameter at a time to its high and low values (or by plus and minus ten percent of its best estimate). Results may be displayed in a conventional “tornado diagram”. The two or more parameters that produce the widest range of variation in profitability (NPV change or benefit-to-investment ratio) are identified as “decision drivers.”

In the level 3 analysis, these drivers would then be treated as uncertain parameters with estimated probability distributions used as inputs to a random sampling stochastic analysis of profitability, (Non-driver parameters would be kept at their best-estimate values.) Examination of the resulting probability or cumulative probability distributions of profitability allows the owner/investor to select projects that either minimize the risk of a loss or maximize the chances of high returns. In a portfolio analysis, the distribution of profitability results allows an efficient frontier to be used in selecting projects for investment (See Chapter 5 discussion of risk management tools).

This three-level approach ensures that the cost of more sophisticated and accurate risk-informed project evaluation is incurred only when there is a benefit in performing the asset management analysis. The factor that often determines the level of overall sophistication that is cost effective is project size, which can be characterized by investment cost. In general, only large projects that cost at least millions of dollars warrant a probabilistic level 3 analysis. For intermediate-sized projects costing tens or hundreds of thousand dollars, a level 2 point-value analysis will likely suffice. It is reasonable to assume that only large and intermediate-sized projects would be evaluated with the more sophisticated decision support tools. Table 4-1 summarizes the characteristics of the three analysis levels.

Another important factor in deciding the level of analyses to be employed in asset management decisions is the maturity of the asset management program. This report anticipates that only companies with maturity levels of Organizing or greater would employ risk analysis of this type. A significant education process may be required for management and business analysis staff to facilitate meaningful interpretation of the results. However, as more and more managers are trained in the principals and details of financial analysis techniques, there will be more and more receptivity to the use of similar techniques for investments in the electric utility infrastructure. At the Organizing level, it may be appropriate to keep risk analyses at a more qualitative level, focusing on risk drivers and mitigating actions.


4-7

The challenge imposed by screening for asset management information technology is not trivial. Different levels of analysis produce results with different levels of quality that cannot be directly compared without qualification. If a screened project is returned to its sponsor, the action expected must be clear. If the sponsor submits an alternative, a process may need to be maintained to facilitate reanalysis. Changes in assumptions are often critical in an iterative process, and these can be important and yet easily lost. We are aware of no easy solution to the process, and to date it has been managed manually. A higher level asset management information technology infrastructure with a Service Oriented Architecture and Workflow and Knowledge Management capabilities could conceivably address this process. For an Organizing company, it is important that the process be well documented. As the number of projects analyzed increases, IT and asset management can look to possible solutions for the most analyst intensive portions of the work.

To conclude, the following steps should be considered when implementing screening:

Understand that screening is a complex business process, even if it does reduce the need for analysis resources.

Think carefully about screening criteria and be sure that they are robust and defensible with management and process participants.

Manage information flows, retaining screened items and their bases. Provide feedback to owners of screened investments so that they can see the benefit of the process and improve subsequent investments.

Document the lessons learned from the business process and information flow and be prepared to use them in future asset management applications.

Systems-Level Thinking

In the introduction to this chapter, we discussed the concept of systems-level thinking and provided examples of its benefits. Systems-level thinking is a very important concept for asset management and imposes significant challenges to asset management information technology. Both company employees and software vendors are quite familiar with systems. The challenge comes more when the asset manager (and the operator and the maintenance planner) has to consider the logical groups of components below the traditionally rather large system designator.

A good example is an HVAC “system”. This often large and diverse set of components performs many functions as it relates to important assets. A rather small branch of the system may cool a very critical asset whose failure can cause a significant loss to the company. The same is true of a power distribution system, whether it is serving components in a generating plant or customers in an industrial park. Making the problem more complex is the fact that the component may provide more than one function.

As asset management programs mature, they are able to better understand the often substantial differences in value of individual functions within a system. The information technology system must similarly mature. It may be important that the asset register include a list of “functional equipment groups” that the component serves. It may be important that data mining tools have hierarchical displays that allow one to see the performance of overall systems, their individual


4-8

functions, and the corresponding components. It may be important that project engineers can easily identify and visualize the components that perform certain system functions so that improvements can be done at a much lower expense than might occur by improving the system as a whole.

Many EAM, GIS, and data mining systems have improved these types of capabilities in recent years. Asset managers and IT professionals need to be sure that those capabilities are present when they procure, implement, or enhance those systems.

To conclude, the following steps should be considered when employing systems-level thinking:

Wherever possible, use established system definitions and boundaries, but balance them against the boundaries that are logical for analysis.

Incorporate system level thinking concepts in training and work processes for the asset management group. Lead by example.

Save anecdotal experiences of situations where systems-level thinking points to the correct solution, and use them in communication and training.

Understand how systems thinking can be visualized and exploited in the existing asset management information technology systems.

Attempt to get hierarchical displays and processing in the requirements for systems in the asset management infrastructure.

Project Prioritization and Investment Tools

Project prioritization and investment tools grow in sophistication as the asset management program matures. Additionally, a more mature program has probably increased the number of projects subject to the analysis. In the previous Chapter, we discussed how corporate value models can be used to improve sophistication and consistency for this type of analysis. Earlier in this Chapter we discussed how screening techniques can be used to control the analysis resources applied to this problem.

Despite these efficiencies, project prioritization and investment tools are probably becoming time consuming to use. Improved automation can possibly reduce the time it takes to “feed” the project prioritization tool. Many have considered automating the links between project prioritization software and EAM systems. However, this integration may be more difficult than might be imagined. The key to the integration will be the degree to which the attributes of project value are stored in the EAM system in a manner that can be translated into the project prioritization software.

It is more likely that a process of educating project engineers on the corporate value model will be the appropriate first step at this level of maturity. The give and take between those who have to provide the information (project engineers) and those that must use it (asset managers) is best done without an expensive software integration project in the middle. If indeed, as suggested in the previous Chapter, the performance monitoring criteria and the corporate value model are in synch, the process will be easier.


4-9

Perhaps more importantly, the project prioritization process will then not only estimate value of completed projects, but it will also estimate changes in performance criteria that should be expected. With impacts on performance monitoring estimated, it will be much easier to set up an auditing process for measuring project performance, an attribute of the more mature level we call Processing.

The remaining improvements in project prioritization and investment that occur at the Organizing level of maturity revolve around improving the robustness of project definition and expanding the scope of investments beyond capital to large O&M projects. Regarding the robustness of project definition, the maturity of an asset management program is linked to the company’s ability to generate a robust set of meaningful and useful alternatives. Obviously, alternatives that are lower in costs are useful since they allow progress on issues even when available funds are limited. And of course, where repair or refurbish is an option to replacing an asset, the more mature asset management processes include that option explicitly.

But it is also important that alternatives of different risk levels and alternatives with different types of values be considered. An important organizational discipline is to avoid combining projects (or issues) in a manner which results in “gold plating”. Combined projects should be an alternative with a synergy that results in a benefit. If not, the larger project should be evaluated as separate individual projects.

Finally, it is important that the timing of projects be addressed. A project might be able to be delayed without significant increase in cost or loss in benefits, at least initially. This characterization of the role of time is important as benefits may not change linearly, especially as equipment ages.

An assessment of the probability of achieving the design level performance should be made. Often, the models used to predict system or equipment performance or the company’s historical data can be used for this purpose. For alternatives employing new technologies, engineering judgment may be all that is available. Nonetheless, quantifying in some fashion the risk of each alternative not performing as expected is an important part of a fair evaluation in a maturing asset management program.

An Organizing company has also begun to look at expenses outside of capital expenditures. Large O&M expenses pose problems and opportunities. Trying to absorb large O&M expenses in a maintenance budget without explicit consideration creates inefficiencies. If all such expenses were uniformly levelized, separating out large O&M expenses would be less of a concern. But the reality is that capital is often installed in boom-bust type cycles matching the economy or local load growth. O&M expenses will usually follow this same cycle. By attempting to levelize such costs, a large O&M expense might be deferred, when in fact, such an expense may be a very good investment.


4-10

The following are key capabilities of project prioritization and investment tools:

Supports creation of a corporate value model, including the following:

– supports qualitative and quantitative approaches

– includes value attributes, scales and weights

o values can be represented hierarchically

o scales may be non-linear and also qualitative

o weights can be developed by comparison of projects with different attributes and not just by top-level specification

– specifies uncertainty/risk information

– develops a “user guide” for project originators to input their projects

Supports project definition

– specifies all values and scales for any project

– includes life-cycle value and cost estimates

– specifies a variety of alternatives, including mutually exclusive and contingent alternatives

– specifies job done and job not done values

– supports input of uncertainty/risk information

Supports optimization and portfolio selection

– selects a portfolio given a constraint (budget at a minimum and preferably other constraints)

– selects the best alternative for a portfolio

– evaluates project deferral as an alternative

Supports a variety of visualizations for management and project originators for the purposes of reviews and intermediate and final approvals

The capabilities mentioned above will allow a company to satisfy the criteria of the Organizing level of maturity as well as progress to a higher level of maturity (see the Maturity Index Table 2-1). An increasing number of commercial products can be found with all or many of these capabilities. Alternatively, some utilities have had success developing custom spreadsheets to perform the calculations for evaluating and prioritizing investments. Although such spreadsheets do not have the full capabilities mentioned above, they can be sufficient (but possibly cumbersome) for an Organizing level of maturity.

But the real challenge for information technology is the amount of pertinent information that needs to be retained, managed, and exposed to decision makers and reviewers. If the associated business processes are well documented and described, it is possible that the IT group can develop capability to help manage the information. For example, data mining tools might be used to gather information that would determine or validate cost estimates and potential benefits.


4-11

Portals might be created to provide access to projects, alternatives, and their analysis of value. Otherwise, it is better to focus on documenting the business process and improving it, then looking for information technology that can aid the analysis and communicate more clearly the results and their bases.

To conclude, the following steps should be considered when building or acquiring and when implementing capability for project prioritization and investment:

Remember that project prioritization and investment is probably the central decision support activity in asset management programs at the Organizing level or below. Be sure that asset management concepts are included in the methods used and obtain some level of ownership in this activity for the asset management group. Also be sure that management support exists for process improvement.

Develop a good understanding of the investment process (explicit and implicit) before focusing on acquiring a project prioritization tool.

Decide where improvements in the process are desirable. Be sure to focus improvements not only on the value model but also on the process for generating and evaluating alternatives. Also, determine a scope of application in terms of the degree to which large O&M projects will be considered.

Develop a plan for increasing the quality of the process.

Education project sponsors on the importance of a good investment process.

Select a tool for assisting in the project investment analyses. Choose a tool that is either technologically simple, e.g., Excel, or one that has the capability to grow in analysis sophistication. The criteria above are a good guide.

Build a documentation process around to tool to ensure that the basis for the input, models, and decisions is clear and reproducible during the next budget cycle.

In the next Chapter, the report describes a variety of ways that asset management information can be visualized. Many of those visualization methods apply to the problems noted in this subsection.

Improving Data Quality and Integration

The Organizing company is building upon its successes and lessons learned from creating an asset inventory and developing and implementing an equipment data structure. Because that data is part of the EAM system, it is growing in size and subject to some level of quality control. The Organizing company is mature enough to expand the scope of asset management data programs to include greater breadth, improved quality, and easier access as well as integration that allows access to other applications.

The reader might wonder why the following discussion does not apply to the data work already done on asset inventory and equipment data structure. In reality, it does. One difference between the Awakening company and the Organizing company is the lessons learned from developing an asset inventory and an equipment data structure. With these projects in hand, the


4-12

Organizing company is ready to take on more data, in many more systems, and of many more types.

The first step in this process is to develop what might be called a data strategy, which identifies data available and data needed. Other steps include ensuring efficiency of data collection and management, ensuring data quality, and providing for data access and integration.

Developing a Data Strategy

Developing a data strategy begins with a good data inventory. (A good starting point is the inventory of data sources pertinent to the asset inventory.) The inventory of data is compared to data needed by asset management decision support tools, including performance monitoring. If data needs exceed data available, provision has to be made for acquiring additional data. The data strategy should be developed with inputs from those business units who collect and use the data and from the information technology group.

In practice, the data strategy is continuously evolving because there is a continuing iteration between adding new decision support capability and identifying and integrating new data. This report takes the position that a corporate value model that addresses both performance monitoring and project prioritization and investment (see Chapter 3) will define much of the data needs for decision support tools. Most of the remaining data needs will be specified in the equipment data model described in Chapter 3 and the cost data model described in Chapter 5. The equipment data model, because it is a degradation model, will represent most of the equipment condition and monitoring information needed for maintenance applications and life-cycle management decisions.

Much of the equipment data collected results from normal maintenance activities. However, it is important that the history of these activities not be “islanded” in the maintenance system. Rather, this information should be available to the larger asset management process, of which maintenance is just a part. In addition to triggering maintenance, such information should be used to determine how well past asset management decisions have been implemented and whether the expected improvements have resulted. For example, maintenance is interested in replacing worn contacts and restoring breaker functionality. The asset manager is interested in knowing that the contacts from the new supplier are not performing up to expectations as well. As mentioned before, this condition assessment information also can provide a starting point for projecting future asset or system condition through the use of degradation models.

The following draws heavily upon Guidelines for Power Delivery Asset Management: A Business Model for Program Implementation – Expanded Version, 1010728. It also makes use of applicable insights from the DOT work on information technology for asset management [1].


4-13

Most companies already have many of the ingredients of an asset management information system including various tools, models and databases. Some of the more common components that are relevant to asset management include the following:

Enterprise Asset Management (EAM) System (which contains the Maintenance Management System as a principal component). These systems typically include asset inventory, work order management and history, and equipment failure and repair information. As described in Chapter 3, the asset inventory is the critical source of basic asset information. The EAM also has schedules of major maintenance activities that are useful for project prioritization and investment.

Operations Data Historians and Condition Assessment Systems. Condition assessment information, e.g., thermal monitoring, operation counts, run hours, etc., may be integrated into the EAM system and used to trigger maintenance. Condition assessment and operations data may also be mined or accessed by an operations data monitoring system available in conjunction with a data historian.

