New Technologies for Asset Performancewith Oracle Asset Solutions

Tom Sichko, Product Strategy Director ALMMark Hornick, Sr. Manager Development Data Mining TechnologiesKamal Ajitsaria, Practice Head - Manufacturing IT Geometric Limited

The following is intended to outline our generalproduct direction. It is intended for informationpurposes only, and may not be incorporated into anycontract. It is not a commitment to deliver anymaterial, code, or functionality, and should not berelied upon in making purchasing decisions.The development, release, and timing of anyfeatures or functionality described for Oracle’sproducts remains at the sole discretion of Oracle.

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Program

• Introductory Remarks

• Manufacturing Operations Center andEnterprise Asset Management

• Predictive Analytics for Asset Management

• Summary and Conclusions

• Appendix

Real-time Technologies for Asset Performance with Oracle Manufacturing OperationsCenter (MOC)

Oracle OpenWorld 20008

4

Contents

• Benefits of Real-time Asset Performance Management

• Solution Demo – Real-time Equipment PerformanceManagement

• Operations Environment

• Demonstration Scenario

• Solution Technical Details

• Demonstration

• About Geometric

5

Adverse impact of disconnected asset managementsystems

6

• Operational impact

• Increased mean time to repair

• Reduced mean time between failure

• Loss of production capacity andschedule conformance

• Business impact

• Revenue loss due to

• missed customer commitments;higher customer dissatisfaction

• Cost escalation due to

• increased cost of asset management

• loss of productivity on shop floor

Disconnect

Disconnect

Disconnected Preventive Maintenance (PM)

7

• Manual entry of meter readings in log books and then inmaintenance management system

• Meter usage calculated periodically and maintenancescheduled

• Preventive maintenance performed without taking into accountactual condition

• Fixed cost incurred for each preventive maintenance activity

Meter Reading for runhours and consumption

Defined PM strategy Due date for PMPM done without

information of actualcondition

Fixed cost incurred foreach maintenance

Disconnected Breakdown Maintenance

8

• Maintenance problems typically noticed when throughputsuffers

• Delays in flow of information from operator to planner totechnician

• Maintenance plans to resolve the issues as per priority andresource availability

• By that time, small issues might have contributed tosignificant asset breakdowns

Operator observesproblem (Typically

noticed when problemcontributing to

throughput)

Informs maintenance &updates logbook

Problemcommunicated to

planner

Planner schedules forchecking the listed

problems

Technician reportsbacks with problem /

requirement

Work planned &executed

Work planned &executed

Key reasons for disconnected asset managementsystems

9

• Staggered asset information across disparate shop-floorsystems

• Hidden / undefined asset performance information

• No connectivity between operational real-time data andmaintenance management system

• Inability to track and schedule resources in real time

Oracle Manufacturing Operations Center

The Foundationfor ContinuousImprovement inManufacturing

Operations

Automation& Process

ControlSystems

ERPSystems

Users

PLC CNCMachines

DCS SCADASystems

AdvancedProcessControl

HumanMachineInterface

- MES- Quality- Cost Mgmt…

ManufacturingOperationsData Model

ManufacturingOperationsData Model

Role-BasedDashboards

Contextual-izationEngine

Corporate BI

Cross-PlantKPIs

HistoricalTrends

Plant-SpecificKPIs

Real-TimeUpdates

Device-Generated Data

Production ActualsSchedules

Item Master Data

ProductionManager

Plant Mgr /VP of Mfg

MES Shop Floor Communication Drivers

Single Repository for Mfg Operations DataProvide Consistent Information for All Manufacturing Users

• Generic data model supportshierarchical structure for reportingor building KPIs and metrics

