The research of the Bullwhip effect has given rise to many papers, aimed at both analysing its causes and correcting it by means of various management strategies because it has been considered as one of the critical problems in a supply chain. This study is dealing with one of its principal causes, demand forecasting. Using different simulated demand patterns, alternative forecasting methods are proposed, that can reduce the Bullwhip effect in a supply chain in comparison to the traditional forecasting techniques (moving average, simple exponential smoothing, and ARMA processes). Our main findings show that kernel regression is a good alternative in order to improve important features in the supply chain, such as the Bullwhip, NSAmp, and FillRate.
Forrester, J.: Industrial Dynamics. MIT Press, Cambridge, MA, 1961
Towill, D. R.: Industrial Dynamics Modelling of Supply Chains. International Journal of Physical Distribution and Logistics Management, Vol. 26, 1996, pp. 23-42
Metters, R.: Quantifying the Bullwhip Effect in Supply Chains. Journal of Operations Management, Vol. 15, 1997, pp. 89-100
Lee, H. L., Padmanabhan, V., Whang, S.: The Bullwhip Effect in supply chains. Sloan Management Review, Vol. 38, No. 3, 1997, pp. 93–102
McCullen, P., Towill, D. R.: Diagnosis and Reduction of Bullwhip in Supply Chains. Supply Chain Management, an International Journal, Vol. 7, No. 3, 2002, pp. 164-179
Chatfield, D. C., Kim, J. G., Harrison, T. P., Hayya, J. C.: The Bullwhip effect-Impact of Stochastic Lead Time, Information Quality, and Information Sharing, A simulation Study. Production and Operations Management, Vol. 13, No. 4, 2004, pp. 340-353
Hosoda, T., Disney, S. M.: An analysis of a three echelon supply chain model with minimum means squared error forecasting. Second World Production and Operations Management Conference, Cancun, Mexico, April 30th - May 3rd, 2004
Wright, D., Yuan, X.: Mitigating the bullwhip effect by ordering policies and forecasting methods. International Journal of Production Economics, Vol. 113, 2008, pp. 587– 597
Campuzano, F., McDonnel, L., Lario, F. C.: Bullwhip effect consequences according to different supply chain management strategies: modeling and simulation. Journal of Quantitative Methods for Economics and Business Administration, Vol. 5, 2008, pp. 49-66
Campuzano, F., Mula, J., Peidro, D.: Fuzzy estimations and system dynamics for improving supply chains. Fuzzy Sets and Systems, Vol. 161, No. 11, 2010, pp. 1530-1542
Campuzano, F., Mula, J.: Supply Chain Simulation: A System Dynamics Approach for Improving Performance. Springer, 2011
Campuzano, F., Guillamón, A., Lisec, A.: Assessing the impact of prices fluctuation on demand distortion within a multiechelon supply chain. Promet Traffic and Transportation, Vol. 23, No. 2, 2011, pp. 131-140
Deziel, D. P., Elion, S.: A linear production–inventory control rule. The Production Engineer, Vol. 43, 1967, pp. 93–104
Sterman, J.: Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment. Management Science, Vol. 35, No. 3, 1989, pp. 321-339
Lee, H. L., Padmanabhan, V., Whang, S.: Information distortion in a supply chain: the bullwhip effect. Management science, Vol. 43, No. 4, 1997, 543-558
Lee, H.L., So, K.C., Tang, C.S.: The value of information sharing in a two-level supply chain. Management Science Vol. 46, No. 5, 2000, pp 626-643
Disney, S. M., Towill, D. R.: On the bullwhip and the inventory variance produced by an ordering policy. Omega, Vol. 31, 2003, pp. 157–167
Dejonckheere, J., Disney, S. M., Lambrecht, M. R., Towill, D. R.: The impact of information enrichment on the bullwhip effect in supply chains: A control engineering Perspective. European Journal of Operational Research, Vol. 153, 2004, 727-750
