optimizing manufacturing operations using big data and analytics
TRANSCRIPT
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Optimizing Manufacturing Operations Using Big Data and Analytics
Rajasekhar Katta, OracleRamasamy Palaniappan, FCS
Gareth Foster, Wm Morrison Supermarkets PLC
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Agenda
Oracle BI Applications – An Overview of Oracle Manufacturing Analytics
Customer Success Story – Wm Morrison Supermarkets PLC
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Oracle Business Analytics
IntegratedAnalytic
Applications
Any Data, Any Source
Full Range of Analytics
On Premise, On Cloud,On Mobile
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BI Applications – Rapid Performance InsightCRM ANALYTICS
ORACLE BUSINESS INTELLIGENCE FOUNDATIONAND OTHER OPERATIONAL AND ANALYTIC SOURCES
SOURCE ADAPTERS:
SALES
• Pipeline Analysis• Forecast Accuracy• Up-sell/Cross-sell• Cycle Times• Lead Conversion• Sales Team
Effectiveness
MARKETING
• Campaign Effectiveness
• Customer Insight• Product Propensity• Market Basket
Analysis• Campaign ROI
SERVICE & CONTACT CENTER
• Service Effectiveness• Customer Satisfaction• Resolution Rates• Service Rep Efficiency• Service Cost• Service Trends
PRICE
• Price Segments• Price Waterfall
Analysis• Deal Life Cycle• Deal Desk Analysis • Product Pricing
Performance
LOYALTY
• Member Demographics
• Membership Trends• Promotion Cost• Promotion
Effectiveness• Points Analysis
ERP ANALYTICS
FINANCIALS
• General Ledger• Accounts Receivable• Accounts Payable• Cash Flow• Profitability• Expense Management
PROCUREMENT & SPEND
• Direct & Indirect Spend• Buyer Productivity• Contract Compliance• Supplier Perf.• Purch. Cycle Time• Employee Expense
SUPPLY CHAIN & ORDER MGMT
• Revenue & Backlog• Inventory Analysis• Fulfillment Status• Customer Status• Order Cycle Time• BOM Analysis
PROJECTS
• Project Funding and Budget
• Product Cost• Project Revenue• Project Billing• Project Profitability
HUMAN RESOURCES
• Employee Productivity• Compensation• Talent Management• Recruiting Analysis• Learning Analysis• Workforce Profile
MANUFACTURING
• Plan to Produce• Work Orders• Inventory and WIP• Quality• Resource Usage• Bill of Material• Costing • Kanban
ENTERPRISE ASSET
MANAGEMENT
• Maintenance History• Maintenance Costing• Asset Genealogy• Breakdown &
Preventive • Quality• Inventory
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Oracle BI ApplicationsDeeper than Dashboards
• Business Analytics Warehouse
• Over 100 star schemas
• Designed for analysis and reporting
• Over 9,000 metrics • Additional pre-
defined calculations
• Based on best practices
• Pre-mapped integration
• Fusion, EBS, PSFT, SEBL , JDE, SAP, IBM
• Role-based Security
• Over 500 Dashboards and Pages
• Over 3,000 reports• Across Lines of
Business
Pre-Built Analytic Warehouse
Best Practice Metrics
Certified App Integration
Role Based Dashboards
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Oracle Manufacturing Analytics Overview
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Business ChallengesDemand Uncertainty• How can I quickly respond to constantly
shifting customer demand?• How do I create a flexible production
environment that supports mass customization?
Operational Complexity• How do I simultaneously minimize costs and
ensure consistently high quality?• How do I optimize the use of resources while
balancing execution needs and production constraints ?
Quality Expectation• How can I quickly respond to quality issues
in manufacturing execution?• How do I reduce process variability?
Supply Chain Execution• How can I synchronize sales orders,
purchases, and production?• How can I increase inventory velocity &
customer service levels?
Your Company
Suppliers Customers
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Issue Materials
Produce and CompleteBatch /Work OrderCompletion Date
Schedule ProductionPlannedStart Date
Release Planned OrdersRelease Date
ForecastCustomer Demand
Generate andapprove productionplan
Open Batch/Work OrderActualStart Date
Assign Lots
Sales Orders
Close Batch/Work OrderClose Date
Complete Operations
MoveCompletionsto Sub Inventory
ConsumeResources
How much did the planned production change between plans?
