INTERMITTENT DEMAND FORECASTING USING DATA MINING TECHNIQUES

Gamze Ogcu KAYA; Ali TURKYILMAZ

doi:10.23743/acs-2018-11

Open full text

pdf

Published: Jun 30, 2018

DOI: https://doi.org/10.23743/acs-2018-11

DOI

https://doi.org/10.23743/acs-2018-11

Authors

Gamze Ogcu KAYA

gamzeogcu@gmail.com

Sampoerna University, Department of Industrial Engineering, Jakarta

Ali TURKYILMAZ

ali.turkyilmaz@nu.edu.kz

Nazarbayev University, School of Engineering, Astana

Abstract

Intermittent demand occurs randomly with changing values and a lot of periods having zero demand. Ad hoc intermittent demand forecasting techniques have been developed which take special intermittent demand characteristics into account. Besides traditional techniques and specialized methods, data mining offers a better alternative for intermittent demand forecasting since data mining methods are powerful techniques. This study contributes to the current literature by showing the benefit of using data mining methods for intermittent demand forecasting purpose by comprising mostly used data mining methods.

Keywords:

ANN, support vector regressions, Intermittent Demand Forecasting

References

Bacchetti, A., & Saccani, N. (2012). Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice. Omega, 40, 722–737. DOI: https://doi.org/10.1016/j.omega.2011.06.008

Chau, K. W. (2006). A review on the integration of data mining into coastal modeling. Journal of Environmental Management, 80, 47–57. DOI: https://doi.org/10.1016/j.jenvman.2005.08.012

Croston, J. F. (1972). Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23, 289–304. DOI: https://doi.org/10.1057/jors.1972.50

Hoover, J. (2006). Measuring Forecast Accuracy: Omissions in Today’s Forecasting Engines and Demand-Planning Software. International Journal of Applied Forecasting, 1, 32–35.

Hua, Z., & Zhang, B. (2006). A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Applied Mathematics and Computation, 181, 1035–1048. DOI: https://doi.org/10.1016/j.amc.2006.01.064

Kennedy, W. J., Patterson, J. W., & Fredendall, L. D. (2002). An overview of recent literature on spare parts inventories. Int. J. of Production Economics, 76, 201–215. DOI: https://doi.org/10.1016/S0925-5273(01)00174-8

Mitchell, T. (1997). Machine Learning. Boston: McGraw-Hill.

Pandya, R., & Pandya, J. (2015). C5.0 Algorithm to Improved Decision Tree with Feature Selection and Reduced Error Pruning. International Journal of Computer Applications, 11, 718–21. DOI: https://doi.org/10.5120/20639-3318

Punjari, A. K. (2006). Data Mining Techniques. Universities Press Private Limited.

Rao, A. V. (1973). A comment on: forecasting and stock control for intermittent demands. Operational Research Society, 24, 639–640. DOI: https://doi.org/10.1057/jors.1973.120

Regattieri, A., Gamberi, M., Gamberini, R., & Manzini, R. (2005). Managing Lumpy Demand for Aircraft Spare Parts. Journal of Air Transport Management, 11, 426–431. DOI: https://doi.org/10.1016/j.jairtraman.2005.06.003

Syntetos, A. A. (2007). A note on managing lumpy demand for aircraft spare parts. Journal of Air Transport Management, 13, 166–167. DOI: https://doi.org/10.1016/j.jairtraman.2007.01.002

Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York: Springer. DOI: https://doi.org/10.1007/978-1-4757-2440-0

KAYA, G. O., & TURKYILMAZ, A. (2018). INTERMITTENT DEMAND FORECASTING USING DATA MINING TECHNIQUES. Applied Computer Science, 14(2), 38–47. https://doi.org/10.23743/acs-2018-11

Article Sidebar

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License