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Dejonckheere, J.; Disney, Stephen Michael; Lambrecht, M. R.; Towill, Denis Royston (2004)
Publisher: Elsevier
Languages: English
Types: Article
Subjects: HD28, H1, HD, HF
This paper examines the beneficial impact of information sharing in multi-echelon supply chains. We compare a traditional supply chain, in which only the first stage in the chain observes end consumer demand and upstream stages have to base their forecasts on incoming orders, with an information enriched supply chain where customer demand data (e.g. EPOS data) is shared throughout the chain. Two types of replenishment rules are analysed: order-up-to (OUT) policies and smoothing policies (policies used to reduce or dampen variability in the demand). For the class of OUT policies, we will show that information sharing helps to reduce the bullwhipeffect (variance amplification of ordering quantities in supply chains) significantly, especially at higher levels in the chain. However, the bullwhip problem is not completely eliminated and it still increases as one moves up the chain. For the smoothing policies, we show that information sharing is necessary to reduce order variance at higher levels of the chain.\ud \ud The methodology is based on control systems engineering and allows us to gain valuable insights into the dynamic behaviour of supply chain replenishment rules. We also introduce acontrolengineering based measure to quantify the variance amplification (bullwhip) or variance reduction.
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    • Adelson, R.M., 1966. The dynamic behaviour of linear forecasting and scheduling rules, Operational Research Quarterly 17 (4) 447-462.
    • Berry, D., Naim, M.M. and Towill, D.R., 1995. Business Process Re-Engineering an Electronics Products Supply Chain, Proceedings of IEE Science, Measurement, and Technology, 142 (5), 395-403.
    • Bertrand J.W.M., 1986. Balancing production level variations incomplex production systems, International Journal of Production Research, 24 (5) 1059-1074.
    • Chen F., Drezner, Z., Ryan, J., Simchi-Levi, D., 2000b. Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information, Management Science 46 (3) 436-443.
    • Chen, F., Ryan, J., Simchi-Levi, D., 2000a. The impact of exponential smoothing forecasts on the bullwhip effect, Naval Research Logistics 47 (4) 271-286.
    • Dejonckheere, J. Disney, S.M., Lambrecht. M.R and Towill, D.R., 2001, Measuring the Bullwhip Effect: a control theoretic approach to analyse forecasting induced Bullwhip in order-up-to policies. Forthcoming in the European Journal Of Operational Research, 2001.
    • Dejonckheere, J., Disney, S.M., Farasyn, I., Janssen, F., Lambrecht, M., Towill, D.R. and Van de Velde, W., 2002. Production and Inventory Control: The variability trade-off. Proceedings of the 9th EUROMA Conference, June 2-4, Copenhagen, Denmark, ISBN 1 85790 088X.
    • Deziel, D.P. and Eilon, S.,1967, A linear production-inventory control rule. The Production Engineer 43 93-104.
    • Disney, S.M. 2001. The production and inventory control problem in Vendor Managed Inventory supply chains. PhD. Thesis, Cardiff University, UK.
    • Disney, S.M. and Towill, D.R., 2001a. A discrete transfer function model to determine the dynamic stability of a Vendor Managed Inventory supply chain. International Journal of Production Research 40 1 179-204.
    • Disney, S.M. and Towill, D.R., 2002. A robust and stable analytical solution to the production and inventory control problem via a z-transform approach. Proceedings of the 12th International Working Conference on Production Economics, Igls, Austria, 18- 22 February, Vol. 1, pp37-47, ISSN 0925 5273.
    • Disney, S.M., Naim, M.M. and Towill, D.R., 1997, Dynamic simulation modelling for lean logistics. International Journal of Physical Distribution and Logistics Management, 27 (3) 174-96.
    • Disney, S.M. and Towill, D.R., 2001b. A procedure for the optimization of the dynamic response of a Vendor Managed Inventory system. Computers and Industrial Engineering 43 1 7-58
    • Forrester, J., 1961, Industrial dynamics. (Cambridge MA, MIT Press).
