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

Mitigating variance amplification under stochastic lead-time: the proportional control approach

Wang, Xun ORCID: https://orcid.org/0000-0001-7800-726X and Disney, Stephen M. ORCID: https://orcid.org/0000-0003-2505-9271 2017. Mitigating variance amplification under stochastic lead-time: the proportional control approach. European Journal of Operational Research 256 (1) , pp. 151-162. 10.1016/j.ejor.2016.06.010

[thumbnail of Mitigating variance amplification under stochastic leadtime Pre-print.pdf]
Preview
PDF - Accepted Post-Print Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB) | Preview

Abstract

Logistic volatility is a significant contributor to supply chain inefficiency. In this paper we investigate the amplification of order and inventory fluctuations in a state-space supply chain model with stochastic lead-time, general auto-correlated demand and a proportional order-up-to replenishment policy. We identify the exact distribution functions of the orders and the inventory levels. We give conditions for the ability of proportional control mechanism to simultaneously reduce inventory and order variances. For AR(2) and ARMA(1,1) demand, we show that both variances can be lowered together under the proportional order-up-to policy. Simulation with real demand and lead-time data also confirms a cost benefit exists.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: H Social Sciences > HD Industries. Land use. Labor
Uncontrolled Keywords: Inventory; Bullwhip effect; Stochastic lead-time; Demand correlation
Publisher: Elsevier
ISSN: 0377-2217
Date of First Compliant Deposit: 6 June 2016
Date of Acceptance: 6 June 2016
Last Modified: 13 Nov 2023 14:36
URI: https://orca.cardiff.ac.uk/id/eprint/91540

Citation Data

Cited 31 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics