1. 1 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.

2. 2 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.

3. 3 Outline Motivation Simulation Procedures ANOVA Some Results Summary

4. 4 Motivation Simulation in MRP  Impact of Lot-sizing rules  Impact of freezing MPS parameters Can Simulation Methodology be used to SCM Researches?

5. 5 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

6. 6 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

7. 7 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

8. 8 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

9. 9 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

10. 10 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?

11. 11 Simulation Procedures Research Design –Basic models –Independent variables –Dependent variables Simulation –Program development (or selecting software) –Validation –Repetition numbers –Data analysis

12. 12 Research Design Basic Supply Chain Model Retailer 1 Retailer 2 Retailer 3 Retailer 4 Supplier (capacitated) DEMAND

13. 13 Independent Variables … …………..

14. 14 Demand Generators

15. 15 Retailer’s Demand Patterns Average demand in simulation periods [50,350] = 1000 Parameters should be changed if simulation periods changes

16. 16 Levels of Information Sharing (IS) NIS: No Information Sharing DIS: Demand Information Sharing (Share forecasted net requirements) OIS: Order Information Sharing (Share planned orders)

17. 17 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

18. 18 Cost Structures for Supplier and Retailers T * =

19. 19 Forecasts for Retailers Forecasting error bias EB4-50,0,+50,+100 Forecasting error deviation ED30,50,200 Increase rateIR3LIN, CVX, CCV

20. 20 Patterns of increasing rate for forecast deviation

21. 21 Conditions for Simulation  Retailers' Forecasting Method (or forecasting errors)  Retailers' Inventory Policy: EOQ  Supplier's Production Decision:  Capacitated Lot-sizing

22. 22 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

23. 23 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.

24. 24 Simulation procedure Preparation Generating demand, production capacity In each period Forecast, order, shipment Collect data

25. 25 ANOVA (using SAS or other software) Preparation residual analysis transformation of performance measures ANOVA significance check major effect interaction effect

26. 26 Selected Results Hypothesis test etc. (See papers for details)

27. 27 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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