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doi: 10.3934/jimo.2021130
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Optimization of the product service supply chain under the influence of presale services

Xiaohui Ren 1, , Daofang Chang 1, and  Jin Shen 2,,

1. 

Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
2. 

School of Business, Shanghai Dianji University, Shanghai 201306, China

* Corresponding author: Jin Shen

Received  November 2020 Revised  April 2021 Early access August 2021

Fund Project: This research is supported by Natural Science Foundation of Shanghai (No: 18ZR1413200), Science and Technology Ministry of China for Cruise Program (No: MC-201917-C09), Shanghai Philosophy and Social Science Planning Project (No: 2020BGL030), Humanities and Social Sciences Project of the Ministry of Education (No: 20YJCZH027)

For some high-value and technology-intensive products, customers first ask service integrators to provide presales consulting services for products with potential demand. Improving the service level of presales service will increase service costs and reduce profits, but it can also increase the demand for products. The change in market demand under the influence of services will result in a series of chain reactions, such as changes in supply chain inventory costs and distribution costs. Thus, this paper considers the changes in the product service supply chain (PSSC) network caused by changes in presale service levels and service prices from the overall perspective of the supply chain and chooses a reasonable service level and price so that service integrators and product suppliers in PSSCs can achieve a win-win situation while meeting customer needs. First, a PSSC network optimization model is established considering the presale service level and price. Then, a double-layer nested genetic algorithm with constraint reasoning is proposed to solve this problem. Finally, by calculating the PSSC case of a building material company that produces a water mist spray system for ships, the feasibility and practicability of the algorithm was verified.

Keywords: Product service supply chain, presales service, service level, service price, service integrator.
Mathematics Subject Classification: 90B06.
Citation: Xiaohui Ren, Daofang Chang, Jin Shen. Optimization of the product service supply chain under the influence of presale services. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2021130
References:
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S. Axsäter, Using the deterministic eoq formula in stochastic inventory control, Management Science, 42 (1996), 830-834.   Google Scholar

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P. J. Colen and M. R. Lambrecht, Product service systems: Exploring operational practices, The Service Industries Journal, 33 (2013), 501-515.  doi: 10.1080/02642069.2011.614344.  Google Scholar

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B. Dan, H. Gao, Y. Zhang, R. Liu and S. Ma, Integrated order acceptance and scheduling decision making in product service supply chain with hard time windows constraints, J. Ind. Manag. Optim., 14 (2018), 165-182.  doi: 10.3934/jimo.2017041.  Google Scholar

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[21]

K. Li, S. Mallik and D. Chhajed, Design of extended warranties in supply chains under additive demand, Production & Operations Management, 21 (2012), 730-746.  doi: 10.1111/j.1937-5956.2011.01300.x.  Google Scholar

[22]

J. Little and E. Tsang, Foundations of constraint satisfaction, Science Direct. Google Scholar

[23]

H. Lockett, M. Johnson, S. Evans and M. Bastl, Product service systems and supply network relationships: an exploratory case study, Journal of Manufacturing Technology Management, 22 (2011), 293-313.  doi: 10.1108/17410381111112684.  Google Scholar

[24]

e. a. Luo, Equilibrium decisions of product service supply chain netword considering service outsourcing, Computer Integrated Manufacturing Systems, 27 (2020), 260-268.   Google Scholar

[25]

O. K. Mont, Clarifying the concept of product-service system, Journal of Cleaner Production, 10 (2002), 237-245.  doi: 10.1016/S0959-6526(01)00039-7.  Google Scholar

[26]

Y. Peng, D. Xu, Y. Li and K. Wang, A product service supply chain network equilibrium model considering capacity constraints, Math. Probl. Eng., 2020 (2020), Art. ID 1295072, 15 pp. doi: 10.1155/2020/1295072.  Google Scholar

[27]

G. Ryzin, Analyzing inventory cost and service in supply chains., Google Scholar

[28]

J. Shen, J. A. Erkoyuncu, R. Roy and B. Wu, A framework for cost evaluation in product service system configuration, International Journal of Production Research, 55 (2017), 6120-6144.  doi: 10.1080/00207543.2017.1325528.  Google Scholar

