How RHF contracts alter demand information

• Autorzy: Walsh P. M. , Williams P. A. , Heavey C.
• Języki publikacji: EN

Abstrakty

EN

A discrete-event simulation model of a supply chain has been developed to evaluate operational performance of sharing uncertain information on upcoming demand between an Original Equipment Manufacturer (OEM) and a Contract Manufacturer (CM) under a formal Rolling Horizon Flexibility (RHF) contract in a four node supply chain. There are two types of RHF contracts evaluated, that is, RHF contract with constant flexibility and decreasing flexibility bounds. The demand is externalised (that is, the OEM receives the demand), stochastic and is generated according to the gamma distribution. This paper reports on analysis of RHF contracts operating with coefficients of variation (CV) of demand up to 2.00 while comparing the supply chain performances with a no contract situation. Analysis on the way RHF contracts alter the demand information transferred between a buyer (OEM) and supplier (CM) are show here.

Słowa kluczowe

EN

simulation; discrete-event; performance analysis; coefficient of variation; bullwhip; RHF contract

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2007
• Tom: Vol. 33, no 1
• Strony: 61--79
• Opis fizyczny: Bibliogr. 33 poz., rys.

Twórcy

autor

Walsh P. M.

autor

Williams P. A.

autor

Heavey C.

• Department of Manufacturing and Operations Engineering, Enterprise Engineering Research Group, University of Limerick,

Bibliografia

• [1] Anupindi R., Bassok Y., Approximations for mitltiproduct contracts with stochastic demands and business volume discounts: single supplier case, IIE Trans., 30(8), 1998, pp. 723-734.
• [2] Bassok Y., Anupindi R., Analysis of Supply Contracts with Commitments and Flexibility, Seattle, School of Business Administration, Department of Management Sciences University of Washington, 1998.
• [3] Bassok Y., Bixby A., Srinivasan R., Wiesel H.Z., Design of component-supply contract commitment-revision flexibility, IBM Journal of Research and Development, 41(6), 1999.
• [4] Burnetas A.. Gilbert S.M., Smith C., Quantity Discounts in Single Period Supply Contracts with Asymmetric Demand Information, SCMEC, University of Florida. 2004.
• [5] Byrne P.J., Supply Chain Analysis of a Vertically Integrated Raw Material Supplier using Simulation, Manufacturing and Operations Engineering Department, Limerick, University of Limerick, 2004, p. 219.
• [6] Chase R.B., Jacobs F.R., Aquilano N.J., Operations Management for Competitive Advantage with Student-CD, McGraw-Hill/Irwin, 2003.
• [7] Chen F., Drezner Z., Smchi-Levi D., Quantifying the Bull-whip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times and Information, Management Science. 46(3), 2000, pp. 436-443.
• [8] Chen F., Ryan J.K., Simchi-Levi D., The Impact of Exponential Smoothing Forecasts on the Bullwhip Effect, Naval Research Logistics. 47(4), 1999, pp. 269-286.
• [9] Chen F., Song J.-S., Optimal Policies for Multiechelon Inventory Problems with Markov-Modulated Demand, Operations Research. 49(2), 2001, pp. 226-234.
• [10] Chopra S., Meindl P., Supply Chain Management. Strategy, Planning and Operation, Prentice Hall, Harlow 2001.
• [11] Graves S.C., A Single-Item Inventory Model for a Non-Stationary Demand Process, Cambridge, Manufacturing Program and A.P. Sloan School of Management Massachusetts Institute of Technology Cambridge, 1998, pp. 1-41.
• [12] Gullu R., A two-echelon allocation model and the value of information under correlated forecasts and demands, European Journal of Operational Research, 99, 1997, pp. 386-400.
• [13] Handfield R.B., Nichols E.L., Supply Chain Redesign Transforming Supply Chains into Integrated Value Systems, Prentice Hall, 2002.
• [14] Helsel D.R., Hirsch R.M., Statistical methods in water resources. Amsterdam; New York: Elsevier, 1993.
• [I5] Hopp W., Spearman M.L.. Factory Physics Foundations of Manufacturing Management, McGraw-Hill. 1996.
• [16] Huang G.Q., Lau J.S.K., Mak K.L., The impacts of sharing production information on supply chain dynamics: a review of literature, International Journal of Production Research. 41(7), 2003, pp. 1483-1517.
• [17] Keaton M., Using The Gamma Distribution To Model Demand When Lead Time Is Random, Journal of Business Logistics, 16(1), 1995, pp. 107-131.
• [18] Kelly M., Boulton W., Bergman D., Chadha B.. Cheng H.M., Kong C.W., Naclerio N., Pecht M., Ranicri J., Roberts G.. Sanyal A., Tucker W., Vardaman J. Wennberg S., Electronics manufacturing in the pacific rim. World Technology Evaluation Center, 1997.
• [19] Lee H.L.. Padmanabhan V., Whang S., The Bullwhip Effect in Supply Chains, Sloan Management Review, 1997. pp. 93-102.
• [20] Monden Y., Toyota Production System An Integrated Approach to Just-In-Time, Norcross, Engineers and Management Press, 1998.
• [21] Monks J.G.. Theory and Problems of Operations Management, McGraw-Hill Book Company, 1985.
• [22] Morton T.E., Pentico D.W., Heuristic Scheduling Systems, with Applications to Production Systems and Project Management, John Wiley & Sons. Inc., 1993.
• [23] Simchi-Lcvi D., Kaminsky P., Simchi-Levi E., Designing and Managing the Supply Chain, Illinios, Irwin McGraw-Hill, 2000.
• [24] Sipper D., Bulfin R. Jr., Production Planning, Control, and Integration, McGraw Hill, 1997.
• [25] Stadtler H.. Kilger C., Supply Chain Management and Advanced Planning, Springer, 2000.
• [26] Tayur S., Ganeshan R., Magazine M., Quantitative Models for Supply Chain Management, 1999.
• [27] Tecnomatix, eM-Plant User Guide, 2003.
• [28] Tsay A., The Quantity Flexibility Contract and Supplier-Customer Incentives, Management Science, 45(10), 1999, pp. 1339-1358.
• [29] Tsay A., Lovejoy W.S., Quantity Flexibility Contracts and Supply Chain Performance, Manufacturing & Service Operations Management, 1(2), 1999, pp. 89-111.
• [30] Tsay A.. Nahmias S., Agrawal N., Modeling Supply Chain Contracts: A Review. Quantitative Models for Supply Chain Management, S. Tayur, R. Ganeshan, M. Magazine, Kluwer Academic Publishers, 1999, pp. 299-336.
• [31] Wilkinson L., Statistical Methods in Psychology Journals Guidelines and Explanations, American Psychologist, 54(8), 1999, pp. 594-604.
• [32] Womack J.P., Jones D.T., Roos D., The Machine that Changed the World, Macmillan Publishing Company, 1990.
• [33] Zotteri G., The impact of distributions of uncertain lumpy demand on inventories. Production Planning & Control, 11(1), 2000, pp. 32-33.