Geographic Information Systems (GIS). Since much of a utility's asset information can be tied to a geographic location, another significant element of an asset management system is a CADD or GIS-based map of fixed assets and related data. The GIS may also identify compatible units, or other logical groups of assets, that can or should be evaluated together.

Customer Information System. These systems commonly contain payment history, work order history by customer location, and customer correspondence including compliance and billing data.

Engineering and design tools. These tools may be a part of the EAM system. These tools, or related project management tools, will contain project descriptions, costs, and schedules for both projects and alternatives.

Operational excellence tools. Operator logs and tag-out records may identify outage times for planned maintenance and forced outages. Outage times can be important for understanding and managing planned exposure to risk, i.e., when a level of redundancy/contingency is voluntarily removed and risk levels become much higher. Forced outage times are also an indicator of possible need for action to improve operations, maintenance, or design of an asset.

Finance Databases and Models. These databases may include billing, accounts receivable, accounts payable, tax data, budgeting and forecasting, valuations, and debt management. Often, because of security issues, special arrangements, such as data dumps, must be made in order to provide this information to asset management.

Financial Models for Developing Customer Rates and Replacement Planning Models may also exist in some utilities.

Human resource (HR) systems. These systems contain important resource and financial information, including available human resources by qualification and category and wage rates – direct and indirect (e.g., benefits).

Capital Planning Data. Most utilities have some type of database related to their facility capital improvement projects.


4-14

Strategic and business planning tools. Corporate and possibly major business units will have a tool or spreadsheet containing strategic initiatives and SWOT analyses as well as business and financial forecasts.

Organizational excellence tools. Benchmarking and other continuous improvement processes are an important capability that is further enabled by asset management. However formal tool interfaces are not required.

Business Processes. Individual functional groups within a utility often maintain some information related to their key business processes and standard operating procedures. Sometimes performance measures or targets may also be included.

An audit of the existing information systems should be part of the data strategy development. The audit should include:

Currently available data

– Accessibility

o Cost to access

o Projected future availability

– Location

– Source

o Where and how obtained

o Update frequency

– Data Base Structure

o Format

o Size

– Quality

o Timeliness

o Accuracy

o Integrity

o Consistency

o Completeness

o Redundancy

Current uses of existing data

– Business processes

– Reports

Assumptions and definitions

Communications requirements

Applicable IT and data standards


4-15

Caution should be exercised in specifying new data sources. Costs for collecting, storing, and maintaining data must be considered, and only data for which there is a clearly defined use and benefit should be included in the strategy. No data should be collected more than once. If individual processes require the same kind of information, but in different formats, or at different levels of detail, then automated methods should be established for deriving the necessary information from the primary source.

It is quite likely that much of the data required will already exist somewhere in the organization. In some cases, if the data is not directly available, then it may be possible to take advantage of an existing data collection process to acquire it. It may also be possible to stage the migration of data to provide near-term improvement while planning for longer-term redevelopment. Regardless, the cost of data collection and maintenance needs to be considered in light of the value of the data needed. Identifying a data point alone is not sufficient. The required accuracy, timeliness, and integrity must also be determined from an understanding of how the data will be used.

Ensuring Efficiency of Data Collection and Management

It is critical that the asset management program makes use of existing data collection processes. Organizational implications of collecting asset equipment data are critical. Transition to the Organizing level of maturity requires success in multi-organizational initiatives. Maintenance and asset management organizations working together offer good opportunities for such an initiative, the abovementioned approach being the best. Since maintenance is often the prime user of the EAM installation, at least initially, early cooperation between maintenance and asset management will improve the data that is stored in the EAM system. Good initiatives for cooperative development include condition-based maintenance program development or enhancements as well as maintenance planning program improvements designed to address maintenance resource limitations.

Operations and asset management offers another. The above discussion mentions that asset managers need to obtain records of planned and forced outages, information typically available in operations logs. Other examples include collecting equipment operations counts or observing equipment condition, information typically obtained during operations inspections. This information is then used by others for asset management decisions.

In a multi-organizational initiative, one organization is collecting a portion of its data primarily for another organization’s use. If the two organizations work closely together to ensure there is both immediate gain and mutual gain from the effort, the initiative will most often succeed. If the collectors of data ignore the users, the users will have to spend a great deal of time reworking or recollecting data. If the users of data ignore the needs of the collectors, the collectors will often not collect high quality data.

Successful asset management almost always involves data or actions or both crossing organizational boundaries. It is extremely important that successful multi-organizational initiatives occur early in the development of asset management programs. If they do, investments in information technology for asset management will be more successful and asset management will progress faster and gain more management support.


4-16

Other considerations for efficiency include making multiple uses of data collection equipment and procedures, good training on data collection especially when users and collectors are different people, and consideration of sampling techniques.

Ensuring Data Quality

Ensuring data quality can be done with standard quality assurance techniques. Data mining tools can complement the data collection process by identifying patterns in poor or missing data. Users should provide feedback regarding the value and timeliness of the data and suggestions for data collection improvements, based on lessons learned from its use in decision support tools. Another important consideration is to ensure collectors of data have proper incentives, especially when they are in another organization. Often incentives can be as straightforward as finding ways for the data collected to improve the collectors’ work processes.

Provision should also be made for data quality feedback that results from asset management analysis. When an anomalous result is attributed to a data quality problem, rather than an asset problem, the asset management business process should provide for a cause evaluation and feedback to the data collection process.

Another important aspect of data quality is eliminating duplicate information. The data inventory should identify multiple sources of information, from which a best source or combination of sources should be identified. A plan for eliminating other sources should be made, even if it is a long-term plan.

Opportunities for improving the timeliness of data should be considered if needed. Many times, timeliness problems in data are the result of poorly planned data collection practices. That is, it is rework of data that is costly and time consuming. If rework can be eliminated by collecting the right data at the right level of detail, timeliness and cost of collection will both improve.

In all cases, improvements in data quality should be guided by the value of the data. The value of the data should be related to its value in the asset management process. Plans for improving the quality of data should be realistic. Organizational change takes time, feedback, process improvement, and management support.

Providing For Data Access

The data strategy will define the important asset management information that is available and where it is located. The data integration strategy will be the provision of the information technology group and should fit with their overall information technology plan. However, it is important for the IT group to be aware that the asset management group, because of its dependency on data from multiple sources, will be limited in effectiveness and will not mature without good data access. The following subsection describes items which have general importance to asset management information technology and will also increase the capabilities of data access and integration.


4-17

To conclude, the following steps should be considered when improving data quality and integration:

Start by understanding what asset management related data you do have. Identify the sources within the company. Develop a corresponding Data Strategy by comparing data needs with data available.

Be cautious in developing new data. Ensure that there is sufficient value for all new data. Usually it is better to develop existing data sources, gain experience, and document lessons learned before investing in acquisition of new data.

Look for opportunities for developing strong organizational ties with maintenance. Two of the best opportunities are when condition-based maintenance is developed or enhanced and when maintenance planning improvements are made to overcome maintenance resource limitations.

Similarly, look for data collection and use opportunities with operations.

Identify and document lessons learned from multi-organizational initiatives in data collection and communicate them to management. Consider them in future asset management information technology investments.

Establish a formal quality process for data that addresses the data issues described above. Ensure this process is a continuously improving process with continuously improving data.

Ensure that data access and data integration strategies are developed in conjunction with the IT group.

Include the above strategies and processes in the asset management infrastructure plan described below.

Employing Critical Systems in the Information Technology Infrastructure

In Chapter 6, this report examines some of the more advanced approaches to improving the asset management infrastructure. In this subsection, the report identifies logical steps that the Organizing company can take to exploit its existing information technology infrastructure as well as to gain the lessons learned that will make the advanced techniques much more likely to succeed.

Here we focus on two important topics. The first, establishing “ownership” for asset management information technology improves the organizational aspects that allow the maximum utilization of information technology. The second, providing data mining capability improves the technology in the infrastructure.

It is worth mentioning that both these improvements build upon and add value to the other steps that have come before. Active involvement by asset management in the development of an asset inventory and equipment model for the EAM system establishes asset managers as an important stakeholder for the IT group to be aware of. Those capabilities also allow data mining to be done more easily and more effectively. The same is true for the steps taken to improve data quality and integration. Finally, decision support tools will, in some cases, benefit from direct integration with the information technology infrastructure, e.g., the EAM. In other cases, the


4-18

best choice will be to provide the required data through an intermediary system using the data mining capability described below.

Asset Management Technology Ownership

Asset management information technology should be “owned” by the Asset Manager. (Here we use the term Asset Manager to be the manager identified by management as the leader of the asset management program.) The Asset Manager’s information technology team should include representatives from finance and each of the core business processes, e.g., maintenance, operations, and engineering. Those representatives will be responsible for ensuring that the data collectors within their groups provide good information that is timely, reliable, and accurate so that the asset management business function can support decisions when they need to be made.

The Asset Manager should be ultimately responsible for the asset management information technology; however, the Information Technology group should appoint a “co-owner”, or IT Asset Management Lead. The IT AM Lead is responsible for ensuring the IT infrastructure is effective in supporting the current AM tools and interfaces as well as the growth of the asset management function. The IT Asset Management Lead should also receive the active support of the corresponding IT owners of the systems in the asset management information technology infrastructure, e.g., the IT owner of the EAM system. (A dedicated asset management program with Executive sponsorship and support should also have similar sponsorship of the CIO).

The IT Asset Management Lead is responsible for defining the architecture for databases and systems that support asset management and provide corresponding requirements for asset management decision support tools and performance monitoring tools. This IT “co-owner” is similarly responsible for developing an IT implementation plan that supports asset management and aligns effectively with the implementation and update plans for the systems in the asset management information technology infrastructure.

The Asset Manager and the IT Asset Management Lead must work effectively as a team to lay out a phased approach for asset management automation. An “as-is” approach will likely involve too many manual processes. Asset management will fail to produce timely information at an acceptable cost. A well-integrated system is a longer term proposition, appropriate only as the asset management program begins to mature from Processing to Continuously Improving (see Chapter 6 discussion on information technology infrastructure). The key attribute of a successful asset management automation approach for an Organizing company is to make maximum use of existing infrastructure, while at the same time identifying weak points and gaps and moving expeditiously to resolve them. The more aggressive the information technology plan is, the more important it is to have executive sponsorship, including the CIO.

To conclude, the following steps should be considered when building or acquiring capability to support long-range planning:

Some level of asset management ownership should be established for the asset management information technology infrastructure. Perhaps it is a place at the table with maintenance and the IT group, preferably more.

Develop a strong relationship and point of contact with IT.


4-19

Gain management sponsorship from line and IT senior management.

Establish an asset management working group to assist in developing the consensus needed to adequately manage the asset management information technology infrastructure.

Lay out a plan for development of the asset management information technology infrastructure. Gain management approval. Implement the plan and improve the associated business processes so that the asset management infrastructure continues to mature at a pace corresponding to asset management business process improvement.

Data Mining Capability

This report uses the term data mining to describe an information technology some prefer to call performance monitoring. In this report, the term performance monitoring represents the discipline or overall technique, rather than the software tools. Often a performance monitoring tool is a data mining tool. Equally often, an Organizing company will have a variety of other data mining tools, including one used by finance, one used by operations, and of course Microsoft Access, which will be used by a number of innovators and power users throughout the company.

It is typically the purview of the IT group to decide how many data mining tools it wants to support and the degree to which it puts limitations on Microsoft Access database applications. However, asset management requires data mining tools with unique capabilities, and those capabilities may not be present in one of the existing data mining tools.

This subsection describes those capabilities for an asset management data mining tool. It does not however describe requirements for the information technology architecture. That is, we leave it to the IT department to determine the degree to which the system is thin client, supports web services, is .NET, etc. For the asset manager, it is much more critical that the system be able to process real-time data and combine it with transactional data types, and it is much more important that the data mining tool have convenient interfaces with a variety of condition monitoring capabilities and EAM systems.

Asset management decision support tools and data mining tools often work hand in hand in a well functioning asset management program. Data mining is used as a class of automated analyses that involve acquiring data, processing it (often through calculations) and reporting it (or storing it for further use). Data mining occurs across, among, and within different business units, as does asset management.

As described below, a robust tool with good data acquisition and report capabilities may function effectively for asset management provided it has good calculation capability that easily supports algorithms and applications. A good operations performance monitoring tool often contains excellent data interface capabilities for asset management because it must access real-time and a wide variety of condition monitoring data. Further, an operations performance monitoring tool often has the additional positive attribute of flexible reporting capability suitable for a wide variety of users (many of whom are “clients” of asset management as well). Their reporting capabilities are often robust and can support many or all of the visualization requirements described in Chapter 6.


4-20

Automated analyses can be done in a variety of ways. The capabilities below are articulated using an information technology solution involving a three-tiered approach that separates enterprise data acquisition from the algorithm/application and reporting layers (see figure 4-1).

www reportscharts

Retrieve data Analyze/graph data

DataAcquisition

Layer

Algorithms andApplications

Layer

ReportingLayer

Databases

Figure 4-1 Three Layer Architecture for Asset Management and Performance Monitoring

Data Acquisition Layer

The data acquisition layer must support access to a broad range of data. For asset management applications, for example, data includes work management data, materials and services data, component, circuit, and system reliability data, and financial data (fixed and variable costs) at a minimum. Although all of this data resides in separate information “silos,” a robust underlying data acquisition layer can facilitate the creation of a virtual data warehouse and provide a facility for simple or complex query generation.

At a minimum, the data acquisition layer should comply with the following basic requirements:

Extracts source data from disparate enterprise or point solution databases, files, or application output without duplicating the data (i.e., provides a facility for the creation of a virtual warehouse)

Provides a facility for data transposition, allowing an end user to define dimensional relationships or hierarchies within or among the data sources


4-21

Offers data management capability (i.e., data merging, interpolation, correlation, and cleansing)

Offers a library of standard connectors to enterprise systems (e.g., work management system, financial systems, etc.) and condition assessment systems (e.g., thermography, oil analysis, etc.)

The virtual warehouse is of considerable value to both the utility IT organization and the data consumer because the input and output data are retrieved and derived directly from the source, respectively, via data mapping and asset object models. This allows the data to remain in its native environment, avoiding needless data duplication and long-term, costly database maintenance, as well as alleviating potential data quality issues resulting from running analyses against a non-production warehouse that is out of synchronization with the enterprise system production data.

