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Competing on
Supply ChainAnalytics
Tom DavenportBabson College
Supply Chain Council
May 25, 2011
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Thomas H. Davenport Analytics at Work2
The Planets Are Aligned for Analytics
Powerful information technology
Massive amountsand new types of
data
Critical mass of quantitative skills
A business need for optimization of
supply chains and overall business
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What Are Analytics?
Optimization
Predictive Modeling/Forecasting
Randomized Testing
Statistical analysis
Alerts
Query/drill down
Ad hoc reports
Standard Reports
Whats the best that can happen?
What will happen next?
What happens if we try this?
Why is this happening?
What actions are needed?
What exactly is the problem?
How many, how often, where?
What happened?
DescriptiveAnalytics(the what)
Degreeof Intelligence
Predictive andPrescriptive
Analytics
(the so what)
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Analytical Culture
And Business
Processes
4 | 2011 All Rights Reserved.
Analytics at WorkThe Big Picture
Data
Enterprise
Leadership
Targets
Analysts .
Better
Decisions!
Systematic Review
Analytical Capability Organizational Context Desired Result
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Stage 5AnalyticalCompetitors
Stage 4Analytical Companies
Stage 3Analytical Aspirations
Stage 2Localized Analytics
Stage 1Analytically Impaired
Levels of Analytical Capability
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Analytical Competitors
Old Hands, Turnarounds, Born Analytical
Marriott Revenue management
Progressive risk, pricing
Capital One information-based strategy
Google page rank, advertising, HR
Harrahs Loyalty and service
MCI/Worldcom Cost identification and reduction
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Analytical Competitors
in Supply Chain
UPS Operations and logistics, then customer
WalMartInternal logistics, then supplierrelationships
Dellstill great at connecting demand and supply P&G--$2B in supply network redesign savings
Amazon Down to packing every truck
Netflix DC location and customer preferencealgorithms
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Analytical Competitors or Companies
Across Industries
Professional Sports Oakland As
New England Patriots
Houston Rockets
AC Milan
Financial Services Progressive Insurance
Capital One
Royal Bank of Canada
PharmaceuticalsAstra Zeneca
Merck
Vertex
Industrial Products CEMEX
John Deere & Company
Governmen
t VA
Social Security
IRSTransport FedEx
Schneider National
United Parcel Service
Telecommunications Nextel
Rogers
Cablecom
Consumer Products E&J Gallo
Mars
Unilever
eCommerce Ebay
Expedia
Zillow
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Supply Chain Analytics Alternatives
What will happen?(Prediction/Simulation)
Whats the Best
that Can Happen?(Optimization)
What Happened?
(Reporting)
How and Why Did It
Happen?(Modeling)
Timeframe
Content TypeInformation Insight
Future
Past
Yield management;product mix,
scheduling, routingoptimization
Process control,bottleneck analysis
Demand forecasting,product profitability
Product quality, deliveryperformance, asset
utilization
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10/22Thomas H. Davenport Analytics at Work10 | 2011 All Rights Reserved.
How to Do It: The Analytical DELTA
Data . . . . . . . . breadth, integration, quality
Enterprise . . . . . . . .approach to managing analytics
Leadership . . . . . . . . . . . . passion and commitment
Targets . . . . . . . . . . . first deep, then broad
Analysts . . . . . professionals and amateurs
DELTA = change
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Data
The prerequisite for everything analytical
Clean, common, integrated
Accessible in a warehouse
Measuring something new and important
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Supply Chain Metrics Before Analytics
Level 1 Metrics
Performance Attributes
Customer-Facing Internal-Facing
Reliability Responsive-ness
Flexibility Cost Assets
Perfect Order Fulfillment Order Fulfillment Cycle Time
Upside Supply Chain Flexibility Upside Supply Chain Adaptability Downside Supply Chain Adaptability Supply Chain Management Cost
Cost of Goods Sold
Cash-to-Cash Cycle Time Return on Supply Chain Fixed Assets
Return on Working Capital
Source: Supply-Chain Operations Reference-model (SCOR), Version 8.0
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New Metrics / Data
Wine Chemistry Smile FrequencyOptimized revenue
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Enterprise
If youre competing on analytics, it doesnt make
sense to manage them locally
No fiefdoms of data
Avoiding spreadmartsanalytical duct tape
Some level of centralized expertise for hard-coreanalytics
Firms may also need to upgrade hardware andinfrastructure
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Leadership
Gary Loveman at Harrahs
Do we think, or do we know?
Three ways to get fired
Barry Beracha at Sara Lee
In God we trust, all others bring data
Jeff Bezos at Amazon We never throw away data
Our CEO is a realdata dog
Sara Lee
executive
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The Great Divide
Is your seniormanagementteamcommitted?
Full steam ahead!
Hire the people
Build the systems
Create the processes
Prove the value!
Run a pilot Measure the benefit
Try to spread it
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Targets
Pick a major strategic target, with a minor or two
TD Bank= Customer service and its impact
Harrahs = Loyalty + Service
Google = Page rank/advertising + HR Can also have two primary user group targets
Wal-Mart = Category managers + Suppliers
Owens & Minor= Supply chain managers + hospitals
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Thomas H. Davenport Analytics at Work18 | 2011 All Rights Reserved.
Ladder of Analytical Applications Supply
Chain
Product Database
Product Groups
Routings
Customization
Yield Management
Replenishment
Adjust processflow based onquality, cost,
demand
Automaticallyanticipate needs
and trigger orders
Segment productsby cost,
availability,inventory policy
Configureproducts while
maintainingefficiency
Vary processingand pricing
Capture detaileddata on products,processing steps
Data in Order
Key Targets/Segments
Differentiated Action
Institutional Action
Real-Time Optimization
Predictive Action
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Thomas H. Davenport Analytics at Work19 | 2011 All Rights Reserved.
Analysts
5-10%
Analytical ProfessionalsOwn/RentCan create new algorithms
Analytical Semi-ProfessionalsOwn/RentCan use visual and basic statistical tools,create simple models
Analytical Amateurs--OwnCan use spreadsheets, useanalytical transactions
15-20%
70-80%
* percentages will vary based upon industry and strategy
1% Analytical Champions--OwnLead analytical initiatives
f S C
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Whats the Frontier of Supply ChainAnalytics?
Supplier Integration Wal-Mart
ABB
Customer Integration
P&G
Tesco
Product Profitability
Parker Hannifin
Asva Oy
Real-Time Action
Kraft
UPS
Whole-Process Management
McKesson
AmBev
Alcon
Long-Term Demand and Risk Modeling
Kirin Toyota
Process Change-Ability
Amazon
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Analytics and the SCOR Model
21 | 2011 All Rights Reserved.
Adopting the SCOR model can help withestablishing process metrics
The SCOR model may also provide a context
for descriptive analytics
Predictive and prescriptive analytics have togo beyond the model, however
There may need to be proprietary metrics anddata for competitive advantage
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Keep in Mind
22 | 2011 All Rights Reserved
Five levels, five factors for buildinganalytical capability
Data and leadership are the most
important prerequisites
Make sure your targets are strategic
Tie all your supply chain analytics work tospecific decisions
This is not business as usualthere is ahistoric opportunity to transform yoursupply chains and businesses!