Competing on Supply Chain Analytics_V Good

7/30/2019 Competing on Supply Chain Analytics_V Good

1/22

Competing on

Supply ChainAnalytics

Tom DavenportBabson College

Supply Chain Council

May 25, 2011


2/22

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


3/22

Thomas H. Davenport Analytics at Work3 3

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)


4/22

Thomas H. Davenport Analytics at Work

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


5/22


Stage 5AnalyticalCompetitors

Stage 4Analytical Companies

Stage 3Analytical Aspirations

Stage 2Localized Analytics

Stage 1Analytically Impaired

Levels of Analytical Capability


6/22


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


7/22Thomas H. Davenport Analytics at Work7

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


8/22Thomas H. Davenport Analytics at Work8 8

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


9/22Thomas H. Davenport Analytics at Work9 | 2011 All Rights Reserved.

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



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



Data

The prerequisite for everything analytical

Clean, common, integrated

Accessible in a warehouse

Measuring something new and important



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



New Metrics / Data

Wine Chemistry Smile FrequencyOptimized revenue



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



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



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


17/22

Thomas H. Davenport Analytics at Work17 | 2011 All Rights Reserved.

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


18/22


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


19/22


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


20/22


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


21/22

Thomas H. Davenport Analytics at Work

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


22/22

Thomas H Davenport Analytics at Work

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!

Competing on Supply Chain Analytics_V Good

Documents

Transcript of Competing on Supply Chain Analytics_V Good