Since data should remain in its natural format and environment, the data integration platform should provide the ability to map different calculated or monitored data values stored in multiple data sources to a given asset via a simple interface that facilitates the creation of such relationships.

In addition to the above requirements, the data integration platform should offer an intuitive user interface for configuring desired data sources. At the same time, it should impose a configuration model that exposes the mapping of assets to relevant data. A new trend in these types of tools employs the “meta-data” configuration technique, which allows similar objects to be created from existing objects as long as business rules – such as the query parameter controls, results, and interfaces – are captured in the meta-data objects.

Algorithms and Applications Layer

In the context of asset management, the algorithms and applications layer takes on a variety of important functions. Perhaps foremost, it is a knowledge capture element. That is, an asset’s physical or financial performance can often be expressed as a set of rules or equations. The algorithms and applications layer must have the capability to capture such rules or equations in the form of the data available in the data acquisition layer and the values and performance indicators visualized by the reporting layer. Further the capture process must be in a form familiar to the experts from which the knowledge is being captured, allowing them to perform the majority of the capture process themselves.

In many cases, the knowledge has already been captured in the form of a query, an algorithm, or a program. In these instances, the algorithms and applications layer automates or delivers the capability. Some of the more interesting applications of the algorithms and applications layer in asset management are cases in which a tool or software application used by one department is connected to current time data (real-time and/or transactional) and the resulting values become available for use in another department. In this way, an engineering or planning tool run in that department by manual input processes can be connected to current time data and publish information that can be acted upon by maintenance and operations personnel. The engineering


4-22

tool in turn becomes more valuable because engineering decisions can be based on an integrated accumulation of real experience, rather than on discrete manually input approximations.

At a minimum, the algorithms and applications layer should offer the following capabilities:

Implement current industry standards. The tool should use standard database connectivity protocols and support active data objects for use with extensible mark-up language (XML).

Offer extensible analysis capabilities. The calculation capabilities in a decision support tool must be extensible. The tool must provide or support add-ins or other types of interface “hooks.” Interface hooks allow for the usage of additional unique, specialized, or proprietary analyses from third-party vendors. By being extensible, the tool expands its calculation capabilities through the use of third-party industry-specific products, which provide sophisticated analysis and predictive capabilities.

Offer data manipulation capabilities. Some analyses may require extensive desktop data manipulation capabilities that include desktop joins, data merging, data jumps from one system to another, drill downs between major and minor data sets, incorporation of equations, incorporation of proprietary analyses, the incorporation of industry standards, and the summarization of large data sets through the use of data binning.

Offer the use of templates. The tool should record the construction of an analysis. A user should be able to access the record (i.e., a template) to repetitively perform the analysis using different but related input parameters that can apply to a large set of related assets. The time-series data should allow relative dates rather than actual dates so that repetitive analyses can occur on a periodic basis without any user intervention. Finally, the templates should be available via e-mail on a scheduled basis or available over the Web on-demand.

In an asset management process, there are several different calculations required to calculate the net present value associated with the evaluation of alternatives. For instance, the following calculations are certainly necessary to determine the financial effect of a given alternative:

Operations and maintenance costs

Spares analysis

Forced outages and overloads

Failure rates and repair times

To perform the above calculation types, the algorithms and applications layer should facilitate the execution of standard and ad-hoc queries, perform simple and complex equation processing, and perform probabilistic risk and Monte Carlo simulation-based analyses.

The tools that provide the necessary calculation and automation capabilities, while at the same time providing integration of asset management decision support functionality, are the best choice for meeting the analytical and automation challenge imposed on the typical utility organization (i.e., to assess the health of and development of a long-range plan for a system or component).


4-23

Finally, in the overall context of an asset management decision support environment, the algorithms and applications layer allows content associated with or knowledge of an asset to be maintained separately from the enterprise system. For instance, content can reside in the form of preventive maintenance or corrective maintenance queries by equipment type. The knowledge management aspect of the algorithms and applications layer helps overcome well known weaknesses in decision support capabilities of traditional enterprise asset management and enterprise reporting systems. Of equal importance, this approach provides the evaluator all the benefits that can be derived from maintaining content and knowledge separately, including easy updating when the enterprise system is replaced and provision of a common access point when the same knowledge is used in multiple enterprise systems.

In summary, the algorithms and applications layer facilitates knowledge capture, automation of previously captured knowledge, common access to knowledge, and independence of knowledge from enterprise systems. In this way, the algorithms and applications layer produces important efficiencies for business operations.

Reporting Layer

The requirements of the reporting layer include the following:

Display content in a format consistent with key performance indicators and financial reporting requirements, e.g., the general ledger

Display content in a format consistent with evaluations and forecasts

Several commercial “off the shelf” (COTS) products can satisfy these requirements, including Business Objects, Cognos, Crystal Reports, ChartFX and others.

The user interface (UI) should offer the capability to manually select or specify several inputs, including the type of analysis to be performed. A thin client UI is probably the best approach for generic analyses, although some ad hoc analyses may need to be performed at the algorithms and applications layer. The interface should be capable of displaying graphical output, tabular output, or scalars, as discussed previously.

The following discussion provides some context into why these needs are important. To date, many asset management applications have been limited to a single parameter because of the capability of EAM systems. More sophisticated assessment algorithms utilize multiple parameters to construct a multidimensional measure of asset condition that produces a more comprehensive measure of an asset’s state, sometimes referred to as a “health index.” For a circuit breaker such an index could be constructed with an algorithm that combines parameters such as number of fault operations, average fault current level, elapsed time since last inspection, etc.

The development of these kinds of complex assessment algorithms for power delivery and generation equipment is the subject of on-going research at EPRI. But even today, these more complex algorithms have been in use for system monitoring applications for nuclear plants and condition monitoring applications for maintenance planning in both generation and power delivery.


4-24

Another set of evaluation algorithms for asset management concerns tracking costs or level of effort in a way that the normal financial and payroll systems may not. An example would be average labor hours expended to maintain one supplier’s equipment versus the labor required for another supplier of the same equipment type.

To predict future performance when the deterioration is not just a function of time but also of stress levels, the future stresses must also be predicted. If loading were one of the stress factors, then a load forecast could be used for this purpose, for example. Future performance may also be affected by changes in operating or maintenance practices or by replacement of individual components within a system. Consequently, these factors may also have to be accounted for in projecting future performance. The data mining tool’s ability to access projections through the use of models is therefore an important need.

Developing the proper assessment algorithms is the responsibility of the asset manager and decision support tools for applying them are important requirements for EAM.

To conclude, the following steps should be considered when building or acquiring capability to support data mining:

Start by understanding what data mining tools the company has.

Evaluate the tools against the requirements described in this chapter as well as the infrastructure requirements, ensuring in particular that the tool works well with both real-time and transactional data.

Select an important asset management application, e.g., condition-based maintenance planning, and use the data mining tool to develop the application.

Collect lessons learned and improve the process. Select a different tool if necessary.

Expand the applications to other decision support needs and other decision support tools.

5-1

5 ADVANCED INFORMATION TECHNOLOGY CONCEPTS FOR ENTERPRISE ASSET MANAGEMENT

This chapter is written for those companies that have reached the Processing and Continuously Improving maturity levels in their asset management programs. At these levels of maturity, business capabilities are such that organizational transformation is possible. As the maturity level names imply, business processes are well known, and process improvement is a way of life. This chapter describes the information technology infrastructure that can facilitate advanced enterprise asset management.

This chapter also describes advances in data and decision support tools that can be implemented because of the advanced business capabilities of the Processing and Continuously Improving company. Some of these advances depend on the advanced information technology infrastructure. Others can be implemented, albeit somewhat less effectively, even with the information technology infrastructure of the Organizing company. But those improvements still require the business improvements characterized by the most mature asset management companies.

Information Technology Infrastructure for Advanced EAM

Enterprise software and asset management business processes both pose unique challenges to the information technology infrastructure. They are tightly coupled with business processes, which are often poorly documented, and they cross organizational boundaries. Many requirements are unclear and some are conflicting. It is for these reasons that a large fraction of enterprise software initiatives have failed.

In the previous chapters, the report has recommended very measured developments in the asset management information technology infrastructure. But for companies at the maturity level of Processing and Continuously Improving, there is a maturity in management support, business analysis, and business process documentation and modeling that provides additional assurance that investments in the infrastructure will succeed. In addition, companies at these levels exhibit improved business acumen, the ability to learn from investment and use of EAM, ERP, and other major systems, and familiarity with asset management data and decision support tools.

In short, the Processing and Continuously Improving company is capable of exploiting advances in business modeling and information technology that advanced asset management can realistically build upon.

Advanced Information Technology Concepts for Enterprise Asset Management

5-2

Use of a Service Oriented Architecture in Enterprise Asset Management

Service Oriented Architecture (SOA) is a concept that is built upon the lessons learned of a variety of information technology developments, including information standards like CIM and the enterprise service bus (ESB). However, SOA is not technology per se, or even a technology standard; rather, SOA is a technology-independent, high-level concept that provides the architectural blueprint for enterprise system development and continued use.

SOA is based on the concept of business services. Business services mean business processes and services integration technology. Business processes are described in terms of business data and business logic. Services integration technology can be XML-based Web services, such as SOAP and WSDL, but it could be other technology as well. SOA is not specific to any particular integration or middleware strategy.

A Service Oriented Architecture is a software architecture based on the concepts of an application front end, services, a service repository, and a service bus. An application front end is the element of SOA that delivers value to the user. The services that the application front ends use remain fixed. The front ends initiate and control system activity and provide the user interface. It is presumed that front ends vary as user’s needs change. Services change less frequently, although the number of services can expand as the number of processes are automated.

Services are managed with a service repository. In subsequent chapters, the report describes how some EPRI research could be incorporated into a services concept and repository. Similarly, asset management algorithms, calculations, visualizations, and applications from vendors, industry groups, and the utility would be accessible. By matching the services to a business process and developing them through a front end, the enterprise asset management information technology infrastructure could be enhanced and remain vibrant and consistent with current industry and company practices.

The Natural Role of SOA in Asset Management

In this subsection, we use an example to illustrate how SOA might be used in an evolutionary capacity for asset management applications.

Suppose you are building a portal to allow people within the company to view equipment condition information for a wide variety of equipment types and from providers that are both inside the company, but from many departments, as well as outside the company, e.g., OEMs, diagnostic system specialists, outsourced maintenance specialists, etc.

The first thing that you do is to specify the services of interest. Those outside the company have remotely accessible services. The various departments of the company have also specified services and provide them in the service repository. Next, you build an application front end that displays the results of various condition information available. The front end accesses information by examining the type and manufacturer of the equipment, the available instruments and other observables, and also the available condition assessment services inside and outside the company.


5-3

After implementing the portal, you find that the portal is very effective in a variety of business processes, from maintenance prioritization to equipment replacement strategies to operations contingency planning. For maintenance prioritization, you find that you want more than just the single input alert that is allowed by your current work management system.

Now you might construct another application front end that uses a workflow approach. Workflow describes a set of activities in way that can be used to execute a business process. In the case of maintenance prioritization, there are more factors you want to consider, including in particular the importance of the equipment to overall systems performance. For equipment replacement strategies, you want to consider the manufacturer and model of the equipment as well as the inventory you have in stock. For operations contingency planning, you need to know what other equipment can serve as a backup and what their condition is as well as their near-term uses and expected service conditions.

Each of these types of users (maintenance, engineering, operations) probably prefers a different user interface (UI), and each process has different approvals and possibly different regulatory requirements to consider. In SOA, most of the technology would be reusable because it would be available as services. The users would describe their business process, and the IT department could develop the front end solely from that description.

The following discussion compares a typical asset management information technology application with typical installed technology and then again with an SOA approach, illustrating the point about how information technology inflexibility is a barrier to developing asset management applications.

Work management systems have some capability to access equipment condition information, and in fact, the capability has improved notably in recent years. But often the work management system capability falls short of what a particular type of equipment requires, or the capability does not facilitate data evaluation and documentation of actions taken.

For example, transformer maintenance might depend on real-time instrument readings, manual inspections, and periodic predictive maintenance activities. Planning the timing and selection of maintenance tasks could depend on multiple sources of equipment condition information and could be combined into the evaluation process. Because this type of analysis is often not supported by the work management system, many maintenance planning groups have developed point solutions for maintenance prioritization that operate outside of, or very loosely coupled with, the work management system.

As a consequence, condition information goes unused by operations or engineering staff because they are unfamiliar with the maintenance point solution and its underlying basis, or they cannot easily incorporate the information into their own decision processes and tools. Sometimes operations or engineering develop completely separate tools with different technical bases for equipment condition. Not only does this occasionally lead to confusion, but it leaves the utility exposed to second guessing by regulators when a major event occurs and the departments’ systems have provided differing equipment condition results.


5-4

Perhaps more importantly, a utility’s reliability improvement program can be held hostage to the lack of business agility the current system offers. Because of the cost and delay times associated with information technology, new evaluation processes and predictive maintenance techniques may not be available to maintenance, operations, and engineering in a timely manner. In the extreme case, inability to efficiently integrate with work management could result in a new technology not being applied until the work management system is upgraded, a costly endeavor that often gets delayed year after year.

Let’s look at how SOA might change the capability of asset management in this case. Equipment condition information becomes a hierarchical set of services. Standard adapters are specified for equipment condition service providers. Again, the service provider could be an OEM who offers the service as part of the equipment purchase or an outsourced maintenance contract. The service provider could be the equipment engineering group internal to the company. A service specification defines the type of information required and the contract for providing it.

A workflow specifies how multiple sources of equipment information are combined when they are available. As a result, there is a user interface and business process for performing the evaluation. Another service provides the combined result.

Each of the equipment condition services is maintained in a service repository which also includes the knowledge base upon which the evaluations or results are obtained and guidelines on their accuracy and application. Because we are talking about flexible services and composite applications, it becomes much easier to maintain and update the knowledge base in a separate knowledge management tool if that is appropriate.

