• ISSN: 2148-2225 (online)

Ulaştırma ve Lojistik Kongreleri

alphanumeric journal

The Journal of Operations Research, Statistics, Econometrics and Management Information Systems

A Bayesian Approach Proposal For Inventory Cost and Demand Forecasting

Sinan Apak, Ph.D.


Technology’s perpetual vicissitude and product models’ distinction in industrial market have a crucial effect on forecasting demand for spare components. In order to set forth the future demand rates for products, inventory managers repetitively update their prognostications. Bayesian model is utilizing a prior probability distribution for the injunctive authorization rate which was habituated in order to get optimum levels of account over a number of periods. However, under sundry demand rates like intermittent demand, Bayesian Model’s performance has not been analyzed. With the help of a research question, the study investigates that circumstance.

Keywords: Bayesian Model, Forecasting, Inventory, Probability Distribution

Jel Classification: C11, C18, C53

Envanter Maliyeti ve Talep Tahmini için Bayes Yaklaşımı Önerisi


Endüstriyel pazardaki teknolojinin kalıcı değişikliğinin ve ürün modellerinin farklılığının, yedek parçalar için yapılan talep tahmini üzerinde önemli bir etkisi vardır. Ürünlerin gelecekteki talep oranlarını ortaya koymak amacıyla envanter yöneticileri kendi tahminlerini sürekli güncellemektedir. Bayes modeli, önsel olasılık dağılımı kullanarak kabul edilebilir oranı birkaç dönem üzerinden optimum hesap yapmak için kullanmaktadır. Ancak, aralıklı talep gibi muhtelif talep oranlarının altında, Bayes Modelinin performansı analiz edilmemiştir. Bir araştırma sorusu yardımıyla, bu çalışma bu durum inceler

Anahtar Kelimeler: Bayes Modeli, Envanter, Olasılık Dağılımı, Tahminleme

Cite this article

Apak, S. (2015). A Bayesian Approach Proposal For Inventory Cost and Demand Forecasting. Alphanumeric Journal, 3(2), 41-48. http://dx.doi.org/10.17093/aj.2015.3.2.5000140055


