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

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The Value of Information Sharing and Early Order
Commitmentin 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.

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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 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

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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
  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

DEMAND
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Independent Variables
..
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Demand Generators
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Retailers Demand Patterns

• 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
T
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Forecasts for Retailers
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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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• Thank you very much for your attendance!

• THE END