Analysis Postponement

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Analysis of postponement strategy for perishable items by
EOQ-based models
J. Lia,c, T.C.E. Chengb,∗ , S.Y. Wanga
a

Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, 100080, China

b

Department of Logistics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong c

Department of Mathematics, Qufu Normal University, Qufu, Shandong, 273165, China

Abstract

This paper develops EOQ-based models with perishable items to evaluate the impact of a form postponement strategy on the retailer in a supply chain. We formulate models for a postponement system and an independent system to minimize the total average cost function per unit time for ordering and keeping n perishable end-products. An algorithm is given to derive the optimal solutions of the proposed models. The impact of the deterioration rate on the inventory replenishment policies is studied with the help of theoretical analysis and numerical examples. Our theoretical analysis and computational results show that a postponement strategy for perishable items can give a lower total average cost under certain circumstances.

Keywords: Postponement strategy; Economic-order-quantity model (EOQ); Perishable items; Inventory management

1. Introduction

Postponement, also known as late customization or delayed product differentiation, refers to delaying some product differentiation processes in a supply chain as late as possible until the supply chain is cost effective (Garg and Lee, 1998). Postponement is one of central features of mass customization (van Hoek, 2001). It has been reported that postponement strategy is highly successful in a wide range of industries that require high differentiation, e.g., high-tech ∗

Corresponding author. Tel.: +852-2766-5216; fax: +852-2364-5245. E-mail address: lgtcheng@polyu.edu.hk (T.C.E. Cheng)

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industry, food industry, and fashion industry, etc. One practical example is Hewlett-Packard Development Company. HP produces generic printers in its factories and distributes them to local distribution centers, where power plugs with appropriate voltage and user manuals in the right language are packed. They have saved a lot of money every year by adopting the postponement strategy (Lee, 1998).

However, postponement is not an omnipotent strategy. It has both advantages and disadvantages. The advantages include following the JIT principles of production, reducing end-product inventory (Brown et al., 2000), making forecasting easier (Ernst and Kamrad, 2000), and pooling risks (Garg and Tang, 1997). The high cost of redesigning and manufacturing generic components is the main drawback of postponement (Lee, 1998). Thus, evaluation of postponement structures is an important issue. Many qualitative and quantitative models have been developed to evaluate the cost-effectiveness of postponement strategy under different scenarios. Details can be found in the review articles by van Hoek (2001), and Wan et al. (2003a). Recent quantitative models include, but are not limited to, those by Lee (1996), Garg and Tang (1997), Garg and Lee (1998), Ernst and Kamrad (2000), Aviv and Federgruen (2001), Ma et al. (2002), Su (2005), and Reiner (2005). They evaluated the cost and benefits of applying postponement in a large variety of stochastic settings. If demand is deterministic, e.g., because there is a long-term supply contract between a manufacturer and its customers, the benefits due to economies of scope from risk pooling do not exit. It is necessary to develop deterministic models to evaluate postponement. Recent deterministic models include, among others, those by Wan et al. (2003b, 2004), and Li et al. (2005). Wan et al. (2003b, 2004) analyzed pull postponement using EOQ-based models and EPQ-based models. They showed that the postponed customization of end-products will result in a lower total average cost under...
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