Intermittent demand forecasting using data mining techniques

• Autorzy: Kaya Gamze Ogcu , Turkyilmaz Ali

Identyfikatory

DOI

10.23743/acs-2018-11

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

ANN; support vector regression; Intermittent Demand Forecasting

PL

ANN; regresja wektora nośnego

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2018
• Tom: Vol. 14, no 2
• Strony: 38--47
• Opis fizyczny: Bibliogr. 13 poz., fig., tab.

Twórcy

autor

Kaya Gamze Ogcu

• Sampoerna University, Department of Industrial Engineering, Jakarta, Indonesia

autor

Turkyilmaz Ali

• Nazarbayev University, School of Engineering, Astana, Kazakhstan

Bibliografia

• [1] 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.
• [2] Chau, K. W. (2006). A review on the integration of data mining into coastal modeling. Journal of Environmental Management, 80, 47–57.
• [3] Croston, J. F. (1972). Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23, 289–304.
• [4] Hoover, J. (2006). Measuring Forecast Accuracy: Omissions in Today’s Forecasting Engines and Demand-Planning Software. International Journal of Applied Forecasting, 1, 32–35.
• [5] 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.
• [6] 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.
• [7] Mitchell, T. (1997). Machine Learning. Boston: McGraw-Hill.
• [8] 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.
• [9] Punjari, A. K. (2006). Data Mining Techniques. Universities Press Private Limited.
• [10] Rao, A. V. (1973). A comment on: forecasting and stock control for intermittent demands. Operational Research Society, 24, 639–640.
• [11] Regattieri, A., Gamberi, M., Gamberini, R., & Manzini, R. (2005). Managing Lumpy Demand for Aircraft Spare Parts. Journal of Air Transport Management, 11, 426–431.
• [12] Syntetos, A. A. (2007). A note on managing lumpy demand for aircraft spare parts. Journal of Air Transport Management, 13, 166–167.
• [13] Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York: Springer.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).