Industry-Propelled Evolution ofTeaching and Research in Supply

Chain Management

Hau L. LeeStanford University

2007

Overview

• The bullwhip effect as an example of the evolution of supply chain management

• The new emphasis on empirical-research

• The interactive nature of empirical and model-based research on the bullwhip effect.

Information Distortion:The Bullwhip Effect

• Order variability is amplified up the supply chain: upstream is worse.

• What you see is not what they face.

• Bullwhip, whip-saw, whip-lash effect; or acceleration principle.

Information Distortion:The Bullwhip Effect

Order Variability Up the Pampers Supply Chain

Source: Lee, Padmanabhan and Whang, 1997

Babies

P & G

Wholesalers

Retailers

Customers

Information Distortion:The Bullwhip Effect

Babies

P & G

Wholesalers

Retailers

Customers

3 M

Order Variability Up the Pampers Supply Chain

Bullwhip Impact• Teaching:

– Teaching cases (Barilla, Campbell, Solectron, West Marine, etc.)

– Renewed interest in beer game, computerized beer game, web-based beer game.

• Research:– Bullwhip descendants– Value of information sharing and collaborative forecasting– Incentives– Multi-site coordination– Empirical research

• Industry practice– ECR, QR, EFR, …– Information sharing, visibility, RFID, …

Taming the bullwhip

Dampening the bullwhip

Cracking the bullwhip

Controlling the bullwhip

Y2K bullwhip

Dot-com bullwhipDisaster bullwhip

Gulf war bullwhip

ECREFRQR

Countering the bullwhip

Mitigating the bullwhip

Bullwhip Effect at Barilla SpA

0100200300400500600700800900

Time (Week)

Quintals per Week

Sell-Through fromDC

Orders to Barilla

JITD at Barilla SpA

Stockout

Inventory

Shipments

Results of Test at Cortese's Marchese

DC

Source: Hammond

The Mosquito Link

Source: Benchmarking Partners

Interest in Empirical Research

• Empirical research is multi-dimensional: Field-based case studies Ethnographical approaches Statistical data analyses

• Richer knowledge advances through interactive empirical and model-based research Empirical-Model-Empirical-Model- …

Examples: RFID Logistics friction Bullwhip

Empirical Research of Bullwhip

Micro --firm or supply chain level

Macro --economy level

Various cases studies

Economists,Cachon et al.

Establish existenceModel building for causes and remedial actionsManagement messages

Understand extensiveness of phenomenonWhere is it more prevalent

Focus Sources Purposes

Bullwhip in Electronics Industry

week

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

Peripheral Product Consumables

week

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

Unit orders from a major retailer to manufacturerTotal unit sales at outlets of retailer

More Empirical Evidence

Time in a YearTime

Volume Volume

Orders POS

PC Chicken Noodle Soup

Demand Variability -- Bullwhip Effect in LaserJet L Series

Shipments

4L 5L

Sell Thru-To

4L 5L

Reseller Order Bullwhip -- 5L

Channel Inefficiencies

Constrained Supply

0

2000

4000

6000

8000

10000

12000

0 2000 4000 6000 8000 10000 12000

Sel

l-T

o S

td D

ev

Best Buy

Comp USA

Elek-Tek

Micro Electronics

Office Depot OfficeMax

PC Warehouse

StaplesTandy Corp

Order Std Dev

Bullwhip Factors in Fransoo and Wouters (2000)

Node

Production

DC

Retail franchisee

Meals

1.75

1.26

1.67

Salads

1.23

2.73

2.09

Bullwhip factor defined as: COV of customer orders

COV of orders places at suppliers

Empirical Research on Bullwhip

• Firm-based: – Fransoo and Wouters (convenience stores, 2000)– de Kok et al (Philips Electronics, 2005);– Lai (Sebastian de la Fuente, 2005)

• Industry-based:– Anderson et al. (machine tools, 2000); – Terwiesch et al., (semiconductors, 2005)

• Economy-based:– Cachon et al. (2005)

Cachon, Randall & Schmidt (2005)

• Research questions:– How prevalent is bullwhip effect in economy?

– Why do strengths of bullwhip effect differ across industries in an economy?

– Are there any shifts in the intensity of bullwhip effect over time?

• Data:– US Census Bureau, 1992-2004

– Monthly sales and inventories in retail, wholesale and manufacturing sectors

Setup of Cachon et al (2005)

• Assume shipment = demand.• Data de-trended but not de-seasonalized.• Data aggregated over industry sector (and

monthly).• Adjust shipment to account for margin so that it is

comparable to inventory.• Imputed productiont

= Shipmentt + (Inventoryt – Inventoryt–1)

• Take natural log of all data.

