Skip to content
BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access November 4, 2016

Development of S-ARIMA Model for Forecasting Demand in a Beverage Supply Chain

Dejan Mircetic, Svetlana Nikolicic, Marinko Maslaric, Nebojsa Ralevic and Borna Debelic
From the journal Open Engineering

Abstract

Demand forecasting is one of the key activities in planning the freight flows in supply chains, and accordingly it is essential for planning and scheduling of logistic activities within observed supply chain. Accurate demand forecasting models directly influence the decrease of logistics costs, since they provide an assessment of customer demand. Customer demand is a key component for planning all logistic processes in supply chain, and therefore determining levels of customer demand is of great interest for supply chain managers. In this paper we deal with exactly this kind of problem, and we develop the seasonal Autoregressive IntegratedMoving Average (SARIMA) model for forecasting demand patterns of a major product of an observed beverage company. The model is easy to understand, flexible to use and appropriate for assisting the expert in decision making process about consumer demand in particular periods.

References

[1] Chopra S., Meindl P., Supply chainmanagement, Strategy, planning & operation: Springer, 2007 Search in Google Scholar

[2] Molnár V., SAPwarehousemanagement system for awarehouse of auxiliarymaterial in the selected company, In: Carpathian Logist. Congr., TANGER LTD. Ostrava, 2012, 1–6 Search in Google Scholar

[3] Fedorko G., Husáková N., Dudáš G., Design of allocation of new technological equipment within the frame of production process in company Getrag Ford Transmissions Slovakia, s.r.o., Acta Montan. Slovaca, 2010, 15, 14–22 Search in Google Scholar

[4] Andrejiová M., Pavlisková A., Husáková N., Application of multicriterion decision methods by the selection of optimal constructive elements for devices of continuous transport, In: Carpathian Logist. Congr., TANGER LTD. Ostrava, 2012, 1–6 Search in Google Scholar

[5] Hogarty T. F., Elzinga, K.G., The demand for beer, The Review of Economics and Statistics, 1972, 195–198. 10.2307/1926282Search in Google Scholar

[6] Freeman D. G., Beer in good times and bad: A US state-level analysis of economic conditions and alcohol consumption, Journal of Wine Economics, 2011, 6(02), 231–251 10.1017/S1931436100001620Search in Google Scholar

[7] Brenner M.H., Mooney A., Unemployment and health in the context of economic change, Social Science & Medicine, 1983, 17(16), 1125–1138 10.1016/0277-9536(83)90005-9Search in Google Scholar

[8] Freeman D. G., Beer and the business cycle, Applied Economics Letters, 2001, 8(1), 51–54 10.1080/135048501750041295Search in Google Scholar

[9] Mircetic D., Ziramov N., Nikolicic S.,Maslaric M., Ralevic N., Demand Forecasting in Beverage Supply Chain, In: The 3rd Olympus International Conference In Supply Chains (3rd Olympus ICSC), Greece, Athens, 2015, 58–65 Search in Google Scholar

[10] Cleveland R.B., ClevelandW.S., McRae J.E., Terpenning I., STL: A seasonal-trend decomposition procedure based on loess, Journal of Oflcial Statistics, 1990, 6(1), 3–73 Search in Google Scholar

[11] Hyndman R., Khandakar Y., Automatic time series forecasting: the forecast package for R 7, 2008. 10.18637/jss.v027.i03Search in Google Scholar

[12] Box G., Jenkins G., Reinsel G., Time Series Analysis Forecasting and Control. 3rd ed., Prentice Hall, 1994 Search in Google Scholar

Received: 2016-7-6
Accepted: 2016-8-1
Published Online: 2016-11-4

©2016 D. Mircetic et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Scroll Up Arrow