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1
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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