3_Boyd_Nick_MS_ISE_Lean_Six Sigma_Technical Documentation

| MS ISE Project Documentation

Application of Lean/Six-Sigma + Human Factors Principles To Power Distribution Network Optimization Efforts: Master of Science in Industrial Systems Engineering Thesis Project

Nick Boyd Dr. Carolyn Sommerich Dr. Jerald Brevick April 24th, 2013

| MS ISE Project Documentation 2

> Project Overview

Statement of Individual Completion for Included MS ISE Project Components • All elements related to the following presentation elements have been

completed individually by Nick Boyd for the purpose of fulfilling the MS ISE Degree Exam Requirements:

› I. Technical Documentation › II. Project Poster › III. Project PowerPoint › IV. Project Process Binder

• These items go above and beyond the requirements of the project elements created for the completion of the Fisher Lean/Six-Sigma Foundations and Projects Course.

• Figures, graphical element and text have been developed individually by Nick Boyd and exclusive of other project member’s efforts towards project completion.

Project Timeframe Start Date: 11/30/12 End Date: 03/15/13


> Project Overview

Acknowledgement to Project Contributors • The author of this body of work would like to acknowledge the input

from the following parties and their support in the completion of this MS ISE Project

• I. OSU Advisers and Faculty: › Dr. Jerald Brevick › Dr. Carolyn Sommerich › Prof. Peg Pennington › Prof. Terry Klinker

• II. AEP Senior Leadership and Champions: › Michael Childs › Gloria Feliciano › Omar Hellalat

• III. Fisher Lean/Six-Sigma Project Student Team: › Joseph Antonelli › Justin Dalton › Jeff McKernan › Madhu Nonavinakere-Muruli › Mike Hammersmith


> Project Overview

1. Define a. Identify Customer Needs b. Qualify and quantify the problem or

improvement requirements c. Incorporate Customer Perspective

2. Measure

a. Establish Performance Baselines b. Develop Measurement System c. Create method of Gauge/Trend Tracking

3. Analyze a. Quantify Customer Requirements b. Employ Statistical Analysis for the

identification of Process Inputs of significant effect 4. Improve

a. Identify key areas for Process Improvement

b. Link Analyze data with Customer Needs in quantifiable manner

c. Develop Method for Implementation 5 Control a. Establish Long-Term Measurement System

for trend tracking b. Implement “Proactive Approach” towards

issue mitigation and process improvement

DMAIC Roadmap / Presentation Outline


> Project Overview

• A large number of tools are available in the completion of a Lean/Six-Sigma project; those listed above are a mere sampling

• A specific tool can be useful in multiple phases and iterative use affords a strengthened problem-solving process

• Combination utilized is dependent on the nature of the problem being solved (product vs. process / quantitative vs. qualitative)

Overview of Lean/Six-Sigma Tools


I. Define Phase AEP-OSU Lean/Six-Sigma Project


> Define Phase

Project Charter


> Define Phase

Overview of Lean Six-Sigma Tools

• Provides a structured approached towards “division of labor” and ensures that the tasks are balanced

• Utilized to great effect throughout all parts of the DMAIC process • Structure for project organization and clear communication between all

parties


> Define Phase

Project Scope / “Problem Space”

• Project scope was largely determined by AEP – focus was to be made on the Distribution Network components

• The literal “Distributed” nature of the system affords a significant degree of complexity

• OSU group recognized that the nature of the system would offer considerable challenges and obstacles to overcome


> Define Phase

Distribution Substation > Circuit Breakers + > Transformers

• Distribution Substations contained the following elements: › I. Circuit Breakers › II. Transformers

• Consists of a myriad of components outside of just these two substation elements

• Varying degree of “Distributed Nature” between Substations (Circuit Breakers + Transformers) and Distribution Lines


> Define Phase

Visualizations Developed for “Multi-Level” Communication

• Further depiction of network hierarchy and inherent differences between each component

• Utilized for “multi-level” communication to technical/non-technical project stakeholders

• Diagrams such as the one above allow for a refined overview of the materials reviewed during multiple DMAIC stages

II. Circuit Breakers III. Transformers


> Define Phase

> Voice-Of-Customer (VOC) Hierarchy of Business Success Factors

1. Repair Process Cost Reduction

2. Faster Repair Process

3. Reduced Process Times

5. Increased Marketshare

4. Improve Customer Satisfaction

Primary Focus

Secondary objectives will follow successful implementation of primary drivers

Voice of Customer (VOC) Toolset

• Discussions with AEP representatives lead to the development of Business Success Factors

• Further refines the focus of the project and the “arena” in which improvements efforts will be prioritized

