The Value of Information Sharing and
Early Order Commitment
in Supply Chains: Simulation Studies
Jinxing Xie
Dept. of Mathematical Sciences
Tsinghua University, Beijing 100084, China
Co-works with
Xiande Zhao
Dept. of Decision Sciences & Managerial Economics
Faculty of Business Administration
The Chinese University of Hong Kong, Hong Kong, China
et. al.
1
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.
2
Outline
• Motivation
• Simulation Procedures
• ANOVA
• Some Results
• Summary
3
Motivation
• Simulation in MRP
Impact of Lot-sizing rules
Impact of freezing MPS parameters
• Can Simulation Methodology be used to
SCM Researches?
4
Bullwhip Effect: Analytical Models
Lee, Padmanabhan, and Whang (1997):
– "bullwhip effects" and causes
– Four sources of the bullwhip effect:
• Demand signal processing
• Rationing game
• Order batching
• Price variation
5
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
considerations
† 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 3
DEMAND
Supplier
(capacitated)
Retailer 2
Retailer 4
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Independent Variables
Variable
Number
1
Variable Name
Demand Patterns
Label Number
of Levels
DP
5
Values
ICON,ISEA,ISIT,ISDT,
MIX
2
Capacity Tightness
CT
3
Low, Medium, High
3
Natural Ordering
T
3
2,4,8 periods
Cycles
…
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
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Forecasting Models
FM
3
NAV, SMA, SES
…………..
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Demand Generators
Demand t  base 
slope  t 
2
season  sin(
 t) 
SeasonCycle
noise  snormal ()
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Retailer’s Demand Patterns
Characteristics 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
Order Processing Cost
($/order)
Transportation Cost
($/truck)
Natural Ordering Cycle
(periods)
Unit Backorder Cost
($/unit/period)
T*=
Supplier
1000
Ret1
100
Ret2
100
Ret3
100
Ret4
100
N/A
450
255
331
553
2, 4, 8 periods respectively
10 (“Low”), 50(“Medium”), 250(“High”) times of
inventory cost per unit per period respectively
2K
Dh
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Forecasts for Retailers
Forecast t  Demand t
 EB
 ED  IR (t  t0  1)  snormal ()
Forecasting error EB
bias
Forecasting error ED
deviation
Increase rate
IR
4
-50,0,+50,+100
3
0,50,200
3
LIN, CVX, CCV
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Patterns of increasing rate for
forecast deviation
Figure 1 Patterns of increasing rate for forecast deviation
12.00
10.00
Forecast deviation
8.00
Concave
6.00
Linear
Convex
4.00
2.00
0.00
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
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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 23
value of information sharing.
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
25
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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