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 [email protected]
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
1/1
/20
05
2/1
/20
05
3/1
/20
05
4/1
/20
05
5/1
/20
05
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/20
05
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/20
05
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/20
05
9/1
/20
05
10
/1/2
00
5
11
/1/2
00
5
12
/1/2
00
5
1/1
/20
06
Date
Co
st(
$)
&M
ile
ag
e
TOTAL_COST
READING
Asset Repair or ReplaceForecast asset monthly cost over next 12 months
Forecast for 988F2-B040
0
50000
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250000
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Month
Co
st
($)
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
0
10000
20000
30000
40000
50000
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70000
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100000
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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: [email protected]
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