Ouyang, Y.: The effect of information sharing on supply chain stability and the bullwhip effect. European Journal of Operational Research, Vol. 182, No. 3, 2007, pp. 1107-1121
Mula, J., Poler, R., Garcia, J. P.: Capacity and material requirement planning modeling by comparing deterministic and fuzzy models. International Journal of Production Research, Vol. 46, 2008, pp. 5589-5606
Kastsian, D., Monnigmann, M.: Optimization of a vendor managed inventory supply chain with guaranteed stability and robustness. International Journal of Production Economics, Vol. 131, No. 2, 2011, pp. 727-735
Chen, F., Drezner, Z., Ryan, J. K., Simchi-Levi, D.: Quantifying the bullwhip effect in a simple supply chain. Management Science, Vol. 46, No. 3, 2000, pp. 436–443
Chen, F., Drezner, Z., Ryan, J. K., Simchi-Levi, D.: The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics, Vol. 47, 2000, pp. 269–286
Alwan, L. C., Liu, J. J., Yao, DQ.: Stochastic characterization of upstream demand processes in a supply chain. IIE Transactions, Vol. 35, 2003, pp. 207-219
Zhang, X.: The impact of forecasting methods on the bullwhip effect, International Journal of Production Economics, Vol. 88, 2004, pp. 15-27
Sun, H. X., Ren, Y. T.: The Impact of Forecasting Methods on Bullwhip Effect in Supply Chain Management. Proceedings of the 2005 Engineering Management Conference 1, 2005, pp. 215- 219
Stamatopoulos, I., Teunter, RH., Fildes, R. A.: The impact of forecasting on the bullwhip effect. The Department of Management Science, Lancaster University. Working Paper 2006/016, 2006
Chaharsooghi, SK., Faramarzi, H., Heydari, J.: A simulation study on the impact of forecasting methods on the bullwhip effect in the supply chain. IEEE International Conference on Industrial Engineering and Engineering Management, Singapore 8–11, 2008, pp. 1875–1879
Bartezzaghi, E., Verganti, R., Zotteri, G.: Measuring the impact of asymmetric demand distributions on inventories. International Journal of Production Economics, Vol. 61, 1999, pp. 395-404
John, S., Naim, M. M., Towill, D. R.: Dynamic analysis of a WIP compensated decision support system. International Journal of Manufacturing Systems Design, Vol. 1, No.4, 1994, pp. 283–297
Silver, E. A., Pyke, D. F., Peterson, R.: Inventory Management and Production Planning and Scheduling. Wiley, 1998
McKenzie, E.: General exponential smoothing and the equivalent ARIMA process. Journal of Forecasting, Vol. 3, 1984, pp. 333-334
Assimakopoulos, V., Nikolopoulos, K.: The theta model: a decomposition approach to forecasting. International Journal of Forecasting , Vol. 16, 2000, pp. 521-530
Hyndman, R. J., Billah, B.: Unmasking the Theta method. International Journal of Forecasting, Vol. 19, 2003, 287-290
Nadaraya, E. A.: Nonparametric Estimation of Probability Densities and Regression Curves. Kluwer Academic Publishers. Dordretch, 1989
Watson, G. S.: Smooth regression analysis. Sankhya Series A, Vol. 26, 1964, pp. 359–372
Ruppert, D., Sheather, S. J., Wand, M. P.: An effective bandwidth selector for local least squares regression. Journal of the American Statistical Association, Vol. 90, 1995, pp. 1257–1270
Hyndman, R. J., Khandakar, Y.: Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software 27:3, 2008, http://www.jstatsoft.org/v27/i03 Accessed 05 November 2011
Herrmann, E.: Kernel Regression Smoothing with Local or Global Plug-in Bandwidth. Lokern R package, 2010, http://cran.r-project.org/web/packages/lokern Accessed 05 November 2011
Fransoo, M., Wouters, J. F.: Measuring the bullwhip effect in the supply chain, in: J. C. Bradford, ed. Supply Chain Management, Vol. 5, No. 2, 2000, pp.78-89
Zipkin, P. H.: Foundations of Inventory Management, McGraw-Hill, New York, 2000
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