- Support for industry standardssuch as S-95

- Out-of-the-box hierarchical dimensions:time, product, and equipment

- Flexible and configurable

• Open and extensible to meet therequirements of different industries

- Capture process variables

- Capture additional parameters for Item,Equipment, and Work Orders

Manufacturing

Operations

Data Model

Manufacturing

Operations

Data Model

Gra

nula

rity

Enterprise Level- Products- Orders- Plans / Schedules

Plant Level- Work Orders- Batches- Mfg Routing

Equipment Level- Availability- Status- Output- Quality- Parameters

Device Level- I/O Tags- Sensor ID

360º View of Equipment PerformanceMaximize Performance of Your Assets

Understand Key Factors that ImpactOutput and Quality

- Production Supervisor

- Line Manager

- Maintenance Supervisor

For

• Equipment downtime• Mean time to failure• Effective run-time

• Pressure• Temperature• Humidity

• Quantity produced• By hour, shift, week

• Quantity rejected• Quantity scrapped• Defects by reason code

Process Parameters

Availability

Production Quality

Production Output

Preventive Maintenance powered by Oracle MOC

13

Equipment Idle Operator starts the heater.Temperature reaches 60ºCin next 2 min

Operator switcheson the Motor

Operator opensthe hopper valve.Resin flow starts

Systems run at steady state

Suddenly heater temperaturestarts falling

Due to corresponding increase inmaterial viscosity, motor currentstarts rising

Preventive maintenance fixesthe problem with heater andtemperature returns tosteady state

Operator requests preventivemaintenance based on leadheater temperature trend

Visit us atbooth #

2441,MosconeSouth for

moreinformation

Solution technical details

PLC – Source for real-time equipment data

Kepware OPC Server – First-level shop floor datacollection and aggregation engine

14

Manufacturing Operations Center –Data contextualization , business logicand workflow

Manufacturing Hub Dashboard – Role-based , drill down visualization

Demo15

Simulate Reading from PLCDefined the Collection of Readings from Heater

16

Validate Receipt of ReadingManufacturing Operations Center Data Model

17

Plant Manager Dashboard

18

OEE and Quality Trend – Month to Date

19

Overall Equipment Effectiveness (OEE) byMaintenance Area

20

Click to viewClick to viewalert detailsalert details

Operations Manager DashboardLine-Level Status

21

EquipmentEquipmentdown signaldown signal

EquipmentEquipmentavailabilityavailabilitydistributiondistribution

Maintenance Manager DashboardEquipment-Level View of Critical Process Parameters

EquipmentEquipmentoperatingoperating

parametersparameters

22

Solution Benefits (specific to demo scenario)

• Improvement in productivity & throughput

• Optimization in asset utilization

• Decrease in shop-floor incidence rates

• Minimize response time to exceptions

• Minimize time spent on non-value added activities

• Better collaboration through “single version oftruth”

23

The copyrights/ trademarks of all products referenced herein, are held by their respective companies.

www.geometricglobal.com

Thank you !!!

Visit us at booth # 2441,Moscone South for more

information on our offeringsfor Oracle MOC

24

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Mark HornickSr. Manager DevelopmentData Mining TechnologiesOracle Corporationmark.hornick@oracle.com

Predictive Analyticsfor

Asset ManagementData Warehousing

ETL

OLAP

Data Mining

Oracle 11Oracle 11gg DBDB

Statistics

Contents

• Business Problems in Asset Management

• Introducing Data Mining

• Case Study: Edward C Levy Co• Root Cause Analysis

• Equipment cost forecasting

Business problems inAsset Lifecycle Management

• Provide insight into asset failure

• “Root cause analysis” of equipment failures

• Understand factors in failure after repair

• Understand the impact of operator usage on equipment failure

• Predict asset failures before they occur

• Predict asset failure or parts shortage based on analysis of work orders,parts and equipment usage

• Predict when preventive maintenance should be done instead of orin addition to manually determined rules

• Prioritize asset replacement or maintenance by cost forecasting

• Understand factors affecting fuel consumption

• Operator, operator experience and training

• Environmental factors

• Maintenance and repairs, along with technicians experience and training

Business Intelligence & Analytics

Knowledge discoveryof hidden patterns

What is the predictedcost of repairsfor the coming period,and why?