Cachon et al (2005) Results

• Bullwhip Amplification Ratio AR = Var(Production)/Var(Shipment)

• Results:– Strong bullwhip effect observed if data was seasonally

adjusted.

– With seasonally unadjusted data: little bullwhip at manufacturers (62% with AR < 1) and retailers (86% with AR < 1, some at wholesalers (84% with AR > 1).

– Production smoothing due to predictable seasonality may have overwhelm tendency to amplify.

Potential Measurement Problems• Claim: no need to focus on demand order, since it is

information which is costless to supply chain – but, isn’t it the case that distorted information creates inefficiencies in the supply chain?

• Production inferred by differences in Average Inventory in consecutive months, which is a “smoothed” measure and not the same as beginning and ending inventory.

• Production levels are constrained by capacity and material availabilities, but demand orders are not.

• Aggregation may hide bullwhip– Aggregation across substitutable products– Aggregation across time

Flexibility Contracts

0

20

40

60

80

100

N N+1 N+2 N+3 N+4 N+5 N+6 N+7

Time in Forecasted Month Out

Allowable change in

forecasts in percent (+/-)

Three Key Problems

• Value of information sharing inconclusive, probably based on specific demand model used. Can we use the most general demand model to generalize results?

• Information sharing usually assumes supplier having knowledge of actual demand model and order policy used at retail level. What if supplier doesn’t?

• Ordering decisions are based on two motivations: responsive to demand, and order smoothing. How can we analyze bullwhip effect in the presence of order smoothing effects?

A Generalized Demand ModelChen and Lee (2007)

0,

itittD

Dt : Demand in period t.t-i,t : IID random variable normally distributed with mean 0 and standard deviation , where .2

0

2

i i

• Termed MMFE (Martingale Model of Forecast Evolution• IID, AR(1), IMA(0,1,1), general ARIMA, and ADI

models are all special cases of MMFE model.

Forecasting Under MMFEChen and Lee (2007)

0,,

itittitF

Ft-i,t : Forecast of period t made in period ti.

tittittit FF ,,1,

General Order-Up-To PolicyChen and Lee (2007)

1iit

Tit mS εw St : Order-up-to level in period t.

wi : Row vector of weights.ti : Row vector of forecast revisions made in period ti.

itT

iii itttt DSSO

εeww )()( 1111

Ot : Order quantity in period t.ei : Unit vector with the i-th element equal to one.w0 = 0.

Setup of Supply Chain Model• Retailer could optimize m and wi to minimize its cost.• Supplier also uses generalized order-up-to policies

with inventory borrowing assumption when stockouts.

• Supplier could optimize its own m’ and wi’ to minimize its cost, based on whether retailer shares its forecast revision data to supplier or not.

• Difference of supplier cost with or without retailer forecast revision data constitutes the value of forecast information sharing.

• Such sharing requires retailer sharing its order policy (m and wi), and the forecast revisions t with supplier.

Advanced Order Revision ModelChen and Lee (2007)

itT

iiitito 111, )( εewwot-i,t : Revision of order projection to supplier for

period t made in period ti.

0,

ititt oO

1. Retailer gives advanced order projections to supplier.

2.

3. Final order for period t is:

4. Can show that the advanced order revision model is equivalent to the model of forecast revision sharing, but NO need to share retailer order policy and forecast revisions.

Total Supply Chain Analysis

1,

)(

))(1(

12,

131

2

11,

1

2

1,

io

o

o

tT

iLitt

tTLt

TL

kktt

tT

L

kktt

e

eεe

εeOptimizing total supply chain cost results in:

(where L is the lead time to retailer and is a computed constant between 0 and 1.)

Observations:1. If retailer optimizes its own cost, then the resulting order

revision vector has element given by above, but that = 0. This is equivalent to postponing a fraction of the order quantity to the subsequent period, i.e. order smoothing.

2. With order smoothing, bullwhip may or may not exist.

The Evolution Cycle

ResearchResearch

IndustryIndustryPracticePractice

NewNewVenturesVentures TeachingTeaching

Summary

• Supply chain management as a field has benefited from the joint evolutions from industry practice, teaching and research.

• Close interactions have created a field with more rigor, relevance, and business values.

• Such evolutions also breed a new group of research-based business ventures.

• Evolutions are still on-going, and the opportunities remain great.