Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Forecasting of lead-time demand variance: implications for safety stock calculations

Babai, Zied, Dai, Yong, Li, Qinyun, Syntetos, Aris and Wang, Xun 2021. Forecasting of lead-time demand variance: implications for safety stock calculations. European Journal of Operational Research 10.1016/j.ejor.2021.04.017
Item availability restricted.

[img] PDF - Accepted Post-Print Version
Restricted to Repository staff only until 18 April 2023 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB)

Abstract

Lead-time demand forecasting constitutes the backbone of inventory control. Although there has been a considerable amount of research on forecasting the mean lead-time demand, less has centered around forecasting lead-time demand variance, especially in the case of stochastic lead-times. This represents an important gap in the literature, given that safety stock calculations rely explicitly on the lead-time demand variance (or equivalently the variance of the lead-time demand forecast error for unbiased estimators). We bridge this gap by exploring the viability of three strategies to estimate the variance of the lead-time demand forecast error under stochastic lead-times: (1) aggregating the per period variance of forecast errors over the lead-time, which is the classical approach; (2) considering the variance of the aggregated (over the lead-time) forecast error; (3) considering the variance of the forecast errors resulting from temporally aggregated (over the lead-time length) demand. Analytical results are derived for a first order autoregressive moving average ARMA(1,1) demand process for both a single exponential smoothing and the minimum mean squared error forecasting method. A numerical investigation assesses the effects of demand autocorrelation and lead-time variability on the accuracy of each strategy, and the conditions under which one outperforms the others. The results show that the classical strategy presented in textbooks appears to be the least accurate one, except for cases with a high negative demand autocorrelation. An analysis of the inventory control performance also reveals that the classical strategy often leads to higher inventory costs and lower service levels for positive autocorrelation.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Business (Including Economics)
Publisher: Elsevier
ISSN: 0377-2217
Date of First Compliant Deposit: 20 May 2021
Date of Acceptance: 8 April 2021
Last Modified: 20 May 2021 12:57
URI: http://orca.cardiff.ac.uk/id/eprint/140521

Actions (repository staff only)

Edit Item Edit Item