Extraction ofdetailed androll up data

How manywork order ofeach type(breakdown,maintenance,etc.)?

Summaries,trends andforecasts

How manybreakdowns byasset, month,and dept.?

Queryand Reporting OLAP Data Mining

“Insight & Prediction”“Information” “Analysis”

What is Data Mining?

• Also known as Predictive Analytics

• Process of sifting through massive amountsof data to find hidden patterns and discovernew insights

• Understand the past, predict the future• Take “reporting” to a whole new level

• Data Mining can provide valuable results:• Rank factors associated with a target attribute (Attribute Importance)

• Predict a continuous numeric outcome (Regression)

• Predict individual behavior (Classification)

• Find profiles of individuals or asset with a given behavior (Decision Trees)

• Segment a workforce or assets (Clustering)

• Determine important relationships (Associations)

• Find fraud or rare “events” (Anomaly Detection)

Oracle Data Mining 11gOracle in-Database Mining Engine

• Data Mining Interfaces (Server)

• PL/SQL & Java APIs• Develop & deploy predictive analytics applications

• Wide range of DM algorithms (12)

• Classification & Regression

• Clustering

• Anomaly Detection

• Attribute Importance

• Feature Extraction

• Association Rules

• Structured & Unstructured Data (text mining)

• Oracle Data Miner (GUI)

• Simplified, guided data mining using wizards

• Predictive Analytics• “1-click data mining” from a spreadsheet

Data Warehousing

ETL

OLAP

Data Mining

Oracle 11Oracle 11gg DBDB

Statistics

What is a model?

• A compact representation of knowledge orpatterns present in data

• Produced from a variety of techniques…

Regression, Classification, Clustering, Association

Attribute Importance, Anomaly Detection, Time Series,

Feature Extraction, Text Mining, Sequence Mining

• A model can predict or represent values

in a generalized way

Regression

For a simple dataset with two attributes,a line can be used to approximate the values

y = mx + b

A simple model can be expressedin terms of values (m, b)

Models aren’t perfect…predictions have an error component

Metrics like Root Mean Square Error (RMSE)are useful for assessing and comparing models

Asset Usage (hours)

Ass

etA

nn

ua

lC

ost

($)

Predict a continuous numerical valueHow much is an asset likely to cost given its usage?

Why data mining models?

• Consider large datasets• 100s or 1000s attributes

• 1000s to millions of records

• Some are strings, others are numbers

• Some have ordered values, others have unordered values

• It is intractable for a person to identify patterns or

extract knowledge from such a large dataset

• But a computer and the right algorithm

can do so very efficiently

Case Study: Edward C Levy Co.Proof of Concept

• An international, fully integrated construction and materialscompany providing…• steel mill services, paving services

• flame technologies and services

• premium aggregates to the construction industries

• manufacturing and distribution of asphalt mixtures

• POC business problems• “Root cause analysis” of equipment failures

• Prioritize asset replacement or maintenance by cost forecasting

• Oracle Data Mining with Oracle Data Miner GUI

• Data (4 years worth)

• ~121,000 work orders

• ~3,800 assets

• ~350,000 meter readings

• ~186,000 work order cost details

Issues in Data and Interpretation

• Where to focus?• Most costly asset groups

• Recommend engine replacements, but…• a rule is imposed to replace at 13K miles…

• no data to analyze over 13K miles

• How and where is the equipment being used• On/off road, work environment conditions (weather)

• Geography

• Operator(s) and operator skills / experience

• “Flex fields”

• How quickly is the data available formaking recommendations?

Work Order Count by Type percentageData Understanding(Do we have enough examples for mining?)

