Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31903
A Hybrid Neural Network and Traditional Approach for Forecasting Lumpy Demand

Authors: A. Nasiri Pour, B. Rostami Tabar, A.Rahimzadeh


Accurate demand forecasting is one of the most key issues in inventory management of spare parts. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of the forecasting methods may perform poorly when demand for an item is lumpy. In this study based on the characteristic of lumpy demand patterns of spare parts a hybrid forecasting approach has been developed, which use a multi-layered perceptron neural network and a traditional recursive method for forecasting future demands. In the described approach the multi-layered perceptron are adapted to forecast occurrences of non-zero demands, and then a conventional recursive method is used to estimate the quantity of non-zero demands. In order to evaluate the performance of the proposed approach, their forecasts were compared to those obtained by using Syntetos & Boylan approximation, recently employed multi-layered perceptron neural network, generalized regression neural network and elman recurrent neural network in this area. The models were applied to forecast future demand of spare parts of Arak Petrochemical Company in Iran, using 30 types of real data sets. The results indicate that the forecasts obtained by using our proposed mode are superior to those obtained by using other methods.

Keywords: Lumpy Demand, Neural Network, Forecasting, Hybrid Approach.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2481


[1] D. C Montgomery, L.A.J and S.J Gardiner, Forecasting and Time series Analysis, Mc Graw-Hill, 1990.
[2] Makridakis Spyros, S. C. W., Victor E.McGee, Forecasting: Methods and Applications, John Wiley and Sons, 1983.
[3] R. V Bartezzaghi., G Zotteri , "A simulation framework for forecasting uncertain lumpy demand", International of Journal of Production Economics, vol.59, 1999, pp.499-510.
[4] A. Dolgui.and M. pashkevich, "Extended beta-binomial model for demand forecasting of multiple slow-moving items with low consumption and short requests history", 2005, Research report.
[5] J.E Boylan, "Intermittent and Lumpy Demand: a Forecasting Challenge", Foresight, International Journal of Applied Forecasting 1(1), 2005, pp.36-42.
[6] A.A Syntetos, J.E Boylan, "on the bias of intermittent demand estimates", International Journal of Production Economics, vol.71, 2001, pp.457-466.
[7] A.A Syntetos, J.E Boylan, "the accuracy of intermittent demand estimates", International Journal of Forecasting, vol.21, 2005, pp.303- 314.
[8] M. Kalchschmidt, G. Zotteri, R. Verganti, "Inventory management in a multi-echelon spare parts supply chain", International Journal of Production Economics vol.81-82, 2003, pp.397-413.
[9] A.A Ghobbar, C. H. Friend, "Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive mode", Computers & Operations Research, vol.30, 2003, pp.2097-2114.
[10] Z.S Hua, B. Zhang, J Yang and D.S Jan Tan "A new approach of forecasting intermittent demand for spare parts inventories in the process industries", Journal of Operational Research Society, vol.58, 2007, pp.52-61.
[11] J.D Croston, "Forecasting and Stock Control for Intermittent Demand", Operational Research Quarterly, vol.23, no.3, 1972, pp.289-303.
[12] W. Thomas, C. N. Smart., H. F. Schwarz , "A new approach to forecasting intermittent demand for service parts inventories", International Journal of Forecasting, vol.20, 2004, pp.375- 387.
[13] E. Leve'n, A. Segerstedt, "Inventory control with a modified Croston procedure and Erlan distribution", International Journal of Production Economics, vol.90, 2004, pp.361-367.
[14] J.E Boylan and A.A Syntetos, "the accuracy of a modified Croston procedure", International Journal of Production Economics vol.107, 2007, pp.511-517.
[15] A.H.C Eaves, B.G Kingsman, "Forecasting for the ordering and stockholding of spare parts", 2004, Journal of the Operational Research Society, vol.55, pp.431-437.
[16] A.A Syntetos, J.E Boylan and J.D Croston, "on the categorization of demand patterns", Journal of the Operational Research Society, vol.56, 2005a, pp.495-503.
[17] AA. Syntetos, J.E Boylan, "On the stock control performance of intermittent demand estimators", International Journal of Production Economics, vol.103, 2006, pp. 36-47.
[18] N Altay, F Rudisill, L Litteral, 2007, "Adapting Wright-s modification of Holt-s method to forecasting intermittent demand", International Journal of Production Economic, vol.111, 2008, pp.389-408.
[19] J Carmo, A. J Rodriguez, "Adaptive forecasting of irregular demand processes", Engineering Applications of Artificial Intelligence, vol. 17, 2004, pp.137-143.
[20] R.S Gutierrez, A.O Solis and S. Mukhopadhyay, "Lumpy demand forecasting using neural networks", International Journal of Production Economic, vol.111, 2008, pp.409-420
[21] M.R Amin-Naseri, B.Rostami tabar and B.Ostadi, "Generalized regression neural network in modeling lumpy demand." Presented at the 2007 8th international conference on operations and quantitative management, Bangkok, Thailand
[22] M.R Amin-Naseri, B.Rostami tabar, "Neural network approach to lumpy demand forecasting or spare parts in process industries." Presented at 2008 international conference on computer and communication engineering, kuala lumpur, Malaysia.
[23] D.F Specht, "a general regression neural network", IEEE Trans Neural Network, vol.2, no.6, 1991, pp. 568-76.
[24] J.L Elman, D. Zipser, "Learning the hidden structure of speech", Institute of Cognitive Science Report 8701, 1987, UC San Diego.
[25] R.J Hyndman, "Another Look at Forecast-Accuracy Metrics for Intermittent Demand", Foresight, International Journal of Applied Forecasting, 1(4), 2006, pp.43-46.
[26] J. Hoover, "Measuring Forecast Accuracy: Omissions in Today-s Forecasting Engines and Demand-Planning Software", Foresight, International Journal of Applied Forecasting, 1(4), 2006, pp.32-35.
[27] AA. Syntetos, J.E Boylan, "forecasting for inventory management of service parts", Chapter 20. To appear in 2007: In (eds: Kobbacy, K.A.H. and Murthy, D.N.P.) Complex System Maintenance Handbook, Springer.