KESİKLİ TALEPLERİN TAHMİNLENMESİNDE ATA METOT VE CROSTON TEMELLİ METOTLARIN KARŞILAŞTIRILMASI

Abstract

Kesikli talep tahmini, şirketler ve ticari faaliyetler için çok önemlidir. Son zamanlarda, birçok araştırmacı kesikli talep için tahmin yöntemlerine odaklandı ve çeşitli tahmin teknikleri önerdiler. Bu önerilen teknikler arasında öne çıkan yöntemler, üssel düzleştirmeye dayanan Croston yöntemi ve bu yöntemin iki türevi olan SBA (Syntetos-Boylan Yaklaşımı) ve SBJ (Shale-Boylan-Johnston Yaklaşımı) metotlarıdır.e Croston yöntemi, kesikli talep ve envanter (stok) kontrolünün tahmininde yaygın olarak kullanılmaktadır. Bu talepler genellikle sıfır değerini içerdiğinden, bu verilerde Croston tarafından geliştirilen öne çıkan metodun kullanılması kaçınılmaz hale gelir. Bununla birlikte; bu yöntemin, yanlı tahminler üretmek gibi bazı eksiklikleri vardır ve bu sebeple türevleri önerilmiştir. ATA metot, üssel düzleştirmeye alternatif olarak yeni geliştirilen bir tahmin metodudur. Bu çalışmada, kesikli talebin tahmin edilmesi için bir ATA yönteminin bir modifikasyonunu öneriyoruz. Önerilen yaklaşımın sonuçlarını, Croston ve kesikli talep tahmini için kullanılan diğer tahmin yöntemleriyle karşılaştıracağız.

Keywords

• Croston metot,Envanter Kontrolü,Kesikli Talep,Talep tahminlemesi,Üssel düzleştirme,Zaman serisi

COMPARISON OF ATA METHOD AND CROSTON BASED METHODS ON FORECASTING OF INTERMITTENT DEMAND

Abstract

Intermittent demand forecasting is crucial for firms and commercial activities. Recently, many researchers have focused on forecasting methods for intermittent demand and proposed various forecasting techniques. The most prominent methods among these proposed techniques are the Croston method, which is based on exponential smoothing, and its two popular variations: SBA (Syntetos-Boylan Approximation), SBJ (Shale-Boylan-Johnston Approximation). Croston method is widely used in forecasting of intermittent demand and inventory (stock) control. Since these demands usually include zero values, using the ground breaking method developed by Croston in this data becomes inevitable. Nevertheless, there are some shortcomings to this method such as producing biased forecasts and for this reason its variations have been proposed. ATA method is a recently developed forecasting method which is an alternative to exponential smoothing. In this paper we propose a modification of ATA method that can be used for forecasting of intermittent demand. We will compare the results of the proposed approach to those of Croston and other forecasting methods used for intermittent demand forecasting.

Keywords

• Exponential smoothing,Demand forecasting,Time series,Croston method,Intermittent demand,Inventory control

References

1. Altay, N., & Litteral, L. A., & Rudisill, F. "Effects of correlation on intermittent demand forecasting and stock control". International Journal of Production Economics, 135(1), 275–283, 2012.
2. Boylan, J. E., & Syntetos, A. A.. "On the bias of intermittent demand estimates". Int. J. Production Economics, 71, 457–466, 2001.
3. Croston, J. D. "Forecasting and Stock Control for Intermittent Demands". Operational Research Quarterly, 23(3), 289-303, 1972.
4. Eaves, A. H. C. "Forecasting for the ordering and stock-holding of consumable spare parts," PhD Thesis, Lancaster University, 2002.
5. Hyndman, R., & Koehler, A., & Ord, K., & Snyder, R. "Forecasting with Exponential Smoothing The state Space Approach", Springer Series in Statistics, 2008.
6. Johnston, F. R., & Boylan, J. E. "Forecasting for items with intermittent demand". Journal of the Operational Research Society, 47(1), 113–121, 1996.
7. Kourentzes, N. "Intermittent demand forecasts with neural networks". International Journal of Production Economics, 143(1), 198–206, 2013.
8. Levén, E., & Segerstedt, A. "Inventory control with a modified Croston procedure and Erlang distribution". International Journal of Production Economics, 90(3), 361–367, 2004.