Searching for causes of asset failuresAssociation Rules / Sequence Analysis

oilchange

tirereplacement

hydraulicbreakdown

oilchange

tirereplacement

hydraulicbreakdown

electricalbreakdown

enginebreakdown

enginebreakdown

oilchange

enginebreakdown

enginebreakdown

Org: 142

Tech#: 554477

Driver#: 669431

Org: 142

Tech#: 320453

Driver#: 669431

Org: 788

Tech#: 554477

Driver#: 669431

Org: 456

Tech#: 654354

Driver#: 669431

Data for Model BuildingAll Breakdowns

Causes of equipment breakdownsAssociation Rules results

If Previous Work Order is ENGINE BREAK DOWN

Then ENGINE BREAK DOWN occurs

With confidence 76.9%involving 3.4% of cases

Next…Go back to the data to understand what could beproducing this result, perhaps by mining thosecorresponding assets and work orders

Asset Repair or ReplaceTime series data with mileage meter reading

Wheel Loader 988F2-B040

0100002000030000400005000060000700008000090000

100000

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/20

05

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/20

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/20

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/1/2

00

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/1/2

00

5

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/1/2

00

5

1/1

/20

06

Date

Co

st(

$)

&M

ile

ag

e

TOTAL_COST

READING

Asset Repair or ReplaceForecast asset monthly cost over next 12 months

Forecast for 988F2-B040

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Actual Cost

SVM

Forecast

Asset Repair or ReplaceBuilding the time series data

cost_details

work_orders

meter_readings

asset_time_series

SELECT w.asset_number, w.asset_group_id, sum(c.actual_total_cost) TOTAL_COST,w.date_completed, w.work_order_type_disp, m.life_to_date_reading, w.wo_description,m.meter_name, m.current_reading_date, m.meter_uom, w.asset_description

FROM cost_details_all c, work_orders_all w, meter_readings_all m

WHERE c.wip_entity_id = w.wip_entity_idand c.organization_id = w.organization_idand w.asset_number = '988F2-B040'and m.asset_group_id (+) = w.asset_group_idand m.asset_number (+) = w.asset_numberand m.wip_entity_id (+) = w.wip_entity_id

GROUP BY w.asset_number,w.asset_group_id, w.asset_description, w.wo_description,w.work_order_type_disp,m.meter_name, m.life_to_date_reading, m.meter_uom,m.current_reading_date, w.date_completed

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Series1

Asset Repair or ReplaceTime series data for specific asset

Asset Repair or Replace

• At time of breakdown, sum costs over next N months• If greater than threshold, recommend “consider replace”

• E.g., threshold could be average + 1 SD cost for asset group

• Build one model per asset…1000s of models

• Forecast periods for each asset

Summary

• A well-designed and audited data warehouse is keyto address important business problems

• Availability of the right data is key for effective data mining

• Domain experts and data mining experts must work together

• Predictive analytics provides valuable insight intoAsset Management business problems…taking us beyond standard query and reporting

• Oracle Data Mining provides a rich environmentfor solving predictive analytics-based business problems

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More Information:

Contact Information: Email: mark.hornick@oracle.com

Oracle Data Mining 11g•oracle.com/technology/products/bi/odm/index.html

Oracle Statistical Functions•http://www.oracle.com/technology/products/bi/stats_fns/index.html

Oracle Business Intelligence Solutions•oracle.com/bi

http://search.oracle.com

oracle data mining

Meet with Development, Strategy, and Subject Matter Experts to

discuss E-Business Suite’s Asset Lifecycle Management (ALM)

including Enterprise Asset Management (eAM) and Oracle Asset

Tracking (OAT)

When: Wednesday, 10:00 am – 11:30 am , Section 3

Where: Applications Lounges, Moscone West - 2nd Floor Lobby

Meet the ExpertsMeet the Experts

Demo Grounds: Moscone South Exhibition Hall

Visit Booths K33 and K34

Follow-On Sessions, Room 2008Asset Lifecycle Management

• 4:00pm Map it! Track it! Maintain Assets with Googleand ESRI

• 5:30pm R12 eAM Implementation Customer Panel

Mar 23-24, 2009

Daytona Beach FL

Hope to see you in March at our 3rd Annual Maintenance Summit!

Mix.oracle.com – Applications Community

Join Groups:

•Asset Lifecycle Management

•Industry specific groups