• Determination of Primary and Secondary focus areas


> Define Phase

Voice of Customer (VOC) Toolset

• Brainstorming and discussion sessions with AEP representatives lead to the identification of potential forms of waste within system

• Indicative of the ability for implementation of Lean principles to improve the overall process flow and efficiency


> Define Phase

Critical to Quality Characteristics (CTQCs) Tree

• An iterative tool utilized to further “drive down” the core characteristics that are indicative of a quality process

• Provides further refinement of the project focus to ensure that an appropriate scope is arrived upon

• Developed in conjunction with input from AEP stakeholders


> Define Phase

Affinity Diagram and Element Correlation

• Affords the ability to visualize connections between “major drivers” related to Network Reliability

• In addition an excellent “multi-level” communication tool


> Define Phase

Network Reliability + Project Scope Prioritizer

• Additional measure related to develop a highly refined vision of the project definition and focus

• Allows for balance between numerous important elements: › I. Perceived Importance › II. Cost of Implementation › III. Feasibility / Probability of Success › IV. Cost Reduction › V. Leverage / Positive Effect on Correlated Processes


> Define Phase

Project Aim Balanced Scorecard

• Correlates the following three components related to the Problem Scope – further defines feasibility and “health” of each option:

› I. Objective › II. Proposed Measure › III. Initiative for Progress


II. Measure Phase AEP-OSU Lean/Six-Sigma Project


> Measure Phase

AEP Operating Regions of Focus

• Datasets provided by AEP included all operating regions, the intent of the project is to gauge the status of the entirety of the company’s presence in all included states:

› I. Kentucky › II. Ohio › III. Texas › IV. Virginia › V. West Virginia


> Measure Phase

Sources of Data for Measurement + Analysis

• Two different types of databases were utilized for the recording of work order related to equipment

• Division of databases is correlated to the structure of the network, with Circuit Breakers and Transformers both within Substations

• The more distributed nature of Transmission Lines affords the need for a dedicated system due to increased complexity


> Measure Phase

ISIS and TORS Database Output Files

• Overall structure and organization where relatively the same on a macro-level and allowed for exploration of measurement approaches

• Details such as metric identifiers (Region, Manufacturer, Equipment ID) however were unique to each database


> Measure Phase

Fishbone Diagram: Measurement Planning

• The utilization of this visualization device allows for the depiction of those metric categories recorded in ISIS and TORS databases

• Provides a “roadmap” from which to develop an appropriate approach for measurement and subsequent analysis


> Measure Phase

Visualization of Metric Distribution

• The exploration of several metric measurement visualizations was vital to the development of an appropriate approach

• The identification of a highly skewed dataset was apparent through this process

• Even with efforts to eliminate these outliers, a highly non-normal distribution of data was present in terms of Cost, as well as other metrics such as Outage Duration


> Measure Phase

Metric Stratification + Fitted Line Plots

• This means of data visualization was helpful in identifying distributions inherent in both continuous (equipment age) and discrete (rated amps)

• Although no overall “trends” could be established, this is further indication of the non-normaility of the data

• Relations to the highly distributed nature of the network may be of considerable influence and warrant further study


> Measure Phase

Trend Tracking + Interval Plots

• The utilization of interval plots allow tracking over the period that data was available for analysis

• High degree of variability was present across metrics • Approach proved useful in the comparison of these metrics for possible

correlations present


> Measure Phase

Year of Occurrence + Region of Operation Distribution/Trends

• Elements such as Year of Occurrence and Region of Operation • A specific tool can be useful in several phases and iterative use affords a

strengthened problem-solving process • High values related to Standard Deviation across all categories holds

implications for measurement ability


> Measure Phase

Two-Dimensional Stratification

• Stratification of data across two metric categories provides further definition related to trends and possible measurement techniques

• For many datasets, there are only a handful of intervals worth of data present which makes overall appraisal of the metric’s appropriateness somewhat challenging


> Measure Phase

Industrial Standards (Outage Duration + Outage Rate)

• Industry-standard metrics were also considered for inclusion › I. Outage Duration

(Count of hours out of service / Count of Failures) › II. Outage Rate

(Count of Failures In Group / Total Population) • AEP expressed heightened interest in these metrics for incorporation in

the Measure and Analysis phases


> Measure Phase

Metric Median + Standard Deviation + Count Visualization

• The incorporation of the following elements where highly informative in the identification of “fitness” related to the quantitative and qualitative metrics recorded

› I. Mean › II. Standard Deviation › III. Count

• This specific method of visualization was utilized across numerous metrics to derive a consistent “baseline” for comparison of the various metrics within each database