[29]

Z. Shuai, X. Song, W. Zhang, D. J. Yu and C. Kai, A hybrid approach combining an extended bbo algorithm with an intuitionistic fuzzy entropy weight method for qos-aware manufacturing service supply chain optimization, Neurocomputing, 272 (2017), 439-452.   Google Scholar

[30]

J. Sun, T. Qu, D. Nie and P. Li, Research on "location-inventory" problem of spare parts supply chain based on product service system, Procedia CIRP, 83 (2019), 819-825.  doi: 10.1016/j.procir.2019.05.024.  Google Scholar

[31]

Y. Wang, L. Sun, R. Qu and G. Li, Price and service competition with maintenance service bundling, Journal of Systems Science & Systems Engineering, 24 (2015), 168-189.  doi: 10.1007/s11518-015-5267-z.  Google Scholar

[32]

C.-H. Wu, Price and service competition between new and remanufactured products in a two-echelon supply chain, International Journal of Production Economics, 140 (2012), 496-507.  doi: 10.1016/j.ijpe.2012.06.034.  Google Scholar

[33]

W. Xie, Y. Zhao, Z. Jiang and P.-S. Chow, Optimizing product service system by franchise fee contracts under information asymmetry, Ann. Oper. Res., 240 (2016), 709-729.  doi: 10.1007/s10479-013-1505-2.  Google Scholar

[34]

D. Yang, J. Jiao, Y. Ji, G. Du, P. Helo and A. Valente, Joint optimization for coordinated configuration of product families and supply chains by a leader-follower stackelberg game, European J. Oper. Res., 246 (2015), 263-280.  doi: 10.1016/j.ejor.2015.04.022.  Google Scholar

[35]

E. A. Zhang, Research on cross-chain coordination mechanism of logistics service supply chain considering operational risks, Highway Transportation Science and Technology, 36 (2019), 135-143.   Google Scholar

[36]

D. Zhao, X. Zhang, T. Ren and H. Fu, Optimal pricing strategies in a product and service supply chain with extended warranty service competition considering retailer fairness concern, Math. Probl. Eng., 2019 (2019), Art. ID 8657463, 15 pp. doi: 10.1155/2019/8657463.  Google Scholar

show all references

References:
[1]

S. Axsäter, Using the deterministic eoq formula in stochastic inventory control, Management Science, 42 (1996), 830-834.   Google Scholar

[2]

T. Baines, H. Lightfoot and P. Smart, Servitization within manufacturing, Journal of Manufacturing Technology Management, 22 (2011), 947-954.  doi: 10.1108/17410381111160988.  Google Scholar

[3]

J. F. Bard and J. E. Falk, An explicit solution to the multi-level programming problem, Comput. Oper. Res., 9 (1982), 77-100.  doi: 10.1016/0305-0548(82)90007-7.  Google Scholar

[4]

W. Candler and R. Townsley, A linear two - level programming problem, Comput. Oper. Res., 9 (1982), 59-76.  doi: 10.1016/0305-0548(82)90006-5.  Google Scholar

[5]

M. S. Chen and C. T. Lin, Effects of centralization on expected costs in a multi-location newsboy problem, J Oper Res Soc, 755–761. Google Scholar

[6]

P. J. Colen and M. R. Lambrecht, Product service systems: Exploring operational practices, The Service Industries Journal, 33 (2013), 501-515.  doi: 10.1080/02642069.2011.614344.  Google Scholar

[7]

B. Dan, H. Gao, Y. Zhang, R. Liu and S. Ma, Integrated order acceptance and scheduling decision making in product service supply chain with hard time windows constraints, J. Ind. Manag. Optim., 14 (2018), 165-182.  doi: 10.3934/jimo.2017041.  Google Scholar

[8]

C. C. Fang, Optimal price and warranty decision for durable products in a competitive duopoly market - sciencedirect, Reliability Engineering & System Safety, 203. Google Scholar

[9]

H. Gebauer, A. Gustafsson and L. Witell, Competitive advantage through service differentiation by manufacturing companies, Journal of Business Research, 64 (2011), 1270-1280.  doi: 10.1016/j.jbusres.2011.01.015.  Google Scholar