  • Boylan, J.E., and Syntetos, A.A., (2010). Spare parts management: a review of forecasting research and extensions. IMA Journal of Management Mathematics, 21(3) pp. 227-237.
  • Chen, Y., Liu, P. and Yu, L., (2010). Aftermarket demands forecasting with a Regression-Bayesian-BPNN model, Intelligent Systems and Knowledge Engineering International Conference, pp. 52–55.
  • De Wit, J.R., (1983). Inventory problems with slow moving items: A Bayesian approach, The Statistician. 32(1) pp. 201-206.
  • Dolgui, A., and Pashkevich, M., (2007). On the performance of binomial and beta- binomial models of demand forecasting for multiple slow-moving inventory items, Computers and Operations Research. 13(8) pp. 112-129.
  • Ghobbar, A.A., and Friend, C.H. (2003). Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model, Computational Operations Research. 30 pp. 2097–2114.
  • Gohodrati, B., Akrsten, P.A. and Kumar, U., (2007). Spare part estimation and risk assessment conducted at Choghart Iron Ore Mine, Journal of Quality in Maintenance Engineering. 13(4) pp. 353-363.
  • Hua, Z.S., Zhang, B., Yang, J., and Tan, D.S. (2007). A new approach of forecasting intermittent demand for spare parts inventories in the process industries, Journal of the Operational Research Society. 58(1) pp. 52-61.
  • Kennedy, W.J., Patterson, J.W., and Fredendall, L.D. (2002). An overview of recent literature on spare parts inventories, International Journal of Production Economics. 76 pp. 201–215.
  • Lee Y.S., 2014. Management of a periodic-review inventory system using Bayesian model averaging when new marketing efforts are made. Int. J.ProductionEconomics158(2014)278–289.
  • Leven E., and Segerstedt, A., (2004). Inventory control with a modified Croston procedure and Erlang distribution, International Journal of Production Economics, 90(3) pp. 361-367.
  • Li, S. and Kuo, G., (2008). The inventory management system for automobile spare parts in a central warehouse, Expert Systems with Applications. (34) pp. 1144–1153.
  • Lindsey M., and Pavur R. Prediction intervals for future demand of existing products with an observed demand of zero. Int. J. Production Economics 119 (2009) 75–89.
  • Price, B.A., and Haynsworth, H.C., (1986). How to prepare inventory forecasts for very low demand items, The Journal of Business Forecasting. 5(2) pp. 21-22.
  • Popovic, J.B., (1987). Decision making on stock levels in cases of uncertain demand rate, European Journal of Operational Research. 32(2) pp. 276-290.
  • Razi, L.A., and Tarn, J.M., (2003). An applied model for improving inventory management in ERP systems, Logistics Information Management. 16(2) pp. 114-124.
  • Shale, E.A., Boylan, J.E., and Johnston, F.R., (2008). Demand Forecasting for Inventory Management: Characterizing the frequency of orders received by a stockiest, IMA Journal of Management Mathematics. 19(2) pp. 137-143.
  • Silver, E.A., (1965). Bayesian determination of the reorder point of a slow moving item, Operations Research. 13(6) pp. 989-997.
  • Snyder, R.D., (2002). Forecasting sales of slow and fast moving inventories, European Journal of Operational Research. 140(3) pp. 684-699.
  • Stevenson, W.J., (2007). Operations management (9th ed.). St. Louis: McGraw- Hill/Irwin.
  • Strijbosch, J.W.G., Heuts, R.J.M., and Schoot, E.H.M., (2000). A combined forecast-inventory control procedure for spare parts, Journal of the Operational Research Society. 51(10) pp. 1184-1192.
  • Syntetos, A.A., and Boylan, J.E., (2005). The accuracy of intermittent demand estimates, International Journal of Forecasting, 21(2) pp. 303-314.
  • Syntetos A.A., Boylan J.E. and Croston, J.D., (2005). On the categorization of demand patterns, Journal of the Operational Research Society, 56(5) pp. 495-503.
  • Syntetos, A.A. and Boylan, J.E., (2006). On the stock-control performance of intermittent demand estimators, International Journal of Production Economics, 103(1) pp. 36-47.
  • Vereecke, A.A., and Verstraeten, P. (1994). An inventory management model for an inventory consisting of lumpy items, slow movers and fast movers, International Journal of Production Economics. 35(1/3) pp. 379-389.
  • Viswanathan, S., Widiarta, H., and Piplani, R., (2008). Forecasting aggregate time series with intermittent subaggregate components: top-down versus bottom-up forecasting, IMA Journal of Management Mathematics. 19(3) pp. 275-287.
  • Willemain,T.R., Smart, C.N., and H. F. Schwarz, A new approach to forecasting intermittent demand for service parts inventories, International Journal of Forecast, 20 (2004) pp. 375–387.
  • Yılmaz, A., (2012). Yedek parça piyasası, www.subconturkey.com/2010/Mart/koseyazisi-Yedek-parca-piyasasi.html (available on 28.12.2012).

Volume 3, Issue 2, 2015


alphanumeric journal

Volume 3, Issue 2, 2015

Pages 41-48

Received: Sept. 16, 2015

Accepted: Dec. 24, 2015

Published: Dec. 31, 2015

Full Text [551.4 KB]

  • Share

2015 Apak, S.

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Contact Us

School of Transportation and Logistics, Istanbul University
Avcilar Campus 34320 Avcilar/Istanbul/TURKEY

+ 90 (212) 473 70 00 - 19263

alphanumeric journal

alphanumeric journal has been publishing as "International Peer-Reviewed Journal" every six months since 2013. alphanumeric serves as a vehicle for researchers and practitioners in the field of quantitative methods, and is enabling a process of sharing in all fields related to the operations research, statistics, econometrics and management informations systems in order to enhance the quality on a globe scale.