9. Ord, J. K., & Snyder, R. D., & Beaumont, A. "Forecasting the Intermittent Demand for Slow-Moving Items". Research Program on Forecasting, George Washington University, 2010
10. Petropoulos, F., & Kourentzes, N., & Nikolopoulos, K. "Another look at estimators for intermittent demand". International Journal of Production Economics, 181(A), 154–161, 2016.
11. Petropoulos, F., & Nikolopoulos, K., & Spithourakis, G. P., & Assimakopoulos, V. "Empirical heuristics for improving intermittent demand forecasting". Industrial Management & Data Systems, 113(5), 683–696, 2013.
12. Rao, A. V. "A Comment on: Forecasting and Stock Control for Intermittent Demands." Journal of the Operational Research Society, 24(4), 639–640, 1973.
13. Segerstedt, A. "Inventory control with variation in lead times, especially when demand is intermittent". International Journal of Production Economics, 35(1–3), 365–372, 1994.
14. Shale, E. A., & Boylan, J. E., & Johnston, F. R. " Forecasting for intermittent demand: the estimation of an unbiased average". Journal of the Operational Research Society, 57(5), 588–592, 2006.
15. Silver, E. A. "Operations Research in Inventory Management : A Review and Critique". Operational Research, 29(4), 628–645, 1981.
16. Syntetos, A., A., & Babai, M., Z., & Dallery, Y., & Teunter, R. "Periodic control of intermittent demand items: theory and empirical analysis". Journal of the Operational Research Society, 60(5), 611–618, 2009.
17. Syntetos, A., A. "Forecasting of intermittent demand". PhD Thesis, Brunel University Business School, 2001.
18. Syntetos, A., A., & Boylan, J., E. "The accuracy of intermittent demand estimates". International Journal of Forecasting, 21(2), 303–314, 2005.
19. Teunter, R., & Sani, B. "On the bias of Croston’s forecasting method". European Journal of Operational Research, 194(1), 177-183, 2009.
20. Teunter, R., H., & Duncan, L. "Forecasting intermittent demand: a comparative study". Journal of the Operational Research Society, 60(3), 321–329, 2009.
21. Teunter, R., H, & Syntetos, A., A., & Babai, M., Z. "Intermittent demand: Linking forecasting to inventory obsolescence". European Journal of Operational Research, 214(3), 606–615, 2011.
22. Vasumathi, B., & Saradha, A. "Forecasting Intermittent Demand for Spare Parts 1". International Journal of Computer Applications, 75(11), 12–16, 2013.
23. Vereecke, A., & Verstraeten, P. "An inventory management model for an inventory consisting of lumpy items, slow movers and fast movers". International Journal of Production Economics, 35(1), 379–389, 1994.
24. Wallström, P., & Segerstedt, A. "Evaluation of forecasting error measurements and techniques for intermittent demand". International Journal of Production Economics, 128(2), 625–636, 2010.
25. Willemain, T., R., & Smart, C., N., & Schwarz, H., F. "A new approach to forecasting intermittent demand for service parts inventories". International Journal of Forecasting, 20(3), 375–387, 2004.
26. Yapar, G., & Yavuz, I., & Selamlar, H., T. "Why and How Does Exponential Smoothing Fail ? An In Depth Comparison of ATA-Simple and Simple Exponential Smoothing". Turkish Journal of Forecasting, 1(1), 30–39, 2017.
27. Yapar, G. "Modified simple exponential smoothing". Hacettepe Journal of Mathematics and Statistics. https://doi.org/10.15672/HJMS.201614320580, 2018.