III. Analyze Phase AEP-OSU Lean/Six-Sigma Project


> Analyze Phase

Overview of Approach Structure


> Analyze Phase

A. Data Validation/Screening Analysis (Source + System Reliability)

I. Circuit Breakers II. Transformers III. Transmission Lines

Details of Approach Structure

• High degree of incomplete and “invalid” data present within all three equipment categories – inherent of issues with the design of metric database


> Analyze Phase

B. Industrial Standards Analysis (Failure Duration + Failure Rate)

I. Circuit Breakers II. Transformers III. Transmission Lines


• Industry-standard proved promising for analysis › I. Outage Duration

(Count of hours out of service / Count of Failures) › II. Outage Rate

(Count of Failures In Group / Total Population) • Mild correlations were present between Outage Duration and Rate,

however varying relations present from regions • Extended interval of historical data could afford higher power


> Analyze Phase

C. Two-Dimensional Categorical Analysis (Quantitative + Qualitative)

I. Circuit Breakers II. Transformers III. Transmission Lnes


• Two-dimensional analysis allows for continued exploration of trends and correlation between metrics including

› I. Quantitative Metrics › II. Qualitative Metrics

• Allows for association between qualitative and quantitative metrics • Augmenting the time interval over which the data is depicted could

afford a higher degree of resolution in future efforts


I. Circuit Breakers

36

> Analyze Phase

D. Linear Regression Analysis



I. Circuit Breakers

37

> Analyze Phase




I. Circuit Breakers

38

> Analyze Phase




IV. Improve/Control Phase AEP-OSU Lean/Six-Sigma Project


> Improve/Control Phase

Brainstorming + Ideation Refinement

• Several rounds of brainstorming and ideation related to improvement efforts were conducted

• Inclusion and participation of AEP representatives allowed for an enhanced ability to include Customer Perspective and develop Quality-centric recommendations



Affinity Diagram for Improvement Drivers

• Improvement drivers were identified, with corresponding core area of incorporation identified:

› I. Accuracy + Completeness – Metric Databases › II. Additional Fields – Metric Databases › III. Clear Definition of Concepts - Operational Definition › IV. Data Consistency - Metric Databases › V. Reporting – Field Technicians / System Engineers



Finalized Set of Improvement Drivers

• Further resolution of specific “Improvement Efforts” that have the potential of improving the overall quality of the system and reducing potential for errors associated with:

› I. Accuracy + Completeness – Metric Databases › II. Additional Fields – Metric Databases › III. Clear Definition of Concepts - Operational Definition › IV. Data Consistency - Metric Databases › V. Reporting – Field Technicians / System Engineers



Workflow Improvement: System Design and Human Factors

• The most significant issue present in the current system is lack of accountability for fully completing Work Order entries

• Improved workflow with “feedback” affords an enhanced ability to close out each entry and enhance the statistical power of the collected data

• Establish means for effective measurement and analysis phases in future iterations



Refinement of Cause Code Classifications

• Enhanced outage cause coding system • “Meta-Categories” can be implemented in order to drive a higher degree

of analysis not just between individual categories but within and between these classifications



Cause Code System Improvement

• Continuation of the improved cause code system • A pool of pre-established causes are presented to the repair technician,

from which the most three relevant are selected • Eliminates the subjectivity present within the current system and manual

entry of cause • Moves beyond just one cause and can allow for advanced analysis

between multiple potential causes – trend tracking, predictive behavior within the network



Metric QR Code Implementation

• Utilization of QR technology off-loads the monotonous task of metric recording from technician

• Offloads the error-prone task of manual equipment metrics entry from the technician to an established automated system

• Affords the ability for the technician to focus on proper cause code determination

• Ability to utilize new entry system with “feedback” more effectively through the offloading of metric entry



Outage Cause + Persistence Tracking

• Current system only tracks one element, an improved system can afford the ability to develop models of the following:

› I. Outage Cause – Performance of the Distribution Network › II. Outage Persistence – Performance of the Repair Process



Proposal for Improved Measure/Analysis Process

• Through the completion of the Analysis Phase, the team iteratively revised the approach towards processing the data

• To sustain the process, this flow diagram was developed in order to establish a standardized means for completion of this task

• Aim is to improve efficiency of analysis and turnaround of results



Outage Visual Control: Frequency + Duration + Tracking

• Network visualization affords a tool for communication and tracking of overall system status and “flagging” of trends.

