Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand:theory and empirical analysis

Babai, M. Z. and Ali, M. M. and Boylan, John and Syntetos, A. A. (2013) Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand:theory and empirical analysis. International Journal of Production Economics, 143 (2). pp. 463-471. ISSN 0925-5273

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Abstract

The ARIMA(0,1,1) demand model has been analysed extensively by researchers and used widely by forecasting practitioners due to its attractive theoretical properties and empirical evidence in its support. However, no empirical investigations have been conducted in the academic literature to analyse demand forecasting and inventory performance under such a demand model. In this paper, we consider a supply chain formed by a manufacturer and a retailer facing an ARIMA(0,1,1) demand process. The relationship between the forecasting accuracy and inventory performance is analysed along with an investigation on the potential benefits of forecast information sharing between the retailer and the manufacturer. Results are obtained analytically but also empirically by means of experimentation with the sales data related to 329 Stock Keeping Units (SKUs) from a major European superstore. Our analysis contributes towards the development of the current state of knowledge in the areas of inventory forecasting and forecast information sharing and offers insights that should be valuable from the practitioner perspective.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Production Economics
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2209
Subjects:
?? INVENTORYFORECASTINGARIMA DEMANDSUPPLY CHAININFORMATION SHARINGBUSINESS, MANAGEMENT AND ACCOUNTING(ALL)ECONOMICS AND ECONOMETRICSMANAGEMENT SCIENCE AND OPERATIONS RESEARCHINDUSTRIAL AND MANUFACTURING ENGINEERING ??
ID Code:
72969
Deposited By:
Deposited On:
19 Feb 2015 08:06
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Sep 2023 00:41