    • Forrester, J., 1958, Industrial dynamics, a major breakthrough for decision makers. Harvard Business Review July-August 67-96.
    • Fransoo, J.C, and Wouters, M.J.F., 2000. Measuring the Bullwhip Effect in the Supply Chain. International Journal of Supply Chain Management, 5 (2) 78-89.
    • Garnell, P. and East, D.J., 1977. Guided weapon control systems. (Pergamon Press, Oxford).
    • Holmström, J., 1997, Product range management: a case study of supply chain operations in the European grocery industry. Supply Chain Management 2 (3) 107-115.
    • Holt, C.C., 1957. Forecasting seasonals and trends by exponentially weighted moving averages”, ONR memorandum, Carnegie Institute of Technology, Pittsburgh, Pennsylvania, 52.
    • John S., Naim M.M., Towill D.R., 1994. Dynamic analPysiscomofpenasateWd I decision support systemI.nternational Journal Manufacturing System Design, 1 (4) 283-297.
    • Lee, H.L., Padmanabhan, V., Whang, S., 1997a. The Bullwhip effect in supply chains. Sloan Management Review Spring 93-102.
    • Lee, H.L., Padmanabhan, V., Whang, S., 1997b. Information Distortion in a Supply Chain: The Bullwhip Effect. Management Science 43 (4) 546-558.
    • Lee, H.L., So, K.C., Tang, C.S., 2000. The value of information sharing in a two level supply chain. Management Science 46 (5) 628-643.
    • Makridakis, S., Wheelwright, S.C., McGee, V.E. 1978. Forecasting: methods and applications. (John Wiley & Sons).
    • Mason-Jones, R. and Towill, D.R. 1997. Information Enrichment: Designing the supply chain for competitive advantage. International Journal of Supply Chain Management 2 (4) 137-148.
    • Mason-Jones, R., 1998. The holistic strategy of market information enrichment through the supply chain. PhD Thesis, Cardiff University, UK.
    • Metters, R., 1997. Quantifying the bullwhip effect in supply chains. Journal of Operations Management 15 (2) 89-100.
    • Nise, N.S., 1995. Control systems engineering. (The Benjamin/ uCmmings Publishing Company, Inc., California).
    • Shannon, C.E., Oliver, B.M. and Pierce, J.R., 1948. The philopshoy of Pulse Code Modulation. Proceedings of the IRE 36 (11) 1324-1331.
    • Simon, H.A., 1952. On the application of servomechanism theory to the study of production control. Econometrica 20 247-268.
    • Sterman, J., 1989. Modelling managerial behaviour: Misperceptions of feedback in a dynamic decision making experiment. Management Science 35 (3) 321-339.
    • Suzaki, K, “The New Manufacturing Challenge”, 1987, The Free Press, New York.
    • Towill D.R. and McCullen, P., 1999. The impact of an agile manufacturing programme on supply chain dynamics. International Journal Logistics Management, 10 (1) 83-96.
    • Towill D.R., 1970. Transfer function techniques for control engineers. (Iliffe Books, London).
    • Towill D.R., 1982. Dynamic analysis of an inventory and order based production control system. International Journal of Production Research 20 369-383.
    • Towill, D.R., 1999. Fundamental theory of bullwhip induced by exponential smoothing algorithm. MASTS Occasional Paper No. 61, Cardiff University.
    • Van Ackere, A., Larsen, E.R. and Morecroft, J.D.W., 1993. Systems thinking and business process redesign: An application to the Beer Game. European Management Journal 11 (4) 412-423.
    • Van Aken, J.E. 1978. On the Control of Complex Industrial Organisations. (Martinus Nijhoff (Social Sciences Division), London).
    • Vassian, H.J., 1955. Application of discrete variable servo theory to inventory control. Operations Research 3 272-282.
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