• Consolidated and visualized metrics related to a specific outage offer powerful communication and tracking tools to AEP

• High degree of utility in terms of Inter-departmental and Multi-level communication due to visual nature and scalable complexity

• Eventual incorporation into “Dashboard” program could provide further consistency in communication and network tracking


> Referenced Works

Amaro, Vincent A., Jr. Evolver - A Practitioner's Guide to Lean Manufacturing - 5S Edition. 2nd ed. San Juan Capistrano, California: Lean Manufacturing Consulting & Vincent A. Amaro Jr, 2006. Print.

American Electric Power Co., Inc. AEP Operating Regions Per State. Digital image. AEP - Rates and Tarrifs. American Electric Power Co., 2010. Web. <https://www.aepnationalaccounts.com/account/bills/ rates/RatesAndTariffs.aspx>.

The Apache Software Foundation. Visual Control - Individual Repair Progress Tracking. Digital image. Express Dashboard | Q.6. Maps. The Apache Software Foundation, 2009. Web. <http://data.quad base.com/Docs/edab/help/manual/M>.

Brook, Quentin, and Quentin Brook. Lean Six Sigma & Minitab: The Complete Toolbox Guide for All Lean Six Sigma Practitioners. [S.l.]: OPEX Resources, 2010. Print.

Desktop to Mobile. QR Code Implementation for Equipment Metric Tracking. Digital image. QR Codes – Quick Response | What Is a QR Code? Desktop to Mobile -Web Design & Mobile Web Design in Dorse, 2009. Web. <http://www.desktop-mobile.co.uk/qr-codes-quick-response/>.

DTE Energy Company. Illustration of System Components within Scope. Digital image. DTE Energy: About Electric Service. DTE Energy Company, 2012. Web. <http://www.dteenergy.com/residentialCusto mers/productsPrograms/electric/aboutElectricService.html>.

Excelarator LLC. Overview of Six-Sigma Statistical Analysis Methods. Digital image. Excelarator News. N.p., 2012. Web. <http://excelator.org/introduction-to-lean-six-sigma/>.

Global Energy Network Institute. Representation of Power Distribution Network Infrastructure. Digital image. GENI Archives. Global Energy Network Institute, 2006. Web. <http://www.geni.org/globalen ergy/library/national_energy_grid/united-states-of-america/ americannationalelectricitygrid.shtml>.

Gupta, Bhisham C., and H. Fred Walker. Statistical Quality Control for the Six Sigma Green Belt. Milwaukee, Wisc.: ASQ Quality, 2007. Print.

Harry, Mikel J. The Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements. Hoboken: Wiley-Blackwell, 2010. Print.


> Referenced Works Cont.

IHS GlobalSpec. National AEP Operating Region. Digital image. Power Generation & Distribution News. IHS GlobalSpec Group, 2007. Web. <http://www.globalspec.com/newsletter/pub/65/power-generation-distribution?vol=2&issue=10&isPastIssue=1>.

MicroStrategy. Visual Control - “Bird’s Eye” Outage Dashboard. Digital image. Feature Summary for the MicroStrategy GIS Connector for Google Map. MicroStrategy, 2012. Web. <http://www.microstra tegy.com/producthelp/9.3/GISHelp/topics/gis_integration.htm>.

Moresteam University Inc. Lean/Six-Sigma Black Belt Course Training: Coursebook Companion. 5th ed. Columbus, OH: Moresteam University, 2010. Print.

Ōno, Taiichi. Toyota Production System: Beyond Large-scale Production. Cambridge, MA: Productivity, 1988. Print.

Oregon DHS. Eight Forms of Waste Associated With Lean Methods. Digital image. DHS | OHA Transformation. Oregon.gov, 2009. Web. <http://www.oregon.gov/DHS/transformation/Pages/ lean.aspx>.

QFuse Network Inc. QR Code System Operation and Network Integration. Digital image. QFuse | What Are QR Codes? QFuse Network Inc., 2010. Web. <http://qfuse.com/learning/what-are-qr-codes>.

WasteSyn Inc. Representation of Power Generation Process. Digital image. WasteSyn Inc.- Technical Documentation. WasteSyn Inc., 2009. Web. <http://www.wastesyn.com/tech_ft.html>.

Williams Learning Network. Introduction to Distribution Systems: Introduction to Transmission and Distribution Systems - Workbook. Rockville: Williams Learning Network, 2007. Print


Application of Lean/Six-Sigma + Human Factors Principles To Power Distribution Network Optimization Efforts: Master of Science in Industrial Systems Engineering Project

Nick Boyd Dr. Carolyn Sommerich Dr. Jerald Brevick April 24th, 2013

Thank you! Questions/Comments?

3_Boyd_Nick_MS_ISE_Lean_Six Sigma_Technical Documentation

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