A Hybrid Neural Network and Traditional Approach for Forecasting Lumpy Demand
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:59845", author = "A. Nasiri Pour and B. Rostami Tabar and A.Rahimzadeh", title = "A Hybrid Neural Network and Traditional Approach for Forecasting Lumpy Demand", abstract = "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.", volume = "2", number = "4", pages = "1233-7", }