Service Oriented Architecture offers a lot of promise to advanced forms of asset management. But for it to work effectively, there must be a business process modeling capability and there must be a service repository of asset management calculation types. Perhaps more important, there must also be a capability to automatically assign asset management services to the hundreds of thousands of assets in an electric utility company. Otherwise, even though applications are easier to write, we will still be limited by the engineering time it takes to determine applicability of the application to the specific asset and information available for it.

The following sections describe how EPRI research and technology can facilitate the capability of an asset management information technology infrastructure based on an SOA architecture.

Business Processes and Models

Business modeling provides an approach to improve productivity in a business. As businesses advance their productivity, understanding and improving critical business processes eventually become the best means to drive process improvement. The nuclear industry is a case in point.

To improve benchmarking effectiveness, the Nuclear Energy Institute (NEI), Electric Utility Cost Group (EUCG), and Institute of Nuclear Power Operations (INPO) worked to define a standard nuclear performance model (SNPM). This three-year effort resulted in closer


5-5

coordination of process descriptions, key business performance indicators (KPIs), and activity based costing (ABC) definitions [8]. Supporting this overall business process are process descriptions for key business processes, including Nuclear Asset Management [9] and Equipment Reliability [10], to name a few.

Business process modeling comes in a variety of forms. The information technology professional is familiar with the “use case”. It is a description of a business process designed to guide the implementation of one or more software applications. With the advent of Service Oriented Architecture, it will be important that business process descriptions are understandable to technology users like asset managers and that those process descriptions also conform to a standard compatible with the needs of information technology to support SOA.

As we all know from our daily lives, business processes are complex. They are difficult to visualize. As such, business process modeling begins with a visual representation of how the process works, often in a flow chart comprised of activities in the business process together with inputs and outputs and key process interfaces. The visual representation is accompanied by descriptions of each “box”. Grouping of these activities sequentially and describing the business rules that apply to them is the essence of a business process.

This type of generic business process is useful for many business improvement purposes, including benchmarking and process improvement. But to take the business process to a level where it can be a specification for information technology services, the specific role of people, places, time, and information may need to be specified.

One company, Business Genetics, bases its xBML™ approach [11] on familiar questions:

What activities must the business unit perform to achieve its purpose?

Who performs each activity?

Where are the activities performed?

When are they performed?

Which information is used to perform each activity?

How do all the above interrelate to yield a business processes?

In their approach to business modeling, once activities are identified, the business process analyst can then capture who is responsible for the completion of the activity, where the activity is performed, when it is performed, and which information is used and produced by the activity. Models for each of these questions are developed and then combined into an overall how model.

Regardless of the approach taken for business modeling, it is a critical step in the mature asset management program. Process must lead technology, or else productivity growth will have to follow technology. Large software development projects have taught us the difficulties in attempting to capture business processes in software.


5-6

In the Processing company, descriptions of processes facilitate the ability to capture cost information by activity. The role of decision support tools and the asset management technology infrastructure in those processes can be well defined and more easily understood by management and employees. In the Continuously Improving company, process improvement becomes ongoing. Asset management provides the metrics by which company goals and objectives can be measured. Business process descriptions become the means by which improvement can be described and implemented.

If the promise of SOA is realized, those business process descriptions also become the means by which information technology services are described. Large software development and application integration and implementation projects for asset management become incremental improvement efforts as opposed to disruptive events.

Business process modeling is therefore an important enabler for advanced asset management information technology capabilities. To conclude, the following steps should be considered when building the capability for business modeling:

Identify the various models that exist explicitly and implicitly in procedures and other business documents.

Acquire and understand generic electric power process models, even if they originate in a different business unit or are constructed to a variety of different “standards”.

Work together with the IT department to understand their use of business process modeling, including use cases that may have been developed for implementation of EAM, GIS, or other systems in the asset management information technology infrastructure.

Develop an asset management business process model for your company and use it to communicate how asset management works to management and employees in interfacing organizations.

Understand standards for business process modeling, and work with the IT department on any SOA pilot activities, even if they are outside of asset management.

Participate in a pilot process for process modeling of a key business process which uses asset management business processes. Help describe asset management’s role in that business process.

Continue to participate in the development and improvement of business process models that are related to asset management or the asset management information technology infrastructure.

Enterprise Asset Management Repository of Services

EPRI’s strong contribution to the development of the Common Information Model has naturally led to the development of a variety of capabilities that are compatible with the SOA concept of services. These capabilities are beneficial at most levels of asset management program maturity. Indeed, using libraries of algorithms is a critical efficiency that should be exploited even at the Awakening level. But a repository of services can be even more effectively exploited when the asset management information technology infrastructure has matured to a high level.


5-7

This subsection describes three types of EPRI research which naturally support the concept of a repository of services. The first example describes continuing efforts in EPRI’s Power Delivery Sector to develop improved maintenance management and asset management capabilities. Those capabilities developed from the use of specific EPRI software, but the resulting utility and software implementation of specific algorithms and calculations have been and continue to be stored with the intention of more widespread use.

The second example describes an effort to think about asset management decision support tools using a concept of modules. The work is inspired by some cooperative efforts between the EPRI and EdF nuclear business units. As described below, the concept is intuitively similar to the SOA concept of services, and the work done to date may lay a foundation for certain aspects of asset management service description.

The final example relates to another critical concept in the maturity of an asset management program. EPRI research and work with many utilities has clearly indicated that one of the difficulties asset management faces is the development of techniques that can help visualize the results of more sophisticated decision support tools. The concepts of risk and uncertainty, as well as alternatives and portfolios, are difficult to convey even to decision makers receptive to the concepts of asset management. The final portion of this subsection describes a set of visualizations that have proven useful to EPRI members in communicating asset management results. These visualizations focus on project prioritization and investment because much of the early asset management research has been on those techniques. These visualizations represent experience from both the power delivery and nuclear sectors.

Library of Algorithms and Calculations

In Distribution Applications of the Asset and Risk Management (ARM) Workstation, 1008565, EPRI described a series of asset management applications for distribution that could be carried out with a particular data mining tool known as MMW. In the language of SOA, the applications were a combination of front ends and services. The services most often where specific algorithms or calculations which would process mined data and produce a result useful for asset management. In one case, the service processed data so that it could be used in EPRI’s Project Prioritization decision support tool. The front ends were typically web page reports which provided the results in a form which allowed further interpretation and use, e.g., predefined drill downs.

EPRI has continued the process of developing and describing algorithms that can provide useful asset management calculations. These algorithms can be applied with data mining tools that have the capabilities described in the previous chapter. They also form the basis for a potential set of services that could be provided via a Service Oriented Architecture.

Modular Approach to Asset Management

The concept of a Service Oriented Architecture is compatible with a concept proposed recently based on research by EPRI. The concept is called a “modular approach to asset management” and is part of an overall concept called the Asset Management Toolkit (AMT) [12]. The concept has been discussed in EPRI reports for power delivery and nuclear generation. It has also been


5-8

informally tested in fossil generation by EdF. It appears the modular approach is really a description of a service, while the overall AMT approach is more related to the creation of a front end based on a series of connected services. The following discussion provides further details on the modular approach.

A fundamental premise of the asset management methodology is that decisions affecting the design, operation, and maintenance of components and systems (assuming that they are not constrained by local, state, or national regulatory requirements) will not only impact reliability and power quality from an engineering perspective, but will also affect the economic performance of the asset through their impact on expected costs and revenues. The asset management application will enable evaluation of these costs and revenues via several key business functions that can be represented in the form of modules (or services in the parlance of SOA). Figure 5-1 represents how these modules (services) combine to perform the basic asset management analysis of the value of a project or investment.

Figure 5-1 Asset Management Module Interaction

Meeting the information requirements of asset management business objectives and processes can involve a complex series of economic calculations. A modular approach and framework of these economic calculations promotes understanding of the many functions involved in performing asset management, regardless of the tools employed. The approach allows a prospective asset management user to build elements from existing capabilities in their software and information technology portfolio. Eventually this dissection of functionality can lead to the

Component O&M Data Provider (CMMS)

Failure & Maintainability

Rate Provider

Recovery/ Repair Time

Provider

PM Cost Calculator

CM Cost Calculator

Non- Maintenance

Cost Calculator

Reliability Calculator

Power Quality

Calculator

Delivered Power

Calculator

Power Price

Forecaster

Revenue Calculator

Total Cost

Calculator

Economic Calculator

(Profitability)

Externality Value

Provider

Asset Improvement

Optimizer (Portfolio)


5-9

specification of a toolkit of methods and tools (i.e., the Asset Management Toolkit) for improved and consistent asset management for all types of power industry facilities.

The goal of functional modules as applied to asset management is to benefit from the commonality of functional modules and their combinations to address a wide range of asset management applications. These can include component reliability diagnostics, performance reports, probabilistic risk assessments, net present value evaluation of projects, specific component repair/replacement strategy, reliability trend impact analysis, economic asset durability assessments, project evaluation and ranking, etc.

Modularization clearly establishes functions that can reside in differing areas of expertise, ranging from economics/finance to reliability to structural/fracture mechanics. The modules clearly define the role, inputs, and outputs for which each area is responsible. The same functionality can provide input data for several applications, and the same functionality can provide intermediate results used in several processes.

In a calculation chain, the input data for downstream modules are provided by outputs from upstream modules, such as the failure rate of a component, the replacement cost of a component, or corrective maintenance direct costs (see Figure 5-2). At the module boundaries, the data exchanged are either inputs or outputs. Of course, some of these data can differ for different issues, and they can be obtained from different modules. Any of these values can also have uncertainty distributions, which are propagated through the calculation chain.

Failure Rate Updaterfrom experience data

InputsInitial failure rateNew failure dateNew exposuretime

OutputNewfailurerate

Reliability Calculator

InputsFailure rateRepair rateSystem logicRecovery times

OutputUnplannedoutages

CM Cost Calculator

InputsTime to failureMaterial & laborcost of failure

OutputCM directcost

PM Cost Calculator

InputsImplementationdatesMaterial & laborcost of PM task

OutputPM directcost

Economic Calculator

InputsDiscount rateInflation rateTax rulesEnd of operationdate

Power priceReliabilityFuel costsDirect CM costsDirect PM costsOther costs(insurance,safety-relatedcosts, etc.)

OutputsChange inNPVBOIIRRCash flowDiscountedcash flow

Figure 5-2 Example Calculation Chain Decomposition


5-10

The modularization and its associated description provide the following benefits:

Modularization clearly differentiates the functionalities that use given data from those that create these data. The data flow can then be more easily understood and shown, including the global inputs and outputs of the approach.

Development of new modules benefit from the past developments, avoiding the rebuilding of existing functionalities, especially when a new tool is under investigation. The efforts can be focused on the specific part of the new approach, or on the assembling of the different elements to answer the new issue. For the modules that are implemented as software, the computer connection can be envisaged.

When a new approach is proposed, modularization can show the elements that are already mastered and the difficult issues that will need to be addressed. The R&D effort can then be focused on the new elements.

Modularization can improve information exchange between experts of the different functionalities, especially in the step addressing validation of the consistency of exchanged data in the process or the calculation chain.

Modularization can open other perspectives on the use of a module, either downstream (using its outputs) or upstream (seeking better assessments of its input data). This shows more clearly to other experts the potential interfaces, providing access to the objective of the functionality.

Modularization can help to clarify the black boxes of tools or processes by revealing their component parts.

Modularization can enable more robust tool evolutions and improve tool compatibility.

The modular approach to asset management offers significant potential for describing asset management services that can be stored in an asset management service repository and combined within a front end to perform basic asset management analyses. This approach offers the potential to replace legacy decision support tools which have often been cumbersome to use and integrate with the asset management infrastructure of EAM, ERP, GIS, etc.

Visualization Services

Useful visualization of the results of asset management analyses is a critical requirement in the asset management process. Illustration of results must consolidate views in various forms to address the key criteria of a wide range of decision makers, including executives, upper management, engineering management, engineers, financial analysts, and others. Hence, the results must address various financial indicators, performance indicators, cash flow projections, expenditure projections, and various ways of displaying uncertainties and risk. Decision makers must also be able to use the results across a control area, corporation, or region as applicable. This chapter provides examples using hypothetical results of various ways of effectively displaying the results of asset management analyses in the power delivery industry.


5-11

Phase Plane Graph

Figure 5-3 illustrates a phase plane graph. This type of illustration is particularly useful when two important stakeholder values (in this case, reliability and profitability) involve trade-off decisions. Plotting one value against the other in this manner can produce some useful insights. For example, projects in the upper right quadrant (e.g., tree trimming) improve both values, while projects on the left side decrease one of the values.

Plot of Cumulative Benefit versus Investment

A plot of cumulative benefit versus investment (the “funding curve,” often mistakenly called an “efficient frontier”) is useful for illustrating the effect of a constraint (e.g., a fixed budget) on the incremental value of a ranked ordered list of investments. In Figure 5-4, the projects are shown in ranked order from left to right along the curve. The budget constraint is shown as a vertical dotted line. In this case, the first three projects (substation transformer replacement, tree trimming, and relay upgrades) could be implemented and remain under the budget constraint. A scaled-back version of the capacitor bank addition project or some other highly ranked, lower cost project, could also be implemented. Alternatively, if the budget constraint could be relaxed somewhat, this next project could also be implemented. Note that even though the pole inspection project cannot be implemented, since the slope of the curve has flattened out in this region, the incremental benefit of that project is less than that of the previous projects.

Figure 5-3 Example Reliability-Profitability Phase Plane Graph


5-12

Figure 5-4 Example Cumulative Benefit NPV Versus Cumulative Investment NPV Graph

Project Uncertainty Comparison

Figure 5-5 illustrates a case in which two investment options have the same mean net present value (NPV) of $300,000, but different levels of risk. This visualization enables the decision maker to evaluate the probability of loss as well as potentially higher gains. In this example, option A is a safer investment, because its probability of loss is zero (no part of the curve crosses the vertical line at zero). Conversely, option B presents a 6 percent chance of loss, and a 7 percent chance of a higher NPV than option A. Hence, option B is “riskier.” Of course, this presentation technique also provides useful information when the two investments have different mean NPVs and less extreme differences in the shape of the probability distribution.


