ch 04 value of information

8/8/2019 Ch 04 Value of Information

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The Value of

Information

Phil Kaminsky

[email protected]

David Simchi-LeviPhilip Kaminsky

Edith Simchi-Levi


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McGraw-Hill/Irwin 2003 Simchi-Levi, Kaminsky, Simchi-Levi

Value of Information

In modern supply chains, information replaces inventory Why is this true?

Why is this false?

Information is always better than no information. Why?

Information Helps reduce variability

Helps improve forecasts

Enables coordination of systems and strategies Improves customer service

Facilitates lead time reductions

Enables firms to react more quickly to changing market conditions.


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The Bullwhip Effect

and its Impact on the Supply Chain

Consider the order pattern of a single colortelevision model sold by a large electronics

manufacturer to one of its accounts, a nationalretailer.

Figure 1. OrderStream

Huang at el. (1996), Working paper, Philips Lab


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Figure 2. Point-of-sales

Data-Original

Figure 3. POS Data After

Removing Promotions

The Bullwhip Effect



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Figure 4. POS Data After Removing Promotion & Trend

The Bullwhip Effect



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Higher Variability in Orders Placed by

Computer Retailer to Manufacturer Than

Actual Sales

Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review


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Increasing Variability of Orders

Up the Supply Chain

Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review


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We Conclude .

Order variability is amplified up the

supply chain; upstream echelonsface higher variability.

What you see is not what they face.


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What are the Causes.

Promotional sales

Forward buying

Volume and transportation discounts Batching

Inflated orders

IBM Aptiva orders increased by 2-3 times whenretailers thought that IBM would be out of stockover Christmas

Motorola cell phones


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Single retailer, single manufacturer.

Retailer observes customer demand, Dt.

Retailer orders qt from manufacturer.

Retailer Manufacturer Dt qt

L


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Promotional sales

Volume and transportation discounts

Inflated orders

Demand forecasting

Order-up-to points are modified as forecasts

change orders increase more thanforecasts

Long cycle times

Long lead times magnify this effect


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Single retailer, single manufacturer.

Retailer observes customer demand, Dt.

Retailer orders qt from manufacturer.

Retailer Manufacturer Dt qt

L


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How big is the increase?

Suppose a P period moving average is used.

2

2221

)(

)(

P

L

P

L

DVar

qVaru


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Var(q)/Var(D):

For Various Lead Times

L=5

L=3

L=1

0

2

4

6

8

10

12

14

0 5 10 15 20 25 30

L=5

L=3

L=1


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Consequences.

Increased safety stock

Reduced service level Inefficient allocation of

resources Increased transportation costs


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Multi-Stage Supply

Chains Consider a multi-stage supply chain:

Stage iplaces orderqi to stage i+1.

Li is lead time between stage iand i+1.

RetailerStage 1

ManufacturerStage 2

SupplierStage 3

qo

=D q1 q2

L1 L2


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Multi stage systems

Centralized: each stage bases orders on retailers

forecast demand.

Decentralized: each stage bases orders on

previous stages demand

2

2

11

22

1)(

)(

P

L

P

L

DVar

qVar

k

i

i

k

i

ik

u!!

!

-

u

k

i

ii

k

ar

qar

12

222

1)(

)(


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Multi-Stage

Systems:Var(qk

)/Var(D)

0

5

10

15

20

25

30

0 5 10 15 20 25

Dec, k=5

Cen, k=5

Dec, k=3Cen, k=3

k=1


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The Bullwhip Effect:

Managerial Insights Exists, in part, due to the retailers need to

estimate the mean and variance of demand.

The increase in variability is an increasing functionof the lead time.

The more complicated the demand models and

the forecasting techniques, the greater the

increase.

Centralized demand information can significantly

reduce the bullwhip effect, but will not eliminate it.


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Coping with the Bullwhip Effect

in Leading Companies

Reduce uncertainty POS

Sharing information

Sharing forecasts and policies Reduce variability

Eliminate promotions

Year-round low pricing

Reduce lead times

EDI Cross docking

Strategic partnerships Vendor managed inventory

Data sharing


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Example:

Quick Response at Benetton

Benetton, the Italian sportswearmanufacturer, was founded in 1964. In 1975

Benetton had 200 stores across Italy. Ten years later, the company expanded to

the U.S., Japan and Eastern Europe. Salesin 1991 reached 2 trillion.

Many attribute Benettons success tosuccessful use of communication andinformation technologies.


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Example:

Quick Response at Benetton

Benetton uses an effective strategy, referred to as

Quick Response, in which manufacturing,

warehousing, sales and retailers are linkedtogether. In this strategy a Benetton retailer

reorders a product through a direct link with

Benettons mainframe computer in Italy.

Using this strategy, Benetton is capable ofshipping a new order in only four weeks, several

week earlier than most of its competitors.


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How Does Benetton

Cope with the Bullwhip Effect?1. Integrated Information Systems

Global EDI network that links agents with production

and inventory information EDI order transmission to HQ

EDI linkage with air carriers

Data linked to manufacturing

2. Coordinated Planning Frequent review allows fast reaction

Integrated distribution strategy


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Information for Effective

Forecasts Pricing, promotion, new products

Different parties have this information

Retailers may set pricing or promotion without

telling distributor

Distributor/Manufacturer might have new

product or availability information Collaborative Forecasting addresses these

issues.


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Information for

Coordination of Systems Information is required to move from local to global

optimization

Questions: Who will optimize?

How will savings be split?

Information is needed : Production status and costs

Transportation availability and costs Inventory information

Capacity information

Demand information


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Locating Desired

Products How can demand be met if products are not

in inventory?

Locating products at other stores

What about at other dealers?

What level of customer service will be

perceived?


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Lead-Time Reduction

Why?

Customer orders are filled quickly

Bullwhip effect is reduced Forecasts are more accurate

Inventory levels are reduced

How? EDI POS data leading to anticipating incoming

orders.


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Information to Address

Conflicts Lot Size Inventory:

Advanced manufacturing systems

POS data for advance warnings

Inventory -- Transportation:

Lead time reduction for batching Information systems for combining shipments

Cross docking

Advanced DSS

Lead Time Transportation: Lower transportation costs

Improved forecasting

Lower order lead times Product Variety Inventory:

Delayed differentiation

Cost Customer Service: Transshipment

ch 04 value of information

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