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The Value of Information Sharing and Early Order Commitmentin Supply Chains: Simulation Studies

Jinxing XieDept. of Mathematical SciencesTsinghua University, Beijing 100084, China

Co-works with Xiande ZhaoDept. of Decision Sciences & Managerial EconomicsFaculty of Business AdministrationThe Chinese University of Hong Kong, Hong Kong, China

et. al.

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Papers Reviewed• X. Zhao, J. Xie and J. Leung, "The Impact of forecasting models on the value of Information Sharing in a Supply Chain", EJOR, Vol. 142, No. 2, (Oct. 2002), pp. 321-344.

• X. Zhao, J. Xie and J. Wei, "The Impact of forecasting errors on the value of Order Commitment in Supply Chains", Decision Sciences, Vol. 33, No. 2 (Spring 2002). pp. 251-280.

• X. Zhao, J. Xie, "Forecasting errors and the value of information sharing in a supply chain", IJPR, Vol.40, No.2, Jan. 2002, 311-335.

• X. Zhao, J. Xie and W.J. Zhang, "The Impact of Information Sharing and Ordering Co-ordination on Supply Chain Performance". SCM, Vol.7, No.1, 2002, 24-40.

• X. Zhao, J. Xie and R. Lau, "Improving the Supply Chain Performance: Use of Forecasting Models versus Early Order Commitments", IJPR, Vol.39, No. 17, Nov. 2001, 3923-3939.

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Outline

• Motivation

• Simulation Procedures

• ANOVA

• Some Results

• Summary

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Motivation

• Simulation in MRP

Impact of Lot-sizing rules

Impact of freezing MPS parameters

• Can Simulation Methodology be used to SCM Researches?

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Bullwhip Effect: Analytical ModelsLee, Padmanabhan, and Whang (1997):

– "bullwhip effects" and causes

– Four sources of the bullwhip effect:

• Demand signal processing

• Rationing game

• Order batching

• Price variation

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Bullwhip Effect: Analytical Models

Published in 2000:† Chen, Drezner, and Simchi-Levi (1996): "bullwhip

effect" and moving average forecasting † Chen et al. (1996): “bullwhip effect” and

exponential smoothing forecasting

Demonstrated that the variance of orders was always higher than that of demand

Demand pattern, forecasting model and forecasting parameter influence the variance amplification

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Information Sharing: Analytical Models

Lee, So and Tang (1996, Published in 2000): studied benefits of information sharing and replenishment co-

ordination Findings:

sharing information alone would provide cost savings and inventory reduction for the supplier, but it will not benefit the retailer much

Combining information sharing with replenishment co-ordination would result in cost savings and inventory reduction for both the retailer and the supplier;

the magnitude of cost savings and inventory reductions associated with information sharing and replenishment co-ordination is significantly influenced by the underlying demand patterns

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Short Comings of Analytical Models

Usually, Simple Models to get insights† Simple Supply Chain Structure† Simple Demand Pattern† No cost considerations or inssuficent cost consi

derations† Limited Managerial Implications in term of cost,

service level etc

More Complicated Models? † Possible to formulate, but † Intractable to solve

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Bullwhip Effect: Simulation Models Metters (1997, JOM):

Impact of “bullwhip effects” on profitability based on generated demands of different variance

Johnson, Davis, and Waller (1996, JBL): Impact of VMI (Vendor Managed Inventory) on inventory level VMI reduced inventory for all participants without

compromising services No cost consideration

Boone, Ganeshan, and Stenger (2002, in: Supply Chain Management: Models, Applications, and Research Directions): Impact of CPFR via simulation

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The purpose of our study

– The value of information sharing and early order commitment (one kind of order coordination) under more realistic environments

– How will the supply chain parameters and demand patterns etc. influence the value of information sharing and early order commitment?

