|Table of Contents|

[1] Fei Qingyi, Kong Nan, Zhao Lindu,. Investment decision optimization for delayed productdifferentiation based on queuing theory [J]. Journal of Southeast University (English Edition), 2018, (4): 532-539. [doi:10.3969/j.issn.1003-7985.2018.04.017]
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Investment decision optimization for delayed productdifferentiation based on queuing theory()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
Issue:
2018 4
Page:
532-539
Research Field:
Economy and Management
Publishing date:
2018-12-20

Info

Title:
Investment decision optimization for delayed productdifferentiation based on queuing theory
Author(s):
Fei Qingyi1 Kong Nan2 Zhao Lindu1
1School of Economics and Management, Southeast University, Nanjing 211189, China
2Weldon School of Biomedical Engineering, Purdue University, West Lafayette IN 47907, USA
Keywords:
flexible manufacturing system postponement strategy order penetration point investment process matrix geometric method
PACS:
F273
DOI:
10.3969/j.issn.1003-7985.2018.04.017
Abstract:
To balance inventory cost with diverse demand, an optimal investment decision on necessary process improvement for delayed product differentiation is studied. A two-stage flexible manufacturing system is modeled as a continuous time Markov chain. The first production stage manufactures semi-finished products based on a make-to-stock policy. The second production stage customizes semi-finished products from the first production stage on a make-to-order policy. Various performance measures for this flexible manufacturing system are evaluated by using matrix geometric methods. An optimization model to determine the level of investment on process improvement that minimizes the manufacturer’s total cost is established. The results show that, a higher investment level can reduce both the expected customer order fulfillment delay and the expected semi-finished products inventory. When the initial order penetration point is 0.4, the manufacturer’s total cost is reduced by 15.89% through process investment. In addition, the optimal investment level increases with the increase in the unit time cost of customer order fulfillment delay, and decreases with the increase in the product value and the initial order penetration point.

References:

[1] Adler P S, Benner M, Brunner D J, et al. Perspectives on the productivity dilemma [J].Journal of Operations Management, 2009, 27(2): 99-113. DOI:10.1016/j.jom.2009.01.004.
[2] Lee H L, Tang C S. Modelling the costs and benefits of delayed product differentiation [J].Management Science, 1997, 43(1): 40-53. DOI:10.1287/mnsc.43.1.40.
[3] Perry J. Birthplace of the i3 [J].Automotive Manufacturing Solutions, 2014, 15(3): 12-14.
[4] Lee H L, Billington C, Carter B. Hewlett-Packard gains control of inventory and service through design for localization [J]. Interfaces, 1993, 23(4): 1-11. DOI:10.1287/inte.23.4.1.
[5] Jewkes E M, Alfa A S. A queueing model of delayed product differentiation [J]. European Journal of Operational Research, 2009, 199(3): 734-743. DOI:10.1016/j.ejor.2008.08.001.
[6] Pang R Y, Liu D C, Li Q, et al. Process flow reorder model based on cost control strategy[J]. Computer Integrated Manufacturing Systems, 2009, 15(7): 1286-1291, 1316.(in Chinese)
[7] Zhang M, Cheng W M, Zhang Z Q, et al. Supply chain postponement strategy model for mass customization[J]. Journal of Southwest Jiaotong University, 2011, 46(6): 1055-1059.(in Chinese)
[8] Shao Z F, Zhang T, Lin Y J. TFT-LCD production strategy optimization[J]. Computer Integrated Manufacturing Systems, 2011, 17(5): 1064-1070.(in Chinese)
[9] Shan D W, Chen W, Yang Y X. Application of postponement strategy in apparel supply chain management[J]. Journal of Textile Research, 2016, 37(4): 153-159. DOI:10.13475/j.fzxb.20150302407. (in Chinese)
[10] Liu W H, Wu R Z, Liang Z C, et al. Decision model for the customer order decoupling point considering order insertion scheduling with capacity and time constraints in logistics service supply chain [J]. Applied Mathematical Modelling, 2018, 54:112-135. DOI:10.1016/j.apm.2017.09.027.
[11] Youssef K H, van Delft C, Dallery Y. Priority optimization and make-to-stock/make-to-order decision in multiproduct manufacturing systems [J]. International Transactions in Operational Research, 2018, 25(4): 1199-1219. DOI:10.1111/itor.12464.
[12] Lee H L. Effective inventory and service management through product and process redesign [J]. Operations Research, 1996, 44(1): 151-159. DOI:10.1287/opre.44.1.151.
[13] Su J C P, Chang Y L, Ferguson M. Evaluation of postponement structures to accommodate mass customization [J]. Journal of Operations Management, 2005, 23(3/4): 305-318. DOI:10.1016/j.jom.2004.10.016.
[14] Ngniatedema T, Fono L A, Mbondo G D. A delayed product customization cost model with supplier delivery performance [J]. European Journal of Operational Research, 2015, 243(1): 109-119. DOI:10.1016/j.ejor.2014.11.017.
[15] Neuts M F. Matrix-geometric solutions in stochastic models:An algorithmic approach[M]. Baltimore, USA: Johns Hopkins University Press, 1981.
[16] Dye C Y, Hsieh T P. An optimal replenishment policy for deteriorating items with effective investment in preservation technology [J]. European Journal of Operational Research, 2012, 218(1): 106-112. DOI:10.1016/j.ejor.2011.10.016.
[17] Desai P, Kekre S, Radhakrishnan S, et al. Product differentiation and commonality in design: Balancing revenue and cost drivers [J]. Management Science, 2001, 47(1): 37-51. DOI:10.1287/mnsc.47.1.37.10672.

Memo

Memo:
Biographies: Fei Qingyi(1986—), female, Ph.D. candidate; Zhao Lindu(corresponding author), male, doctor, professor, ldzhao@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.71661147004).
Citation: Fei Qingyi, Kong Nan, Zhao Lindu.Investment decision optimization for delayed product differentiation based on queuing theory[J].Journal of Southeast University(English Edition), 2018, 34(4):532-539.DOI:10.3969/j.issn.1003-7985.2018.04.017.
Last Update: 2018-12-20