Int. J. Production Economics 141 (2013) 529–540
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Int. J. Production Economics
journal homepage: www.elsevier.com/locate/ijpe
Postponed product differentiation with demand information update$ Juliang Zhang a, Biying Shou b,n, Jian Chen c
a b c
Department of Logistics Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China Department of Management Sciences, City University of Hong Kong, Hong Kong S.A.R., China Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Beijing 100084, China
a r t i c l e i n f o
Article history: Received 5 December 2011 Accepted 4 September 2012 Available online 28 September 2012 Keywords: Postponed differentiation Information update Production planning Stochastic programming
This paper studies the problem of how to coordinate postponed product differentiation and forecast update to improve manufacturing efﬁciency. We consider a two-stage model of multiple products with a common component. In stage 1, the manager obtains a prior demand distribution of each product and decides the production quantity of the common component. In stage 2, the demand forecast is updated and the common component is differentiated into various ﬁnal products. Then the ﬁnal demand of each product is realized and inventory leftover (shortage) is assessed. We use stochastic programming to model this problem, and propose an optimal bundle-type algorithm to solve it. Furthermore, we develop some simple and effective approximation algorithms for several special cases. Extensive numerical experiments are conducted to show the effectiveness of the approximation algorithms, to compare the performance between the traditional production model and the postponement production model, and to examine the impact of parameters on the performances of the two systems. & 2012 Elsevier B.V. All rights reserved.
1. Introduction It is well known that the apparel industry has two salient characteristics: a long lead-time and a short selling season. Generally, the lead-time is about 5–8 months and the major selling season lasts only a few weeks (Iyer and Bergen, 1997; Caro and Martinez-de-Albeniz, 2010). This makes it difﬁcult to match supply and demand properly. The retailer can have either: (i) too little inventory, which results in product stockout and low service level, or (ii) too much inventory, resulting in forced markdowns, disposal costs, or expediting cost (Iyer and Bergen, 1997). Frazier (1986) estimated that these costs account for about 25% of sales. In order to circumvent such difﬁculties, since the 1980s many apparel companies have adopted Quick Response (QR), a strategy focusing on cutting down lead-time. QR can enable retailers to postpone their order closer to the start of the selling season and to collect more information about demand. It is reported that forecast error can be reduced from 65% to 35% when lead-time is decreased
$ The ﬁrst author is supported by the National Natural Science Foundation of China (Nos. 71132008 and 71072029) and the MOE Project Key Research Institute of Humanities and Social Sciences at Universities (11JJD630004); the second author is supported by Hong Kong RGC Grant (No. CityU 144209) and grants from City University of Hong Kong (Nos. 7002517 and 7008116); and the third author is supported by the National Natural Science Foundation of China (Nos. 70890082 and 71232007) and Tsinghua University Initiative Scientiﬁc Research Program (No. 20101081741). n Corresponding author. E-mail address: firstname.lastname@example.org (B. Shou).
from eight months to four months; and forecast error might be reduced from 40% to 20% when lead-time is decreased from six months to four months (Crafted with Pride, Inc, 1985; Blackburn, 1991). For example, after adopting QR, Zara had only 15–20% of its sale at markdown prices, compared to 30–40%...
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