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Simulation Procedures• Research Design

– Basic models– Independent variables– Dependent variables

• Simulation– Program development (or selecting software)– Validation– Repetition numbers– Data analysis

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Research Design

• Basic Supply Chain Model

Retailer 1

Retailer 2

Retailer 3

Retailer 4

Supplier(capacitated)

DE

MA

ND

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Independent Variables Variable Number

Variable Name Label Number of Levels

Values

1 Demand Patterns DP 5 ICON,ISEA,ISIT,ISDT,

MIX

2 Capacity Tightness CT 3 Low, Medium, High

3 Natural Ordering

Cycles

T 3 2,4,8 periods

respectively

4 Unit Shortage Cost SC 3 Low, Medium, High

5 Information Sharing IS 3 NIS, DIS, OIS

6 EOC OC 5 0,5,10,15,20

7 Forecasting Models FM 3 NAV, SMA, SES

… …………..

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Demand Generators

()

)2

sin(

snormalnoise

teSeasonCycl

season

tslope

baseDemand t

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Retailer’s Demand PatternsCharacteristics of Demand Generators

Demand Generator base slope season noise

CON 1000.0 0 0 100

SEA 1000.0 0 200 100

SIT 551.0 2 200 100

SDT 1449.0 -2 200 100

• Average demand in simulation periods [50,350] = 1000

• Parameters should be changed if simulation periods changes

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Levels of Information Sharing (IS)

•NIS: No Information Sharing•DIS: Demand Information

Sharing (Share forecasted net requirements)

•OIS: Order Information Sharing (Share planned orders)

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Early Order Commitment (OC)

• The number of periods that retailers place order earlier based on their demand forecasts

• OC = 0,5,10,15,20 periods respectively

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Cost Structures for Supplier and Retailers

Supplier / Retailer Supplier Ret1 Ret2 Ret3 Ret4 Order Processing Cost

($/order) 1000

100 100 100 100

Transportation Cost ($/truck)

N/A 450 255 331 553

Natural Ordering Cycle (periods)

2, 4, 8 periods respectively

Unit Backorder Cost ($/unit/period)

10 (“Low”), 50(“Medium”), 250(“High”) times of

inventory cost per unit per period respectively

T * =Dh

K2

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Forecasts for Retailers

())1( 0 snormalttIRED

EB

DemandForecast tt

Forecasting error bias

EB 4 -50,0,+50,+100

Forecasting error deviation

ED 3 0,50,200

Increase rate IR 3 LIN, CVX, CCV

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Figure 1 Patterns of increasing rate for forecast deviation

0.00

2.00

4.00

6.00

8.00

10.00

12.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

PERIOD

Fo

reca

st

de

via

tio

n

Concave

Linear

Convex

Patterns of increasing rate for forecast deviation

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Conditions for Simulation Retailers' Forecasting Method (or forecasting errors)

Retailers' Inventory Policy: EOQ

Supplier's Production Decision: Capacitated Lot-sizing

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Dependent Variables

• Total cost for retailers (TCR)• Total cost for the supplier (TCS)• Total cost for the entire supply

chain (TC)– Excludes backorder cost of the

supplier

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Research Hypotheses (for example)

• Hypothesis 1: Forecasting error distribution will significantly influence supply chain performance. Higher forecasting errors (EB or ED) will result in a worse performance.

• Hypothesis 2: Forecasting error distribution will significantly influence the value of information sharing. Higher forecasting errors (EB or ED) will reduce the benefits of information sharing.

• Hypothesis 3: Demand pattern faced by the retailer will significantly moderate the impact of forecasting error distribution on the values of information sharing. When the demand has either an increasing or a decreasing trend, the forecasting error distribution will have a greater impact on supply chain performance and the value of information sharing.

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Simulation procedure

• Preparation

Generating demand, production capacity

• In each period

Forecast, order, shipment

• Collect data

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ANOVA (using SAS or other software) Preparation

• residual analysis

• transformation of performance measures

ANOVA

• significance check

• major effect

• interaction effect

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Selected Results

Hypothesis test etc.

(See papers for details)

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Future Research Directions Simulation

Other supply chains with more complicated structures

Other alternative methods of information sharing

Other alternative methods of order coordination

Other production and inventory policy Other demand patterns

Analytical models Impact of EOC on the system performance

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