5-13

Figure 5-5 Example Project Uncertainty Comparison Graph

Probability of Loss for Multiple Projects

Probability of loss is a key concern for many decision makers. Figure 5-6 shows a convenient way to illustrate results of multiple projects that displays probability of loss. By comparing this loss probability to internal rate of return (IRR), decision makers can quickly see at a glance which projects are more favorable than others. In the example, substation transformer replacement offers a favorable IRR with no loss probability. The relay upgrade project provides a much higher IRR, but with a significant loss probability. By these measures, a clear project to avoid in this example is pole inspection, which shows a very high loss probability and very low IRR.


5-14

Figure 5-6 Example IRR Versus Risk of Loss Graph

Project Specific Uncertainty Histogram

The visualization illustrated in Figure 5-7 focuses on the uncertainty in IRR for a particular project. The histogram (cluster of bar charts) shows the probability distribution across IRR. The histogram is generated by making numerous simulation runs using a Monte Carlo simulation. The curve represents the cumulative probability in percent. Hence, there is about a 50 percent chance that the IRR will be lower than about 21 percent.

In addition to helping decision makers understand the risks of a project, this representation also allows consideration of the value of information that might help to reduce this risk. For example, the decision maker could commission an engineering study of uncertain parameters in this project that caused the “tails” of the histogram. Such a study could reduce the uncertainty or help identify changes to the project to reduce uncertainty. Similarly, the study could find ways of increasing the likelihood of attaining a high IRR by emphasizing those factors that led to the highest IRR. In one such study, the analyst could change the value of one of the parameters – preferably one that has the largest impact on cost as identified in a tornado diagram such as Figure 5-13 (e.g., price of power). By obtaining a better evaluation of the market price of power or investing in a hedge to eliminate this risk, the project’s IRR can be increased.


5-15

Figure 5-7 Example IRR Uncertainty Histogram

(Note: histogram refers to left vertical axis; curve refers to right vertical axis)

To conclude, the following steps should be considered when building the capability for repository of asset management services:

Identify the various algorithms, calculations, and visualizations available from EPRI research.

Identify similar “services” in your own company, including those you have purchased from vendors or obtained from industry groups.

Consider using the modularization approach to help decompose your own asset management calculations and decision support tools (including elements and applications of EAM, ERP, and GIS systems) into a set of services.

Describe the services in your company. Strongly consider the use of knowledge management techniques to ensure you capture the background, context, basis, and use experience for these services. Work with the IT department to jointly develop the service descriptions so that the goals and objectives of SOA are anticipated.

Formulate a repository for general use in the company, whether it be with existing applications, manually as part of asset management business processes or in the SOA environment.

Substation Upgrade – Option A – New Substation

0

5

10

15

20

25

30

35

40

45

0.81% 9.53% 18.25% 26.96% 35.68% 44.40% 53.11% 61.83%

70.54% 79.26% 87.98%

IRR

Population

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Cumulative Per Cent


5-16

Automating Service Implementation – Re-usable Analytics

In the abovementioned development and implementation of a library of algorithms and calculations, EPRI research found that one of the principal barriers to implementation of asset management calculations was the requirement for substantial engineering resources. The algorithms tie available information to actions. Both information and actions vary not only by company but also by instrumentation and other sources of information.

EPRI has initiated a pilot project [13] to develop and prove a prototype re-usable analytical platform, a “plug and play” module, capable of data acquisition, analysis, and publishing the result. The following discussion draws heavily from the pilot project report.

One challenge to making this content “plug and play” is that the scope and quality of available data varies from asset to asset depending on the asset-specific availability of instruments, tests, inspections and nameplate data. This project lays the groundwork for creating re-usable platforms by creating a “proof-of-concept” analytical module that demonstrates the potential to create generic applications such as those listed in the Asset and Risk Management Applications Library [14]. The prototype calculates circuit breaker operation counts, a simple analysis that demonstrates the capabilities of a re-usable analytic platform. As such, this groundwork could be used as the foundation for future EPRI application development or modifications of existing applications to use re-usable analytics.

Included in this body of work is an analysis of the functionality of a re-usable analytical platform used within a Common Information Model (CIM) [7] model-driven environment. The project built a simple application inside the commercial software platform known as OSIsoft’s Real-time Performance Management (RtPM) platform, commonly known in the electric utility industry as the “PI Historian.” The application developed is for demonstration of the concept only.

Acquisition of the data, upon which the analytics will be executed, is typically not shared between business applications. However, this is changing. As the Common Information Model and semantics become more widely adopted, it is becoming possible to create an acquisition component interface that can be re-used by multiple business applications and users. The constraint on the creation of such a re-usable and distributable acquisition platform is the agreement of the Common Information Model that is shared between the acquisition platform and the business semantics.

The successful completion of this project has resulted in a CIM-based application which can invoke a data acquisition engine that knows where the data resides. This type of technology can be applied to asset management applications to vastly reduce configuration time and setup time while increasing accuracy and providing advanced capability in data acquisition.

The purpose of this project is to:

Develop the “proof-of-concept” CIM compliant analytical platform. This platform has been developed within the simplified business application development environment provided by the OSIsoft Real-time Performance Management (RtPM) platform.


5-17

Populate the analytical framework with a set of analytics that demonstrate a “simplified” model, which determines the number of breaker operations.

Provide breaker operation data so that this information can be utilized as part of the testing.

Verify the analytical model based upon the calculation of number of breaker operations.

Methodology

Business and utility applications have a need to execute various kinds of calculations that recur frequently in different contexts. The following example demonstrates the process to calculate a count of circuit breaker operations.

1. An application (e.g. Schedule Circuit Breaker Maintenance) determines that a Circuit Breaker (CB) operations count is needed to determine if the CB needs to be operated to lubricate the mechanism. The determination of the requirement for such a calculation could be pre-scheduled, periodically scheduled, or upon the demand of the user or application.

2. The application or user determines the range(s) of time over which to count the number of CB operations. Ranges could be based upon a previous time period like one year or a variable time period like “since the last lubrication.”

3. The data is grouped with its associated time period (ts – te).

4. The CB Operations Count calculation is actually executed.

5. The results are returned, typically as an array of values, to the application or user and would include the start time, stop time and CB operation count between those intervals.

6. The application then decides what to do with the returned results (e.g. display, archive, etc.) and what time-stamp to associate with the returned results. The time-stamp is important to see when the calculation occurred. A recent operation, just past the stop time, would obviate the need to operate the CB for maintenance purposes.

(1) Need to executea specificanalysis

(2)Trigger

Specific Application Analysis

(3) Determine semantic,objects, and other parameterneeded for analysis

(4) Gather information/databased upon (3) neededfor analysis

(5) Execute analysisfor data gathered in (4).

(6) Return results and errors

Figure 5-8 General Analysis Flow of a Calculation


5-18

Regardless of whether a user or application uses Excel, MatLab, OSIsoft, or some other analytical platform, the conceptual steps shown are the same, as illustrated in Figure 5-8. All of these analytical platforms have the following in common:

1. There is a trigger for the execution for an analysis.

2. The trigger is applied to an application that has some configuration knowledge in regards to the analysis being triggered.

3. The configured knowledge (e.g. semantics4, objects, and other parameters) are gathered.

4. The actual data required for the actual instance of analysis is located and gathered.

5. The actual calculation or analysis is executed.

6. The results and/or errors are returned.

7. The triggered application stores or acts upon the returned results.

The typical analytical platforms inherently include all aspects of the general flow. However, depending upon the implementation, analysis can be monolithic, partially or fully distributed, or model-driven.

Architecture for Re-usable Analytics

There are several simplifying assumptions and insights that can be made that allow the creation of a re-usable analytical platform:

(1) Each application or user will have its own mechanism to provide a trigger for the analytics (e.g. scheduled, user requested, etc.). It is acknowledged that analytics need to be triggered, and this functionality must be provided within a deployment. However, each trigger implementation needs to be specific to the application environment, but it would not be wise to constrain the trigger mechanism in any fashion via specification.

(2) In general, each computer-based business application has its own mechanism to configure and maintain the semantic relationships that are needed in order to create the appropriate inputs for the analytics. Examples of such semantic relationships can be maintained through proprietary configuration mechanisms (e.g. backend databases) or through some standardized mechanism (e.g. such as IEC 61968/61970 extensions). It is acknowledged that semantics knowledge is needed, but this information varies on a deployment-by-deployment basis. Thus constraining the mechanism of gathering the semantics could potentially decrease the applicability of a re-usable analytical platform.

(3) If there is an agreed upon Common Information Model and agreed upon access methodologies, then the re-usable analytical architecture can support a data acquisition component. A simple representation of such a component might be expressed as Figure 5-9 shows:

4 In this context, “semantics” means the naming convention used to identify specific pieces of equipment. Mismatches in naming conventions are a major barrier to integrating equipment data across multiple sources.


5-19

Figure 5-9 General Architecture of Re-Usable Acquisition Component

A re-usable acquisition layer depends upon: (1) the mechanism through which the inputs are provided; (2) the ability to invoke the appropriate acquisition interface to obtain the required information and (3) the ability to output the data and/or errors that would be used within the actual analysis.

The ability to create such an acquisition component is greatly aided through the use of standardized and/or industry accepted client interface (e.g. ODBC, OPC, and IEC 61970 GID). It is conceivable that additional deployment-based interfaces could also be included (e.g. MatLab and OSIsoft) without impacting the architecture.

(4) The actual analytic/calculation portion can easily be designed for distributed re-use.

Figure 5-10 General Analytic/Calculation

The generalized analytic can be remotely invoked with: (1) Data and additional parameters passed into an established interface; and (2) results and errors being produced for return to the invoking entity (Figure 5-10).

Parametersand

Constraints

DataData

DataData

Generated byacquisition

ResultResult

ResultResult

ResultAnalytic Calculator

SemanticSemantic

SemanticSemantic

Parametersand

Constraints

(1) Inputs basedupon applicationknowledge

InformationalData sources

(2) Acquisitionof actual databased uponsemantics

Acquisitioninterfaces

DataData

DataData

(3)

Re-usable Analytic


5-20

Of particular interest to a successful architecture is the establishment of:

Well-known and specified, but generalized, input and output interfaces for each re-usable component. It is important that the specified interface not be specific to any particular analytic (e.g. equation or asset). Any component that is designed to be so specific that it can only work when in conjunction with a particular asset would have limited value. The concept is to create generalized components that can work in any situation. The generalization allows for more applications to make use of similar analytics provided by multiple suppliers.

A specification of a standardized remote invocation interface that is supportable across multiple platforms. Given today’s technology, the most likely candidate is Web Services or Service Oriented Architecture (SOA) architecture.

A mechanism that eases configuration and parameter validation.

A mechanism that allows individual debug of each re-usable component.

Applicability to Asset Management

Asset management represents a potential business domain into which the generalized/re-usable calculation architecture could be applicable. The asset management environment has all of the key attributes that lend the domain to the re-usable calculation environment. The application of the architecture should be extensible to transmission, distribution, and generation assets.

Asset management applications are intended to display asset performance, display asset condition, trend asset condition to predict maintenance requirements, define the work associated with assets, define the effectiveness of repairs, and define operational aspects of assets.

Asset management is particularly well suited as an environment for re-usable analytics in that the current implementations have defined many requirements of the re-usable calculator.

The definition of asset and the relationship to other assets and operational characteristics is typically well understood. The semantics and modeling aspects are well understood. The applications that monitor assets have defined the data required to support the application.

The sources of the needed information are available for the acquisition component. The monitored data is typically maintained in several identified disparate databases.

Most asset management applications have a set of well-defined algorithms. This facilitates the re-casting of the analytics into the re-usable and distributable architecture format.

Proof-of-Concept

The prototype re-usable analytical tool provides circuit breaker operation counts as determined from the acquisition of a “PI Historian” circuit breaker record utilizing the re-usable analytical platform.


5-21

OpenClosed

IV1 V2

Figure 5-11 Circuit Breaker Measurements

Most counting of breaker operations is based upon the SCADA monitoring of the status of a breaker (e.g. OPEN or CLOSED). However, given an ampere (I) measurement, the operation counting algorithm could be augmented to calculate not only the number of total operations, but additionally the number of operations where current was interrupted (e.g. OPENED).

Augmenting the model with voltage measurements can allow a determination if an operation occurred under service conditions or during a maintenance test when the circuit breaker was de-energized. The bushing potential device (BPD) would be the determining factor in this case as the bus and line voltage may be at normal values when maintenance occurs due to circuit configuration.

The current interruption/re-establishment plays a major role in the serviceable lifetime of the breaker contacts. Therefore, more accurate information can be calculated given more measurements. If the fault current can be determined from an Intelligent Electronic Device (IED), then the correlation of Fault Interruption Counts can be added to the array.

Table 5-1 Information Available from Circuit Breaker Counts and Related Parameter Data

CB Total Counts

CB Maintenance Counts

CB In Service Counts

CB Load Break Counts

CB Fault Interruption Counts

Data Source

CB Status + time-stamp

CB Status + time-stamp + BPD Value

CB Status + time-stamp + BPD Value

CB Status + time-stamp + CB Current Value

CB Status + time-stamp + CB IED Current Value

Algorithm Sum of status changes

Sum of Status Changes where BPD Value =0

Sum of Status Changes where BPD Value = normal

Sum of Status Changes coincident with CB Current Value to 0 change

Sum of Status Changes where IED Current > CB Rated current value


5-22

The proof-of-concept developed a representative model that allows the re-usable analytical tool for counting breaker operations to be created. As part of the prototype, the design was further refined, a data acquisition component was created, as well as the re-usable analytics.

This report includes the above description of this pilot project because technology advances in the near to mid-term could dramatically change the capability and implementation costs of asset management systems. Those changes in turn will make the Continuously Improving maturity level much more practical to achieve by a larger fraction of electric utilities. Driving down the engineering cost of implementation will be critical to the advancement of asset management, especially in an aging workforce constrained business environment. Equally important, approaches like these foster knowledge management and capture, another critical enabling technology for advanced asset management.

To conclude, the following steps should be considered when building the capability for the reusability of a repository of asset management services:

Identify the various algorithms and calculations, visualizations, and applications in the service repository that will involve large numbers of instances, in particular those requiring significant engineering and information technology resources to implement.

Pilot those applications and compile and evaluate lessons learned on the implementation and use of those services.

Follow EPRI research on this topic, developments in SOA, and progress in industry standards that bear on creating “plug and play” applications. Monitor related advancements made by vendors of systems in your asset management information technology infrastructure who are sponsoring and investing in this type of work.

