Wednesday , October 24 2018

A State of the Art on Supply Planning and Inventory Control under Lead Time Uncertainty

Alexandre DOLGUI, Oussama BEN AMMAR, Faicel HNAIEN, Mohamed Aly O. LOULY
1 École Nationale Supérieure des Mines, LIMOS, UMR 6158 CNRS
158 cours Fauriel, 42023 Saint Etienne Cedex 2, France
2 École Nationale Supérieure des Mines, LIMOS, UMR 6158 CNRS
158 cours Fauriel, 42023 Saint Etienne Cedex 2, France
3 FARCAM, Industrial Engineering Department, College of Engineering, King Saud University
P.O. Box 800, Riyadh 11421, Kingdom of Saudi Arabia

Abstract: In literature, most papers examine several stochastic demand processes where order lead times are constant. In reality, manufacturing firms use inventory management software, especially MRP, which ignored lead time uncertainty. It is true that in certain special cases, lead time uncertainty has essentially no effect and can be ignored. Nevertheless more often, lead time fluctuations strongly degrade tools performance and cause high production costs, just as demand uncertainty does. Seemingly, uncertainty has been neglected for a long time in favour of studying demand uncertainties. Industry agrees that it is overdue and there is a need to rectify this oversight. Nowadays, this gap in research activity begins to be filled in order to respond to companies having non-deterministic lead-times constraints. This paper reviews some of existing literature of supply planning tools under uncertainty of lead times. The extensive literature review that we compiled consists of citations from 1970 to 2012. A classification scheme for supply planning under uncertainty is defined.

Keywords: Production planning, Inventory control, MRP software, Parameterization, Random lead times

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CITE THIS PAPER AS:
Alexandre DOLGUI, Oussama BEN AMMAR, Faicel HNAIEN, Mohamed Aly O. LOULY, A State of the Art on Supply Planning and Inventory Control under Lead Time Uncertainty, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (3), pp. 255-268, 2013.

Introduction

Inventory control in a supply chain is crucial for companies to satisfy their customer demands as well as controlling costs. However, the uncertainty can disrupt the supply and production process (Koh and Saad, 2003). Wazed and al. (2009) have identified the major factors of uncertainty in a real manufacturing environment.

Thousands of papers in the scientific literature proposed several techniques that have been developed to parameterize the Material Requirement Planning (MRP) software under uncertainties. For example, Dolgui and Prodhon (2007) have focused on the development of MRP software for an uncertain environment and have shown that various techniques such as safety stock, safety lead time, and lot-sizing rules can be used to control the supply variability in order to lead the better anticipation of uncertainties.

Whybark and Williams (1976) show how the safety lead time can be used to handle supply uncertainties, such as late delivery. There are other studies in the literature (Molinder 1997, Guide and Srivasta 2000, Hegedus and Hopp 2001, Koh and Saad 2003, Van Kampen Tim et al. 2010) that have shown similar choices.

In literature, many models exist for a random demand. Indeed, Mula et al. (2006b) have done an excellent review of demand uncertainty and shown that the case of lead time uncertainty was less studied in the past, in spite of their significant importance.

Axsäter (2006) describe two different types of stochastic lead times: stochastic lead times are independent/ or dependent of the lead time demand and independent lead times. The author gave examples of two types of lead times and a numerical comparison. The comparison proves that the inventory level varies much less in case of independent lead times. In Svoronos and Zipkin (1991) there are similar discussions of different types of stochastic lead times: endogenous lead time, dependent of the capacity of system, and exogenous lead time, independent of the capacity of system.

From 1970 to 2012, there have been a lot of research and applications aiming to formalize the uncertainty of lead times in supply chains.

Table 1 summarizes the paper reviewed in relation to the number of citations from Scopus database (www.scopus.com).

Table 1. Classification scheme for models for supply planning under uncertainty of lead times

Article Number of citations
Zipkin (1986) 63
Svoronos and Zipkin (1991) 69
Song et al. (1999) 72
Yano (1987 c) 53
Sphicas et al. (1984) 14
Lau et al. (1993) 36
Chiang and Benton. (1994) 28
Yano (1987 b) 22
Song and Yano (2000) 37
Anupindi et al. (1996) 11
Nasri et al. (1990) 42
Dolgui and Louly (2002) 44
Ishii et al. (1981) 7
Zalkind et al. (1978) 14
Tang and Grubbström (2003) 19
 Tang and Grubbström (2005) 21
Alp et al. (2003) 13

A total of 2082 articles on lead times were reviewed. The majority of papers were found in the following journals: International Journal of Production Economics, European Journal of Operational Research, International Journal of Production Research, Management Science, IIE Transactions, Operations Research, Computers and Industrial Engineering, Production and Operations Management (see Table 2).

The objective of this paper is to: Review the literature, classify the literature based on the modelling approach, and Identify future research directions. We do not describe in detail the models that have been considered.

This paper is organized as follows. In the next section, published research with random lead times and deterministic demand is reviewed and classified. Then, published research with both random lead times and random demand are also reviewed and classified. Finally, in section 4 the conclusions and directions for further research are given. A preliminary version of this paper was presented at IFAC conference MIM 2013 (Ben Ammar et al., 2013).

A total of 2082 articles on lead times were reviewed. The majority of papers were found in the following journals: International Journal of Production Economics, European Journal of Operational Research, International Journal of Production Research, Management Science, IIE Transactions, Operations Research, Computers and Industrial Engineering, Production and Operations Management (see Table 2).

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