A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts

Owing to demand characteristics of spare part, demand forecasting for spare parts is especially difficult. Based on the properties of spare part demand, we develop a hybrid forecasting approach, which can synthetically evaluate autocorrelation of demand time series and the relationship of explanatory variables with demand of spare part. In the described approach, support vector machines (SVMs) are adapted to forecast occurrences of nonzero demand of spare part, and a hybrid mechanism for integrating the SVM forecast results and the relationship of occurrence of nonzero demand with explanatory variables is proposed. Using real data sets of 30 kinds of spare parts from a petrochemical enterprise in China, we show that our method produces more accurate forecasts of distribution of lead-time demands of spare parts than do current methods across almost all the lead times.

[1]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[2]  J. B. Ward.  Determining Reorder Points When Demand is Lumpy , 1978 .

[3]  Ralph D. Snyder,et al.  Control of inventories with intermittent demand , 1989 .

[4]  J. D. Croston.  Forecasting and Stock Control for Intermittent Demands , 1972 .

[5]  W. J. Kennedy,et al.  An overview of recent literature on spare parts inventories , 2002 .

[6]  James H. Bookbinder,et al.  Estimation of Inventory Re-Order Levels Using the Bootstrap Statistical Procedure , 1989 .

[7]  C. E. Lemke,et al.  Bimatrix Equilibrium Points and Mathematical Programming , 1965 .

[9]  Edward A. Silver,et al.  Operations Research in Inventory Management: A Review and Critique , 1981, Oper. Res..

[10]  T. Williams.  Stock Control with Sporadic and Slow-Moving Demand , 1984 .

[11]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[12]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[13]  J. Yang,et al.  A new approach of forecasting intermittent demand for spare parts inventories in the process industries , 2007, J. Oper. Res. Soc..

[14]  Wen Wang,et al.  Forecasting intermittent demand by SVMs regression , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[15]  Cletus R. Mercier,et al.  Age and Gender as Predictors of Injury Severity in Head-on Highway Vehicular Collisions , 1997 .

[16]  I. Grosse,et al.  MEASURING CORRELATIONS IN SYMBOL SEQUENCES , 1995 .

[17]  Ping-Feng Pai,et al.  A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .

[18]  T. Willemain,et al.  Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method , 1994 .

[19]  Bernhard Schölkopf,et al.  Extracting Support Data for a Given Task , 1995, KDD.

[20]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[21]  Adel A. Ghobbar,et al.  Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model , 2003, Comput. Oper. Res..

[22]  Min-Chiang Wang,et al.  Estimating reorder points and other management science applications by bootstrap procedure , 1992 .

[23]  C. R. Mitchell,et al.  An Analysis of Air Force EOQ Data with an Application to Reorder Point Calculation , 1983 .

[24]  Lutgarde M. C. Buydens,et al.  Using support vector machines for time series prediction , 2003 .

[25]  Uttarayan Bagchi.  Modeling lead‐time demand for lumpy demand and variable lead time , 1987 .

[26]  T. Willemain,et al.  A new approach to forecasting intermittent demand for service parts inventories , 2004 .

[27]  A. Vijaya Rao,et al.  A Comment on: Forecasting and Stock Control for Intermittent Demands , 1973 .

[28]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[29]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[30]  A. S. Al-Ghamdi.  Using logistic regression to estimate the influence of accident factors on accident severity. , 2002, Accident; analysis and prevention.

[31]  Brian G. Kingsman,et al.  Selecting the best periodic inventory control and demand forecasting methods for low demand items , 1997 .

[32]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..

[33]  J. Boylan,et al.  Forecasting for Items with Intermittent Demand , 1996 .