What % of the plan was executed as planned?
What is the current forecast and forecast accuracy?
What is the cycle time from work order release to actual start?
What is the variance in material usage?
What is the % of resource utilization by department?
What are all the finished product lots that consumed a certain raw material lot?
What % of Inventory will expire in the next 14 days?
What is the formulation yield for a certain batch?
Analyze work order cycle times
What % of the work orders started late?
Manufacturing Execution
The Need for Analytics
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Manufacturing Execution
UpdateAR / GL
Capture Costs;Analyze VarianceProduce and
CompleteBatch /Work Order
Close Batch/Work Order
Complete Operations
The Need for Analytics
Standardize
Sample
Analyze
Disposition*
Report
Quality Open Batch/
Work Order
What are the test results by collection elements?
What are results by collection plan and specification?
What % of completions were scrapped and reworked?
What is the First Pass Yield?
What is the cost incurred variance by cost element?
What is the cost relieved variance by cost element?
Which GL Variance Account has the largest variance?
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• Limited information on production attainment to plans, plant performance etc• Quickly identify exceptions in current performance and root cause analysis
to identify bottlenecks in the production process• Monitor and track resource and material usage and variances
• Need to balance customer service levels while minimizing inventory• Inability to anticipate stock-out & excess inventory situations• Lack of visibility into enterprise-wide inventory status• Track sales order fulfillment in conjunction with supply
• Poor visibility into production costs• Lack insight into reasons for cost variances• Identify causes for cost variances by cost element details and take
corrective action
• No knowledge of critical work orders that are stuck and need to be expedited• Little visibility into work order cycle times and work order aging of open orders• Track execution of “perfect work orders” that are on-time and high quality
Lack of visibility into plan to produce process to help eliminate bottlenecks
No insight into detailed production costs and cost variances
Unable to identify work order performance related to on-timecompletions and quality
Lack of Inventory Visibility
to Improve Return on Assets
Key Challenges
Examples
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Oracle Manufacturing Analytics
Delivers an integrated view of planning, manufacturing execution, manufacturing quality and inventory builds
Summary level KPIs with drill down capabilities to identify exceptions
Cross-functional analysis capabilities with Supply Chain and Order Management , Procurement & Spend and Financial Analytics
Analyze current state manufacturing execution against targets and historical trends
– Track all major points in the process by work order & operation
– Track costs, quantities and amounts of usage
– Track dates between major points in the process
Oracle Exalytics and BI Mobile certified
Insight into the Plan to Produce Process
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Big Data & Structured Data DiscoveryUsing Endeca Information Discovery
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Manufacturing Analytics – Key Objectives and Questions by Role
ProductionExecutive/VP of Operations
Plant wide visibility into production execution against budgets and targets
• What is the production attainment for the current month?
• What is the planned production volumes for the next quarter?
• What are the bottlenecks in the current production process?
• How much revenue was lost due to rework and scrap?
ProductionCostAccountant
Complete visibility and tracking of manufacturing and inventory costs
• What are my cost variances at period close by cost element?
• What are my item gross margins?
• What are the trends in Manufacturing costs by Quarter?
• What is the value of Inventory on hand?
OperationsManager/Supervisor
Monitoring and controlling shop floor execution
• Are there bottlenecks on the production floor that is causing WIP inventory to build up?
• Are there bottlenecks on the production floor that is causing excessive production lead time?
• Is there excessive rework or scrap?• Is the actual production just the right
amount indicated in the production plans?
• Is there a shortage of resource that will cause production bottleneck?