Pilot an activity of this type when the technology is sufficiently robust. Gather lessons learned and develop plans for broader implementation.

Plan for this technology advancement, and implement it when appropriate based on the needs of asset management.

Knowledge Management

Previously, the report discusses a significant number of critical situations where capturing knowledge is important. The success and therefore the maturity of asset management programs are based on the ability to explain and justify data, models, and results. In most cases the information necessary to explain and justify is in dispersed reports and data or in people’s heads. This concern is particularly true of models, whether it is the corporate value model upon which numerous decisions are based or the equipment model for a critical asset type.

The knowledge management challenge for asset management is particularly acute because of the integrated nature of asset management. Asset management crosses business unit boundaries, therefore it is the combined knowledge of a disparate groups of employees that needs to be captured.


5-23

Asset management also brings together information from a wide variety of disciplines to make critical decisions. A repair replace decision requires hands-on knowledge of:

past performance and current condition,

replacement technology, including what is available in the near-, mid- and long-term,

project management and engineering experience on project implementation challenges and opportunities,

terms of financing for O&M and capital, and

regulatory requirements.

A typical justification for knowledge management, namely the aging workforce, hits asset management particularly hard because so many decisions today are made on the basis of undocumented experience. Many business processes require training from experienced personnel to work efficiently. Even when decisions and processes are documented to our own satisfaction at the time, we often find them hard to explain later, because few of us are trained with the discipline to capture what we actually do.

The following are a representative range of examples of information important to asset management in which documentation is usually found wanting in certain respects, even in an asset management program with a high level of maturity:

The translation of a stakeholder goal to a particular element in the corporate value model based on information elicited from management;

A set of assumptions about load growth that drive the long-range plan;

A project funding decision based on a particular visualization of risk or a certain set of assumptions for predicted performance;

A wear model of equipment based on the experience of maintenance and engineering personnel who have been responsible for the equipment for decades;

A code restricts operating in certain regions for a particular piece of equipment;

A business process for making a decision on budgeting.

Potential opportunities for knowledge management to support asset management have been mentioned frequently throughout this report. The report emphasizes capturing the bases of the corporate value model, and this case is a good example. The best decision support software is often good at capturing the results of a process that lead to the development of a model, e.g., at what outage length threshold does the value of preventing an outage start to disproportionately drive customer satisfaction.

But when implementing the software, we are often left scrambling to write down the basis. The exact thought process is sometimes lost, and the customer survey that is the basis might be poorly referenced. For sure, we have not referenced the basis sufficiently to be notified if a new customer survey shows a change, e.g., as electricity use more and more serves digital equipment, or as a new corporate client with unique power quality requirements moves into a service territory.


5-24

We are often happy when a decision support tool provides a notes field for capturing such information. But, we then find it difficult to capture and disseminate that note field to others. Or we find that the note field is too small or does not accept hyperlinks. We need only reach back and think through how we as asset managers do our work for different types of problems to write the requirements for knowledge management.

To conclude, the following steps should be considered when building incorporating knowledge management technology into asset management processes:

Start by understanding what basis information you have for a critical and representative set of models. The criteria for project funding or the basis for selecting key performance indicators (hopefully a corporate value model) are both excellent places to start.

Revisit the information and test it for completeness of assumptions and basis. Identify organizing principals upon which to improve it and do so. Develop lessons learned for capturing the basis of the next model developed for asset management.

Seek out the IT group. Participate in or have asset management be the focus of a knowledge management pilot. Use the pilot as an opportunity to improve an important asset management model that you know is reasonable, and yet its basis is poorly documented. Use your previously generated lessons learned and update them.

Begin to use formal knowledge management techniques every time expert opinion is elicited and every time a model or critical assumption is updated.

Apply the same approach described above to the asset management business process model.

Develop a guideline for when to use knowledge capture techniques during routine asset management work. Develop another guideline for managing the captured knowledge and include a process for notification when updates to bases are needed.

With this or a similar approach and the supporting technology that the IT group provides, the results of asset management decision support tools will be more robust and easier to explain. Also, the process of explaining them will not be as manually intensive as it had been.

Advanced Concepts in Data and Decision Support Tools

In addition to the advancements possible in the information technology infrastructure, important improvements are possible in data and decision support tools for the Processing and Continuously Improving company.

On the data side of the ledger, good cost information has to date been largely out of reach. Substantial organization change is required to obtain substantial improvements in cost data, but management support, other organizational factors, and good data access technology make improvement realistically attainable.

Advances in decision support tools are also possible, particularly in long-range planning and risk management of investments. Truly mature companies can also make progress by combining simulation and equipment degradation models with observable condition data to create near-real-time, quantitative models of assets and systems. Such models can be applied to business critical


5-25

risks of an operational nature or other critical risks that merit an increasing level of analysis accuracy.

Processing and Continuously Improving companies are sufficiently mature organizationally to incorporate the type of information into their business processes and to automate them using the advanced information technology infrastructure described above.

Accurate and Useful Cost Data

Cost information often is the biggest challenge for data standardization. Activity Based Costing (ABC) methods, if used, are often incomplete or not at the level of activities that are of interest to asset managers. The EAM and ERP systems are often the best sources of cost information since maintenance is often nearly half of the non-capital budget related to assets.

However, often only maintenance workers charge to work orders. Other costs which are not charged, e.g., contract workers and employees such as engineers and managers, cannot be evenly apportioned to maintenance worker charges. For example, a corrective maintenance activity may require much more planning and management involvement than a preventative maintenance activity. A contracted refurbishment is often much more costly than the associated employee labor. Because of these variations in cost, use of uniform overheads and indirect charges can easily mislead asset managers regarding the opportunities to create value.

As a result, the initial challenge in developing a cost standard is to decide what charging policies will be used and enforced within the company. If all work is not charged, then analysis will have to be done to estimate appropriate costs as they relate to an activity or to maintenance labor charges for an activity.

Other considerations for collecting cost data include:

Cost information in different systems may also have to be rectified based on type of accounting, namely depreciated costs or operating costs.

Cost information should be collected and stored so that it can be related to the general ledger. A standard with a data map and hierarchy will help.

Costs should be categorized so that they can be compared to projects, activities, and assets. Similarly, they should be categorized so that they can be evaluated from a life-cycle perspective.

Improving on the limitations in cost data will require a concerted effort. If the interest is primarily from the asset management group, asset managers will have to identify and develop workarounds. Asset managers should perform analyses to understand patterns in variations in costs that are not detailed, including indirects, against investments and cost drivers that are the leading focus of the asset management program.

If the interest is broader, then more fundamental change can be considered. Besides implementation of an ABC approach, the Processing company should consider changes in employee time charging and contractor cost reporting. Because of the costs involved in such a


5-26

change, management support and interest in using the cost information for business improvement will have to be gauged. Accurate and useful cost information can be used with benchmarking and process improvement techniques, independent of asset management, to drive profitability improvements. Management interest in these types of business improvements is one of the indications that a company is at the Processing or Continuously Improving level of maturity.

To conclude, the following steps should be considered when building the capability to obtain accurate and useful cost data:

Start by understanding what cost information you do have. Identify the sources of cost information within the company. Much of this information should be available from the Data Strategy described in the previous chapter.

Focus on one system and try to drill down on the costs attributable to individual assets, recent and ongoing project investments, and activities. For assets, use the asset registry. For project investments, use the list of capital and O&M projects in the corresponding asset management decision support tool. For activities, use a process model such as that produced by the nuclear industry [10].

Document the lessons learned as to limitations in the data obtained. In particular, consider the issues described above.

Perform analysis to understand how variations in costs can be properly attributed to the types of investments and cost drivers asset management is evaluating.

Work with business analysts to determine the feasibility of improving cost data collection. Engage management. If the effort moves forward, asset managers should participate so that the process changes related to cost data meet the needs of asset management decision support capabilities.

Advanced Decision Support Tools

The Processing and Continuously Improving company has had enough experience with decision support tools that they can consider new types of approaches. The most important of the developments discussed here is improving the capability of long-range planning. Usually this process starts with the most important assets. In fact, advanced decision support tools are initially practical only for critical business risks. Such tools are complex, but the lessons learned from previous successes with decision support tools allow the implementation of complex tools to remain within reason.

Use of simulation capabilities and developing real-time failure rates are truly advanced applications. The examples envisioned below require excellent data integration. Other, more limited applications can also be envisioned, but the quality of the result produced by the tool is then reduced.

The following includes a substantial chapter on risk management. Risk management techniques, like asset management techniques, can be qualitative as well as quantitative. Quantitative risk management techniques for the asset manager, like long-range planning, can result in a substantial improvement in investment analysis capability.


5-27

Long-Range Planning

Long-Range Planning is a developing discipline (see [16]). A single repository of planning information extending out to the end of life of key assets exists at very few electric utilities.5 Existing EAM and ERP systems, as well as scheduling systems, often focus on work and projects within a near-term time horizon. Hence, there are few commercial products that support long-range planning for the electric utility industry. Consequently, in the near term, LRP decision support tools may be developed in-house by IT or by engineers using spreadsheets and simple scheduling tools. Such tools will involve a significant amount of user action, in particular for iterations required to balance needs and constraints.

The following are key capabilities of Long-range Planning Tools:

produce output in a form compatible with needs of key stakeholders, including regulators (e.g., capital and O&M budgets for rate cases and environmental impacts), customers (e.g., reliability and power quality), and investors (e.g., revenue, net present value, and investment risk).

organize information by major asset types, e.g., transformers, by location, e.g., operating regions, and by time, e.g., budget years

allow for high level representations for management as well as more detailed representatives for asset owners, regional managers, etc.

forecast needs (or accept forecasts from other tools):

– load

– asset condition, e.g., failure rates based on equipment models6 and degradation factors as current measured asset condition

– end-of-life (EOL) criteria for major assets, based on asset condition and economic criteria

– costs of maintenance and replacement

– work force needs for critical staff

– critical resources, especially resources with high demand in a limited time period

– key performance indicators (and correspondingly the needs of key stakeholders)

develop project proposals and alternatives according to a standard template consistent with other tools (see below)

using forecasted needs and available resource constraints, translate options for replacement and life extension into a schedule of projects and activities and associated capital and O&M budgets and key performance indicators

5 The planning horizon for many regulatory agencies was three to five years. Planning for assets has often been done asset type by asset type rather than in an integrated manner. Most importantly, in the past asset replacement could be accomplished through new capacity additions. Today, the large inventory of aging assets as well as lower load growth has created the need for asset replacement to be a fundamental part of the planning function.

6 The concept of equipment models are explained under the topic of Data and Information Standards.


5-28

To conclude, the following steps should be considered when building or acquiring and when implementing capability for long-range planning:

Remember that just as project prioritization and investment is central to decision support activities in asset management programs at the Organizing level or below, long-range planning is central to Processing and Continuously Improving programs. Be sure that asset management concepts are included in the methods used and obtain some level of ownership in this activity for the asset management group. Also be sure that management support exists for process improvement.

Develop a good understanding of the strategic planning and long-range planning processes (explicit and implicit) before focusing on any technology decisions.

Decide where improvements in the process are desirable. Be sure to focus improvements on how investment ideas flow through the entire process.

Focus first on the most significant assets based on either total asset value or risk of business loss. For example, start with the top twenty most significant assets, develop life-cycle plans for each, integrate them and examine impacts on the planned capital and O&M budgets.

Develop a plan for increasing the quality of the process.

Educate project sponsors and business analysts on the importance of a good long-range planning process.

Select technology for assisting in the long-range planning process. Choose simple technology until you are sure of the planning capabilities you want to emphasize. The criteria above are a good guide.

Build a documentation process around to tool to ensure that the basis for the input, models, and decisions is clear and reproducible during the next budget cycle.

Use of Simulation in Asset Management

Simulation capability can have a special place in advanced asset management. Because a simulation model is in effect a predictive model, it can be used to project future performance under a variety of strategic plans and investment strategies. Typically, simulation models also support “systems-level thinking”, meaning that prediction can occur at a level that is consistent with many of the most important performance indicators, e.g., delivery system or power plant reliability.

When simulation is combined with observed condition information, two types of benefits result. First, when simulation is used as a planning tool, it is often used with average data. Using a simulation with observed condition data allows the planner to see a better characterization of future performance and whether or not it meets planning objectives.

Second, simulation combined with observed condition information can be used as an operations risk management aid. A range of deterministic outcomes can be predicted. Those outcomes can also be combined with probabilistic outcomes such as are discussed in the subsection below on risk management to provide a very robust operations risk modeling tool. Such a tool can then be used to provide added defense against low probability, high consequence events.


5-29

To conclude, the following steps should be considered when simulation capabilities are considered for use in asset management:

Start by understanding what high risk operational or planning situations might benefit from a simulation model combined with observed condition data.

Determine the ability to integrate the simulation model with observed data. If the integration of data cannot be automated without significant difficulty, approach such a use of simulation cautiously.

If the simulation modeling capability can be integrated through an integration standard or through robust and available data, discuss with planners and risk managers the benefits of providing the type of information a predictive model can provide.

Develop a plan for using simulation capability and implement it if benefits to planners and risk managers justify it.

Opportunities for Estimating Failure Rates in Real Time

Many maintenance management strategies rely upon an implicit and unstated probability of equipment failure. Some strategies might go so far as to rely explicitly on generic data. What is gaining in popularity is the concept of performing maintenance according to equipment “health”. The “health” of equipment can be inferred from the condition of its various components. The current state of the art is to qualitatively monitor health, usually with a stop light or alarm panel red/yellow/green approach. The basis for health is often predictive and diagnostic techniques, inspections and sometimes real-time data. Despite these recent improvements in maintenance planning, only rarely is maintenance planned and executed with a clear understanding of risk of equipment failure.

Recent EPRI research addresses the possibility of determining the probability of equipment failure based on equipment condition as measured by specific real-time or “near-real-time” methods. By producing such probabilities, we can use them to perform near-real-time quantitative equipment health assessments. We can extend this philosophy to groups of systems by building system health from equipment health and integrating up to the next level, i.e. by using systems-level thinking. Conceptually, this philosophy can extend to the generating station switchyard or substation or even the grid. Such “system-level” health assessments can be used to proactively affect not only maintenance, but also operations and contingency planning.

