INTERMITTENT DEMAND FORECASTING USING DATA MINING TECHNIQUES
Gamze Ogcu KAYA
gamzeogcu@gmail.comSampoerna University, Department of Industrial Engineering, Jakarta (Indonesia)
Ali TURKYILMAZ
Nazarbayev University, School of Engineering, Astana (Kazakhstan)
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 ForecastingReferences
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
Google Scholar
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
Google Scholar
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
Google Scholar
Hoover, J. (2006). Measuring Forecast Accuracy: Omissions in Today’s Forecasting Engines and Demand-Planning Software. International Journal of Applied Forecasting, 1, 32–35.
Google Scholar
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
Google Scholar
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
Google Scholar
Mitchell, T. (1997). Machine Learning. Boston: McGraw-Hill.
Google Scholar
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
Google Scholar
Punjari, A. K. (2006). Data Mining Techniques. Universities Press Private Limited.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York: Springer.
DOI: https://doi.org/10.1007/978-1-4757-2440-0
Google Scholar
Authors
Gamze Ogcu KAYAgamzeogcu@gmail.com
Sampoerna University, Department of Industrial Engineering, Jakarta Indonesia
Authors
Ali TURKYILMAZNazarbayev University, School of Engineering, Astana Kazakhstan
Statistics
Abstract views: 45PDF downloads: 41
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Saheed A. ADEWUYI, Segun AINA, Adeniran I. OLUWARANTI, A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA , Applied Computer Science: Vol. 16 No. 1 (2020)
- Anupa ARACHCHIGE, Ranil SUGATHADASA, Oshadhi HERATH, Amila THIBBOTUWAWA, ARTIFICIAL NEURAL NETWORK BASED DEMAND FORECASTING INTEGRATED WITH FEDERAL FUNDS RATE , Applied Computer Science: Vol. 17 No. 4 (2021)
- Tilla IZSÁK, László MARÁK, Mihály ORMOS, EVALUATION OF SUPPORT VECTOR MACHINE BASED STOCK PRICE PREDICTION , Applied Computer Science: Vol. 19 No. 3 (2023)
- Sahar ZAMANI KHANGHAH, Keivan MAGHOOLI, EMOTION RECOGNITION FROM HEART RATE VARIABILITY WITH A HYBRID SYSTEM COMBINED HIDDEN MARKOV MODEL AND POINCARE PLOT , Applied Computer Science: Vol. 20 No. 1 (2024)
- Nataliya SHABLIY, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, Olha MALYSHEVSKA, KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 17 No. 4 (2021)
- Wojciech DANILCZUK, Arkadiusz GOLA, COMPUTER-AIDED MATERIAL DEMAND PLANNING USING ERP SYSTEMS AND BUSINESS INTELLIGENCE TECHNOLOGY , Applied Computer Science: Vol. 16 No. 3 (2020)
- Saheed ADEWUYI, Segun AINA, Aderonke LAWAL, Adeniran OLUWARANTI, Moses UZUNUIGBE, AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING , Applied Computer Science: Vol. 15 No. 4 (2019)
- Jack OLESEN, Carl-Emil Houmøller PEDERSEN, Markus Germann KNUDSEN, Sandra TOFT, Vladimir NEDBAILO, Johan PRISAK, Izabela Ewa NIELSEN, Subrata SAHA, JOINT EFFECT OF FORECASTING AND LOT-SIZING METHOD ON COST MINIMIZATION OBJECTIVE OF A MANUFACTURER: A CASE STUDY , Applied Computer Science: Vol. 16 No. 4 (2020)
- Monika KULISZ, Aigerim DUISENBEKOVA, Justyna KUJAWSKA, Danira KALDYBAYEVA, Bibigul ISSAYEVA, Piotr LICHOGRAJ, Wojciech CEL, IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY , Applied Computer Science: Vol. 19 No. 4 (2023)
- Mouna TARIK, Ayoub MNIAI, Khalid JEBARI, HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES , Applied Computer Science: Vol. 19 No. 2 (2023)
You may also start an advanced similarity search for this article.