Role Based Best PracticesProvide Relevant and Actionable Insight for Everyone
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Manufacturing Analytics: Sample DashboardsPrepackaged Manufacturing Analytics
Manufacturing Executive Production PerformancePlan to Produce
Production Costs Inventory
• Overview• Operations
• Inventory• Demand Fulfillment
• Plan Attainment• Pegging Analysis
• Plan Comparison
• Overview• Trends• Balances• Aging
• Exceptions• Activities• Returns• Bill of Materials
• Overview• Variance Analysis
• Overview• Production Backlog• Material Usage• Resource Usage• Work Order Aging• Work Order Cycle Time• Production Quality
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Manufacturing Analytics: Sample MetricsSample Analysis & Metrics
Production Execution Work Order Analysis Production Quality
• On Time Production• Production Past Due Backlog• Production Backlog• % Change in Production Quarter Ago• Resource Utilization• Material Usage Variance
• Work Order Age• # of Open Work Orders• Work Order Cycle Times• Start Quantity• Completed Quantity• Late Work Order %• Backlog Quantity
• Scrap %• Rework %• First Pass Yield• Test Quantity• Result Value• Consumed Quantity• Sample Quantity
Costing Planning Inventory
• Planned Cost• Actual Cost• Completions Cost• Cost Incurred Variance• Cost Relieved Variance
• Plan to Produce Linearity• Planned Amount• Production Attainment - Monthly• 0-15 days Demand quantity• 0-15 days Supply quantity
• Inventory Turns• Inventory On Hand Quantity• Obsolete Inventory• Expired Inventory• Expiring Inventory
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How are we tracking to monthly production targets?
Are we producing to plan?
Are we producing on-time and with desired quality?
How much of production backlog is past due?
Are the Inventory levels aligned with demand?
Sample Dashboard
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Oracle Manufacturing AnalyticsHierarchy of Metrics
Action Level
Metrics
OperationalLevel Metrics
Strategic Level MetricsGL Variance
Account
Work Order Cost Variance
Cost Variance By Cost Element
Standard Material Cost
Actual Material Cost
First Pass Yield
Scrap%
Rework%Scrap
ValueProduced
ValueWork Order Completions Qty Start Qty Scrap Qty
Production Linearity
Plan-to-Produce ratio
Service Level
Resource Utilization
Yield Variance
Late Work Orders%
Planned Value
Resource Cost Charged
Actual Yield
Formula Yield
# of Open Work Orders
# of Work Orders On-hold
Resource Time Charged
# of Late Work Orders
Drill t
o Det
ail
Identify Exceptions
Root Cause Analysis
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Hierarchy of Metrics – Production Cost VarianceDrill from GL Variance Account to Work Order Details
Drill to
Detail
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Oracle Manufacturing AnalyticsHierarchy of Metrics
Action LevelMetrics
OperationalLevel Metrics
Strategic Level Metrics
Demand Fulfillment Backlog
Inventory On Hand
Open Work Order Quantity
Planned Order Quantity
Production Attainment of Plan
Scrap Value
Produced Value
Work Order Completed
Quantity
Production to Plan Linearity
Sales Order Fill Rate
On Time Shipments
Production Backlog
Late Started Work Orders%
Planned Value
Ordered Quantity
Shipped Quantity
Planned Order QuantityWork Orders
Scheduled CompletionActual Completion
On Time Work Orders %
Work OrdersScheduled StartActual Start
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Oracle BI ApplicationsCross Module Analysis
Manufacturing
Supply Chain and Order Management Analysis spans one or
more OBIA modules
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Complementary Oracle BI Applications
Manufacturing Analytics
Oracle Manufacturing Analytics
Module Example of Cross-Functional Analysis
Supply Chain and Order Management Analytics
• Consolidate supply and demand to project fulfillment shortages• Sales Order to Work Order pegging
Procurement and Spend Analytics • Identify purchase order delays and it’s impact on production backlog
Financial Analytics • GL variance account drill down to work order details to analyze cost variance exceptions
Enterprise Asset Management Analytics • Provides visibility into spares inventory to analyze enterprise wide valuation of inventory
• Drill down from bottleneck resources to review planned and unplanned downtime trends for these resources
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BI and EPM Practice Introduction
Insert Picture Here
Platinum Partner
Development Alliance Partner
BI Pillar Initiative Partner
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Frontline Consulting ServicesA TEKsystems Global Services Division
Established 1996
Offices North America | APAC | EMEA
Business Units Strategic Consulting | Outsourcing | Solutions
Key Focus Areas Oracle BI/ EPM | Applications | Integration
Customers 500+ served Worldwide
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Why FCS
• Oracle R&D co-development partner• Demand Signal Repository
• Oracle On-Demand partner• Largest BI Partner in North America• In house Exalytics, Exalogic and Exadata machines and CoE• #1 Oracle recommended partner for large Global deployments like
General Electric, Schlumberger, Emerson, Sony, Clorox, Activision
• Oracle R&D recommended partner for Manufacturing and EAM Analytics
• Deep domain experience in Oil & Gas, Industrial, Hitech Manufacturing, CPG
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FCS BI Practice Summary
400+ Dedicated Resources
• 250+ successful live implementations• Tailored processes, templates and reporting for BI Apps implementations / upgrades• Technology Areas – OBIA, Informatica, OWB, ODI, DAC, OBIEE, GoldenGate etc.