The philosophy used to generate a near-real-time failure rate begins with monitoring the equipment parameters currently available through an IED or SCADA. Field inspections and diagnostic test results from predictive maintenance programs will improve accuracy and complete missing information. EPRI equipment applications use algorithms to simulate component wear and pass that information on to a “cause-and-effect” model of how equipment performs. This cause-and-effect model is contained in the EPRI PM Basis (see the discussion on equipment models in Chapter 3). PM Basis can take component wear information (from duty cycles and environmental conditions and other algorithms from the repository of services described elsewhere in this chapter) and generate a failure rate. The ability to generate a failure rate and the validation exercises that have been done to date can be found in EPRI TR-1009633, Nuclear Asset Management Database. Since the component wear information can be monitored


5-30

(or inferred) in real-time or near real-time, “connecting” the PM Basis with an equipment health monitoring program can also generate a real-time or near-real-time failure rate.

To conclude, the following steps should be considered when building or acquiring capability to support estimating real-time failure rates:

It is important that asset management applications have as a foundation an equipment model such as the one described in Chapter 3. The ability to link degradation mechanisms to condition monitoring is critical to calculating failure rates.

A repository of services of asset algorithms is required for practical application of this method.

A re-usable analytics approach will need to be implemented to perform the calculations for a large number of components at a reasonable cost.

A model that can turn failure rates into triggers for action is important for implementation. A risk management model for operations such as the one described below will allow the greatest possible application of this capability.

Use of Risk Management

One of the more sophisticated asset management techniques is the risk management decision support technique. This level of sophistication in turn implies the need for management understanding and support. While simple approaches to risk management are expected to be undertaken at lesser maturity levels, this report anticipates the more sophisticated risk management techniques to be implemented only by the most mature asset management programs.

Risk is defined as a combination of the probability of an event and the consequence of an outcome of that event; both higher probability and larger consequence yield more risk. Risk management is the process of managing the probability or the consequence to manage the risk. Management may involve reducing the probability or consequence to reduce the risk if the consequence is bad. An example of a bad consequence would be a system failure resulting in loss of power to a customer. Risk management in this case often focuses on asset operation, including “allowable” maintenance. Such a risk management approach is often used in addition to, rather than in replacement of, a traditional operations strategy, e.g., N-1 contingency analysis.

Risk management for the asset manager involves selecting the level of acceptable risk for stakeholders and ensuring the corresponding maximum return for that level of risk. Here risk management techniques are used to support the asset manager in deciding on the appropriate investment in assets, including both capital and O&M. While the primary role of the asset manager is to increase returns and create value, a secondary role is to protect against unexpected events and avoid large losses.

Successful risk management depends on accurate models of stakeholder value and accurate data that supports the characteristic needs of the asset manager. Often companies do not have good models of what they value, and the process for creating value is ad hoc, often depending on the best presenter, most persistent advocate, or “management override”. Additionally, application of


5-31

risk management techniques often suffers from poor asset data, whether that data be reliability/performance data or cost data. Such data is often not collected and when collected, done so with little consistency.

This following elaborates on the two applications and then describes an example for risk management use by an asset manager. Following the example, a synopsis of recommendations is presented for the use of risk management decision support tools.

Risk Management for the Asset Manager

Risk management for the asset manager involves developing an understanding of uncertainty of the outcome of an investment. Probability in this case is the likelihood of a certain value being achieved. Consequence is the value returned from the investment. The risk manager characterizes the uncertainty, understands if further investigation can cost effectively reduce the uncertainty, and then decides whether to make the investment based of whether the return justifies the risk.

The process is very similar to how we as individuals decide to invest in stocks and bonds, namely we establish a risk level we can tolerate and then evaluate the investment returns achievable among the available choices at that risk level. Carrying the investment analogy one step further, the asset manager also selects a portfolio of investments to maximize return, diversified so that the common sources of risk, e.g., fuel price, do not affect all investment options.

Below, the report provides an example that illustrates the usefulness of risk management decision support capability as part of the asset manager’s toolkit.

Risk Management for Asset Operator

The asset operators typically use administrative requirements to control the risk of operating their system. An administrative requirement would be one that would ensure that system operation succeeds even if a transmission line or major generating station went out of service. As operations have become more complex, expectations of reliability have increased along with desires for reduced O&M costs, so operators are giving more consideration to using risk management in operations.

The first applications of risk management in operations occurred in the nuclear industry, but the principals apply to power delivery. Risk management in this case involves identifying a spectrum of scenarios, including N-2 scenarios, that can cause system failure. Then probabilities of failure are applied to contingencies and the most likely risks are identified. Such a risk management approach helps the operator know which portions of the system that are out of service are most important to return to service and which portions of the system are most important to defend. It also helps the operator (or planner) identify common contributors to N-2 scenarios, such as a tree-trimming program, that could affect multiple contingencies. This type of management becomes particularly important when the risk management system indicates that risk levels of system failure have increased, even when all administrative controls are met.


5-32

Information Technology Requirements for Risk Management

The information technology requirements vary depending on the nature of the risk management application. Risk management use by the asset manager can often be done with the asset management information technology infrastructure corresponding to the higher levels of maturity. The principal increase in technology revolves around the ability to identify and measure variabilities in performance, which in turn can help characterize risk. Often those variabilities are obtained from industry-wide data as the amount of experience in any one electric utility does not sufficiently characterize risk. Regardless, these types of capabilities are not particularly demanding on information technology systems.

However, when risk management moves into operations, information technology challenges increase, primarily that changes associate with a greater need for understanding current operating conditions, including what equipment is in service and what equipment is out of service. The need for current equipment condition information (and its implied risk of failure) also increases (see previous subsection on real-time failure rates). But the greatest influence on risk is most often what equipment is being taken out of service or is already out of service.

The need for this information comes for the most part from maintenance planning and operator logs. These information system integrations must be live for operator logs. Live information means that logs must not be solely in the form of text, but instead must be in the form of asset inventory data. Accomplishing this change for logs is difficult, but historically it has been accomplished in the nuclear industry, and commercial operator log products are available to meet this need.

For maintenance planning, risk can be evaluated in advance of maintenance activity and incorporated into the planning process. Risk management in maintenance planning means equipment is not removed for maintenance if it causes high risk and maintenance plans are adjusted in advance to prevent these conditions. Maintenance schedules can be downloaded periodically, evaluated for risks, and adjusted to mitigate them. When the time comes for maintenance, operations reviews the current conditions to ensure the risk is still acceptable. The information technology requirements for operator logs are all that is required in this case.

To conclude, the following steps should be considered when building or acquiring capability to support risk management:

Start by understanding what stakeholders and management think are the principal risks that the company faces.

Consider how those risks can be addressed by the existing asset management infrastructure with the addition of risk management capabilities and tools. That is, see what can be done exploiting existing asset management data sources and models.

Determine if operational risks are an important concern for any key company assets. If so, examine what level of benefit might result from an active risk management program. If the company has a nuclear risk management program, consider adapting the business processes and the technology to other risk problems that need to be addressed.


5-33

For risk management for the asset manager, determine management interest in assessing internal investment risks associated with capital projects.

If management interest exists, pursue risk management strategies such as are described in the illustrative example below.

Illustrative Example for Risk Management

The EPRI program on risk management is built upon the strengths of all our business sectors and exploits data and models for application to many decision makers, including operators and asset managers. Our decision tools support key decisions such as capital project prioritization and budgeting, fleet asset repair and replacement, and systems operation.

This chapter illustrates portfolio evaluation and risk assessment in an asset management context via an example that is of interest to both distribution and transmission system owners: tree trimming. In this example, the decision posed is whether to trim trees this year, or wait until a future year. A preliminary analysis without considering risk determined that the total cost of not trimming the trees (considering outage costs, etc.) was $10,425, and that the total cost of trimming was $13,543, indicating that not trimming is the lower cost option. These results, called “expected values,” flow from a purely deterministic analysis (i.e., an analysis that does not consider uncertainty or probability).7 However, considering risks could lead to a different conclusion.

A brief review of the principles of risk assessment provides useful background information for another way of looking at this example. Risk means uncertainty, and good decision-making requires consideration of risk. When considering risk, a “good decision” does not necessarily guarantee a good outcome. However, consideration of risk does increase the likelihood that the outcome will be a good one.

The key steps in assessing risk include the following:

Pose the decision

Identify influencing factors

Identify sources of risk

Perform sensitivity analyses

Use judgment in decision making

Use historical data

Apply an analytical model

Evaluate portfolio risk

7 This section is based on an EPRI presentation by Jeremy Bloom [9]


5-34

Influencing Factors and Risk Sources

It is essential in risk analysis to precisely pose the decision problem. Otherwise, ambiguities in the problem definition will obscure the uncertainties that create the risks.

To pose the decision in this example, the issue is whether to trim trees along a substation feeder this year or wait until later to reconsider trimming. Two situations need to be examined: 1) ordinary (isolated) faults occur on the feeder, and 2) a major storm results in multiple faults on the feeder. Factors that influence the decision include the following:

The fault rate under ordinary and storm conditions

The decrease in fault rate when trees are trimmed

The length of time required for service restoration after a fault under ordinary and storm conditions

The number and type of customers affected

The cost of customer outages

The utility’s cost of service restoration

The tree trimming cost

Others

To clarify how these factors influence the decision, it is helpful to construct a conceptual model (see Figure 5-12) that represents their interrelationships. Note that the top five elements lead directly to cost. Also, note that the right side of the diagram addresses ordinary conditions, while the left side of the diagram addresses storm conditions.

The next step in the process involves identifying the sources of risk or uncertainty in the decision, which can include the following:

The number of interruptions due to ordinary and storm conditions per year

The duration of interruptions due to ordinary and storm conditions

The storm probability

The customer outage cost


5-35

Cost

# Customers interrupted (ordinary)

# Faults (ordinary)

Restoration time

(ordinary)

# Customers interrupted (ordinary)

# Faults (ordinary)

Restoration time

(ordinary)

Customeroutage

cost (storm)

Utilityrestoration

cost (storm)

Customeroutage

cost (ordinary)

Utilityrestoration

cost (ordinary)

Tree Trimming

Cost

Customeroutage

cost (storm)

Utilityrestoration

cost (storm)

Customeroutage

cost (ordinary)

Utilityrestoration

cost (ordinary)

Tree Trimming

Cost

Tree Trimming

Tree Trimming

# Customers interrupted

(storm)

# Faults (storm)

Restoration time

(storm)

# Customers interrupted

(storm)

# Faults (storm)

Restoration time

(storm)

Figure 5-12 Influence Diagram for Tree Trimming Example

Sensitivity Analysis

In the next step in the process, the factors that have the most influence on the results are identified. This is done by quantifying the influence diagram (Figure 5-12), then varying each parameter (e.g., tree trimming cost, customer outage cost, storm probability, etc.) independently. The same percentage change is applied to each parameter in each sensitivity run. The funnel shaped set of results shown in Figure 5-13 is called a “tornado diagram.” The diagram shows, for example, that for a given percentage change in a parameter (e.g., the tree trimming cost), the impact on study results was an increase or decrease of $2500 (“delta cost”).

This sensitivity analysis does not consider probability or risk; its results pertain to the deterministic results indicated at the beginning of this chapter. Hence, variation in the tree trimming cost can vary the cost of trimming from about $11,000 to $16,000.


5-36

Figure 5-13 Tornado Diagram Displaying the Sensitivity Analysis for Tree Trimming Example

Judgment and Historical Data

Probability is the means of measuring risk. Because assigning probabilities can be quite difficult, judgment can be an important part of risk assessment. Judgment is a perfectly legitimate way to assess risks because people often act on their judgments. Furthermore, when risk factors are subjective, hard to measure, or hard to model, or if limited data is available, judgment may be the only reasonable way to assess risks. For example, customer outage costs are often determined based on judgment because available outage cost data are inconsistent among various studies; the cost varies by customer sector, customer end-uses, the advance warning time of the outage (if any), and many other factors. In many cases, customers do not even know what an outage costs them. Ultimately, assigning customer outage costs is a judgment utility management needs to make because they have to trade-off costs customers incur against costs the utility incurs.

Historical data can also be used to assess uncertainties. For example, the overall duration of outages is captured by various measures such as SAIDI (System Average Interruption Duration Index). A measure of customer reliability, this index indicates the system average number of minutes each year that customers’ power is interrupted (excluding major events). Figure 5-14 illustrates this type of historical data for a 10-year period for 65 utilities in the United States [15]. Notice the range of variation both within a given year and also from year to year. This variation indicates the uncertainty inherent in the occurrence of outages.

-$3,000 -$2,000 -$1,000 $0 $1,000 $2,000 $3,000

Fault Rate (Storm)

Restoration Rate (Storm)

Storm Probability

Restoration Rate (Ordinary)

Fault Rate (Ordinary)

Customer Cost (Ordinary) $/hr

Trimming Cost $/mi

Tornado Diagram - Trimming

Delta Cost


5-37

0

100

200

300

400

500

600

700

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001Year

SA

IDI

(With

ou

t Maj

or

Eve

M ean (107 m inutes)

Figure 5-14 Ten-Year SAIDI Scatter Plot Illustrates Useful Historical Data for Risk Assessment

Analysis

In addition to judgment and historical data, analytical risk models are very useful in risk assessment because the uncertainties involved are often quite difficult to gauge. Figure 5-15 illustrates the results derived from such a model, applied to one particular feeder. The diagram shows the expected values from the deterministic analysis as the two dotted vertical lines – the total cost of trimming on the right, and the total cost of not trimming on the left. The two curves represent the plot of the analytical risk model for the two scenarios. While the expected cost of not trimming is lower than that of trimming (the two vertical lines), the range of possible costs for the trimming case is much narrower than the no-trim case; that is, trimming has much less risk (i.e., uncertainty) than not trimming. Furthermore, the two curves cross at the 75 percent probability point. According to this analytical model, there is a one in four chance (from 75-100 percent) that not trimming will cost more than trimming. Whether a utility’s management considers such a risk to be prudent is a matter for their judgment.