Tools / Implementation Accelerators (FCS Innovations)o Products to fulfill OBIA gaps –Salesforce Analyticso BI setups using BI Configurator for Peoplesoft, JD Edwards and E-Business Suiteo Requirements Gathering using Business Requirements Generatoro Flexfields, Chart Fields, User defined fields setup using BI Flex Generatoro OBIEE Regression Testing using BI Assurer o BI, ETL data lineage using Path Findero Project Reporting on mobile using PMO Signal
Data WarehousingBusiness Intelligence ApplicationsOracle Business Intelligence Foundation
Platinum Partner
Development Alliance PartnerBI Pillar Initiative Partner
Oracle E-Business Suite Supply Chain ManagementOracle E-Business Suite Financial Management
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Manufacturing Transformation at Wm Morrisons PLC
Discovery – Build vs. Buy
Implementation
Roll-out and Stabilization
User Adoption
New Plant Roll-out
Managed Services
•2nd largest fresh food Manufacturer in the UK• 18 manufacturing sites across the UK, & 1 manufacturing site in Holland• Plans for more sites• A unique ‘Vertical Integration’ business model
Morrisons Manufacturing
Morrisons•18+ Billion £Turnover for Morrisons Plc •FTSE 40 •562 Stores•130,000 employees
Drivers for Oracle BI Application•Single version of the truth
•Lots of data, but a lack of meaningful information
•Decisions made on instinct not facts
•Standardization - KPI’s not aligned across sites
•Time and effort spend on producing report rather than analysis
•Incorrect KPI’s that don’t drive the correct business behaviour
•Timely information
•Spread sheet culture
Process•Oracle recommended FCS - Teksystems as there was gap in Manufacturing offering with BI Application then
•Got the reporting requirements created across plants
•Evaluated different options – Build vs. Buy
•Decided upon Build and Extend - out of the box installation of Manufacturing Analytics with customisation leveraging FCS-Teksystems
•Built and tested the solution
•Trained users and rolled out solution across multiple plants
Key Areas of Focus
•Stock Holding
•Plan Attainment
•ASCP Plan
•Yield
•Variance analysis
•Lot Traceability
•Quality
•Usage Tracking
Reduction
•Stock Days reduced
•Reduction in working capital
•Reduced Wastage
Improvement
•Improved Freshness
•Increased flexibility
•Job enrichment
•Informed and timely decisions
•Promotes the right behaviour
Key Learnings
•Use standard reports where possible
•Consider end to end flow and not just reports
•BI is not an just IT project, it affect people jobs and the people element of BI needs consideration.
•If implementing BI and the vertical system only do business critical reports which are needed for day1.
•Have several smaller releases rather than one major release
•Have a clear business requirements
•Be flexible as requirements change.
Key Learnings
•Business engagement is essential
•Be aware that BI will need continuous development after the implementation
•Challenge requirements - ensure any requirements drive action
•Standardise requirement across business units
Future Plans•KPI relationships and cross functional working
•Integrate Manufacturing and Retail
•Near real time loads
•Integrate non EBS systems
•HR analytics
•Significant improvement in quality of Data and master data
•Logistics/ Capacity