5-38

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

$0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000

Total Cost

Prob

abili

ty C

ost <

X

No Trimming

Trimming

No Trimming

Trimming

Figure 5-15 An Analytical Risk Model (Curves) Shows the Results of a Probabilistic Analysis, Compared with the Deterministic Expected Values (Vertical Lines)

In addition to the risk assessment for one project (i.e., a particular feeder), risk assessment techniques can be applied to examine a portfolio (i.e., multiple feeders). For example, if ten feeders are being considered for tree trimming, but the budget only allows for tree trimming to be performed on five feeders, then various portfolios can be defined, each consisting of a different combination of five feeders. For each portfolio, the expected total cost and risk (measured by the standard deviation of this cost) can be calculated. Figure 5-16 illustrates a plot on which each data point represents a portfolio. The line connecting the lower-most data points, for a given expected total cost, is called the “efficient frontier.” For a given expected total cost, this line represents the “best” portfolio because it yields the smallest risk (standard deviation) in the total cost. This means that any portfolio above the line would not be selected, because a portfolio with a lower standard deviation at that cost is a better option. Note that the shape of the efficient frontier reflects the fact that the various risk factors involved in this problem are not independent.

The decision then becomes which portfolio on the efficient frontier to select. The portfolios at the right end of the diagram reflect the lower standard deviation, and hence less risk, albeit at a higher cost. Highly risk averse managers would tend to select a portfolio at this end of the frontier. Conversely, the portfolios at the left end of the diagram reflect the higher standard deviation, and hence more risk, albeit at a lower cost. Highly risk tolerant managers would tend to select a portfolio at this end of the frontier.


5-39

$0$2,000

$4,000$6,000

$8,000$10,000$12,000

$14,000$16,000

$18,000$20,000

$104,000 $106,000 $108,000 $110,000 $112,000 $114,000 $116,000 $118,000 $120,000

Expected Total Cost

Sta

ndar

d D

evia

tion

Tota

l Cos

t

Efficient FrontierEfficient Frontier

Figure 5-16 Portfolio Risk Trade-Off (Efficient Frontier)

Note: Each Data Point Reflects a Different Portfolio of Projects. Managers Select a Portfolio on the Efficient Frontier Based on Their Risk Tolerance.

Conclusion

This example illustrates use of a rational basis for decision-making that incorporates risk. It shows that decisions made with consideration of risk are more likely to result in desired outcomes, compared to decisions that are made purely based on expected values. In addition to risk management, this example also illustrates use of portfolio evaluation to determine the combination of projects that are most likely to lead to desired outcomes.

6-1

6 CONCLUSIONS AND RECOMMENDATIONS

The report discusses critical topics for the development of an information technology infrastructure to support utility asset management at the enterprise level, which are equally applicable to major business units of a utility such as its power delivery unit. Because the IT needs and capabilities of an enterprise depend strongly on the state of development of its asset management program, the report begins with a self-assessment method to help either an IT professional or an asset manager determine the current maturity level of their program. The method classifies AM programs in five different levels of maturity and seven asset management program attributes with six sub-attributes to create a table of over 130 criteria for measuring development of an asset management program.

The report then discusses over twenty critical topics for asset management information technology infrastructure grouped by maturity level into three sections. Many of the topics contain either a wide spectrum of examples or a single in-depth example to illustrate the point. Each topic also contains a set of recommended steps for improving the asset management information technology infrastructure and program which, when combined with the criteria, allow the reader to develop a roadmap for program improvement based on the results of the self-assessment.

The report draws upon the work of EPRI program managers for asset management in both the power delivery and nuclear sectors. It also takes advantage of the experience and work done by the rather large Nuclear Asset Management community of practice in nuclear power as well as the significant number of funding participants in the power delivery asset management program. Because of this breadth and depth of participation and experience, this report is believed to substantially represent the corresponding breadth and depth of asset management issues in an enterprise program.

Finally, the report gives highlights important current advances in information technology, especially related to the topic of Service Oriented Architecture, which seems particularly amenable to the advanced forms of enterprise asset management. The importance of this evolving information technology discipline is demonstrated by the substantial investment made by vendors of Enterprise Asset Management and Enterprise Resource Planning systems. The report shows how EPRI’s asset management technology can facilitate such an architecture and how ongoing EPRI research can lead to even further possibilities for advancement in asset management.

Despite the significant amount of information provided in this report, there is still further work to do. Although comprehensive in its nature, this report falls short of a true information technology guidance document. Further enhancement and the addition of specific roadmaps or plans for development could yield a guidance document of that kind.

Conclusions and Recommendations

6-2

A significant research opportunity also exists in developing enabling capabilities for asset management implementation within a Service Oriented Architecture. Describing and developing EPRI research content on such topics as equipment condition assessment, maintenance, and decision support in the form of services would enable much wider use of asset management automation and could facilitate adoption of EPRI’s technology advances by utilities. Taking the next step to incorporate EPRI equipment failure models into potential information standards would help ensure that early investments in asset management capabilities could be continually expanded as services become more sophisticated. And perhaps the most critical step for asset management automation would be continuing the development and testing of re-usable analytics, a technique that offers the potential of significant productivity gains by freeing asset management application implementation from the repetitive expense of engineering resources.

In the area of purely decision support capability improvement, it would be useful to test the concept of a corporate value model sufficient for enterprise application. Similar testing could be done of risk management techniques and protocols for use at the enterprise level. Finally, it would be useful to pilot the use of knowledge management techniques for the specific purpose of capturing and explaining the basis for asset management models and thereby learning if indeed meaningful improvements could be made in this area.

Much has been accomplished in asset management in the last ten years in terms of improved equipment reliability, reductions in cost, and increases in business process productivity. Advances have been made across the board in data, decision support, and results visualization. Asset management lessons learned abound in the area of information integration and application. The collective experience of the industry is substantial, and as the report’s self-assessment method asserts, such experience lays the foundation for raising the bar for asset management to a new and even more effective level.

7-1

7 REFERENCES

1. Asset Management Primer, US Department of Transportation; Federal Highway Administration’s Office of Asset Management, NCHRP Project 20-24(11), 2001.

2. Carnegie Mellon Software Engineering Institute, Capability Maturity Model (SW-CMM) for Software, summary description from the URL http://www.sei.cmu.edu/activities/cmm/cmm.sum.html, March 2000.

3. Watts, Humphrey, Characterizing the Software Process: A Maturity Framework, Technical Report CMU/SEI-87-TR-11, Carnegie Mellon University Software Engineering Institute (1987).

4. Mark C. Paulk, ed., The Capability Maturity Model: Guidelines for Improving the Software Process, Carnegie Mellon University Software Engineering Institute (1994).

5. Crosby, Phillip B., Quality is Free: The Art of Making Quality Certain, McGraw-Hill (1979).

6. Guidelines for Power Delivery Asset Management: A Business Model for Program Implementation – Expanded Version, EPRI, Palo Alto, CA: 2005, 1010728.

7. Common Information Model (CIM): CIM 10 Version, EPRI, Palo Alto, CA: 2001. 1001976.

8. The Standard Nuclear Performance Model, A Process Management Approach, Revision 4, Nuclear Energy Institute, Nuclear Asset Management Community of Practice, 2004.

9. Nuclear Asset Management Process Description and Guideline, Nuclear Energy Institute, NEI AP-940, 2005.

10. Equipment Reliability Process Description, Institute of Nuclear Power Operations Report AP-913, Revision 1, 2001.

11. Cedric Tyler and Steve Baker, Business Modeling using xBML™, BusinessGenetics, 2004.

12. Asset Management Toolkit Modules: An Approach for Risk-Informed Performance-Focused Asset Management in the Power Delivery Industry, EPRI, Palo Alto, CA: 2005. 1011365.

13. Re-Usable Analytical Platform for Asset and Risk Management Applications. EPRI, Palo Alto, CA: 2005. 1010548.

14. Distribution Applications of the Asset and Risk Management (ARM) Workstation. EPRI, Palo Alto, CA: 2004. 1008565

http://www.sei.cmu.edu/activities/cmm/cmm.sum.html

References

7-2

15. Distribution Reliability Indices Tracking Within the United States. EPRI, Palo Alto, CA. 2003. 1008459.

16. Guidelines for Power Delivery Asset Management Long-Range and Strategic Planning: EPRI, Palo Alto, CA: 2006. 1012496.

A-1

A SURVEY OF EXISTING ASSET MANAGEMENT CAPABILITY

List of Asset Management Capabilities

The tools described in the following EPRI reports can be applied now to practice asset management:

“Equipment Failure Model and Data for Underground Distribution Cables: A PM Basis Application,” Report 1008560 – December 2004 [P3 and P4]

“Equipment Failure Model and Data for Wood Utility Poles,” Report 1008561 – December 2004 [P3 and P4]

“Distribution Applications of the Asset and Risk Management (ARM) Workstation,” Report 1008565 – December 2004 [P3, 4 and 8]

“Guidelines for Intelligent Asset Replacement: Volume 2 - Wood Poles,” Report 1002087 – December 2004 [P 8 and 9]

“Living RCM: First Steps at Integration of Transformer IM&D Algorithms and MMW”, Report 1002126 - March 2004 [P 3 and P4]

“Utility Vegetation Management: Use of Reliability-Centered Maintenance Concepts to Improve Performance,” Report 1008859 April 2004 [P8]

The tools and methodologies described in the following EPRI reports can be used to help develop a PDAM implementation:

“Asset Management Toolkit Modules: An Approach for Risk-Informed Performance-Focused Asset Management in the Power Delivery Industry,” Report 1011365 – June 2005

“Asset Performance Database: A Recommended Approach for Data Modeling to Facilitate Power Delivery Asset Management,” Report 1008553 – May 2005

“Common Information Model for Transmission and Distribution Maintenance Data,” Report 1008557 – December 2004

“Guidelines for Power Delivery Asset Management: A Business Model for Program Implementations,” Report 1008550 – November 2004

“Wood Pole Management — Issue Assessment,” Report 1002093 – December 2004

“Planning and Research Optimizer Version 3.0: Theory Manual,” Research Report 1011345 – December 2004

Survey of Existing Asset management Capability

A-2

“AMT - Transformer Aging Effects and Fleet Requirements: Power Transformer Fleet Management and Sustainability,” Report 1009785 March 2004

“Asset Performance Database: Industry Database Design for Transmission Cables and Components,” Report 1002133 – December 2003

“Guidelines for Intelligent Asset Replacement, Volume 1,” Report 1002086 – December 2003

“Reliability Centered Maintenance (RCM) Technical Reference for Power Delivery,” Report 1002125 – December 2003

“Specification for Enhanced Ranking Module in Maintenance Management Workstation,” Report 1002064 – December 2003

“Maintenance Ranking and Diagnostic Algorithms for Circuit Breakers Most Suitable for Field Installation,” Report 1002063 – December 2003

“Measuring and Valuing Reliability for Distribution Asset Management,” Report 1001707 – September 2003

“Project Prioritization System, Methodology Summary,” Report 1001877, December 2001

The concepts described in the following reports have value in developing a PDAM implementation but requires either up dating or modification to accommodate power delivery business aspects:

“Guidelines for Applying Risk Based Tools to Maintenance Decision Needs,” Report 1009842, November 2004.

“Assessing Nuclear Power Plant Risk Management Effectiveness,” Report 1008242 July 2004

“A Dynamical Systems Model for Nuclear Power Plant Risk Management,” Report 1007969 October 2003

“Transformer Expert System, XVisor: A Practical Statistical Evaluation of Transformer Failure Predictions,” Report 1001772 November 2002

“Risk Based Maintenance Guideline,” Report 1004382 November 2002

“Life-Cycle Decision Making,” Report 1007563 December 2002

“Turbine-Generator Maintenance Interval Optimization Using a Financial Risk Assessment Technique,” Report 1000820 November 2000

Program:

Enterprise Asset Management

1012527

Electric Power Research Institute3420 Hillview Avenue, Palo Alto, California 94304-1338 • PO Box 10412, Palo Alto, California 94303-0813 USA

800.313.3774 • 650.855.2121 • [email protected] • www.epri.com

The Electric Power Research Institute (EPRI), with major

locations in Palo Alto, California, and Charlotte, North Carolina, was

established in 1973 as an independent, nonprofit center for public

interest energy and environmental research. EPRI brings together

members, participants, the Institute’s scientists and engineers, and

other leading experts to work collaboratively on solutions to the

challenges of electric power. These solutions span nearly every area

of electricity generation, delivery, and use, including health, safety,

and environment. EPRI’s members represent over 90% of the electricity

generated in the United States. International participation represents

nearly 15% of EPRI’s total research, development, and demonstration

program.

Together...Shaping the Future of Electricity

Export Control Restrictions

Access to and use of EPRI Intellectual Property is granted with the

specifi c understanding and requirement that responsibility for ensur-

ing full compliance with all applicable U.S. and foreign export laws

and regulations is being undertaken by you and your company. This

includes an obligation to ensure that any individual receiving access

hereunder who is not a U.S. citizen or permanent U.S. resident is

permitted access under applicable U.S. and foreign export laws and

regulations. In the event you are uncertain whether you or your com-

pany may lawfully obtain access to this EPRI Intellectual Property, you

acknowledge that it is your obligation to consult with your company’s

legal counsel to determine whether this access is lawful. Although

EPRI may make available on a case-by-case basis an informal as-

sessment of the applicable U.S. export classifi cation for specifi c EPRI

Intellectual Property, you and your company acknowledge that this

assessment is solely for informational purposes and not for reliance

purposes. You and your company acknowledge that it is still the ob-

ligation of you and your company to make your own assessment

of the applicable U.S. export classifi cation and ensure compliance

accordingly. You and your company understand and acknowledge

your obligations to make a prompt report to EPRI and the appropriate

authorities regarding any access to or use of EPRI Intellectual Prop-

erty hereunder that may be in violation of applicable U.S. or foreign

export laws or regulations.

© 2007 Electric Power Research Institute (EPRI), Inc. All rights reserved. Electric Power Research Institute, EPRI, and TOGETHER...SHAPING THE FUTURE OF ELECTRICITY are registered service marks of the Electric Power Research Institute, Inc.

Printed on recycled paper in the United States of America

Information Technology for Enterprise Asset Management: An ...

Documents

Transcript of Information Technology for Enterprise Asset Management: An ...