[10]

J. A. Guajardo, Pay-as-you-go business models in developing economies: Consumer behavior and repayment performance, Social Science Electronic Publishing, 62 (2016), 1860-1877.   Google Scholar

[11]

J. A. Guajardo, M. A. Cohen and S. Netessine, Service competition and product quality in the us automobile industry, Management Science, 66 (2012), 1-32.   Google Scholar

[12]

Gu pta and Di wakar, Flexible carrier-forwarder contracts for air cargo business, Journal of Revenue & Pricing Management, 7 (2008), 341-356.   Google Scholar

[13]

J. R. Jiao, Q. Xu, Z. Wu and N. K. Ng, Coordinating product, process, and supply chain decisions: A constraint satisfaction approach, Engineering Applications of Artificial Intelligence, 22 (2009), 992-1004.  doi: 10.1016/j.engappai.2009.02.002.  Google Scholar

[14]

M. Johnson and C. Mena, Supply chain management for servitised products: A multi-industry case study, International Journal of Production Economics, 114 (2008), 27-39.  doi: 10.1016/j.ijpe.2007.09.011.  Google Scholar

[15]

U. Karmarkar, Will you survive the services revolution?, Harvard Business Review, 82 (2004), 100-107.   Google Scholar

[16]

V. B. Kreng and T. P. Lee, Modular product design with grouping genetic algorithm-a case study, Computers & Industrial Engineering, 46 (2004), 443-460.  doi: 10.1016/j.cie.2004.01.007.  Google Scholar

[17]

Kumar, Mukesh, Harrington, Toms, Seosamh, Srai, Jagjit, Singh, Yuto and Minakata., Industrial system dynamics for environmental sustainability: A case study on the uk medical technology sector., International Journal of Manufacturing Technology & Management, 31 (2017), 100–132. Google Scholar

[18]

H. Kurata and S.-H. Nam, After-sales service competition in a supply chain: Optimization of customer satisfaction level or profit or both?, International Journal of Production Economics, 127 (2010), 136-146.  doi: 10.1016/j.ijpe.2010.05.005.  Google Scholar

[19]

Z. L., Service-oriented manufacturing: The new tool of enterprise competition, Chinese Mechanics Industry, 12 (2007), 16-17.   Google Scholar

[20]

G. Li, F. F. Huang, T. C. E. Cheng, Q. Zheng and P. Ji, Make-or-buy service capacity decision in a supply chain providing after-sales service, European Journal of Operational Research, 239 (2014), 377-388.  doi: 10.1016/j.ejor.2014.05.035.  Google Scholar

[21]

K. Li, S. Mallik and D. Chhajed, Design of extended warranties in supply chains under additive demand, Production & Operations Management, 21 (2012), 730-746.  doi: 10.1111/j.1937-5956.2011.01300.x.  Google Scholar

[22]

J. Little and E. Tsang, Foundations of constraint satisfaction, Science Direct. Google Scholar

[23]

H. Lockett, M. Johnson, S. Evans and M. Bastl, Product service systems and supply network relationships: an exploratory case study, Journal of Manufacturing Technology Management, 22 (2011), 293-313.  doi: 10.1108/17410381111112684.  Google Scholar

[24]

e. a. Luo, Equilibrium decisions of product service supply chain netword considering service outsourcing, Computer Integrated Manufacturing Systems, 27 (2020), 260-268.   Google Scholar

[25]

O. K. Mont, Clarifying the concept of product-service system, Journal of Cleaner Production, 10 (2002), 237-245.  doi: 10.1016/S0959-6526(01)00039-7.  Google Scholar

[26]

Y. Peng, D. Xu, Y. Li and K. Wang, A product service supply chain network equilibrium model considering capacity constraints, Math. Probl. Eng., 2020 (2020), Art. ID 1295072, 15 pp. doi: 10.1155/2020/1295072.  Google Scholar

[27]

G. Ryzin, Analyzing inventory cost and service in supply chains., Google Scholar

[28]

J. Shen, J. A. Erkoyuncu, R. Roy and B. Wu, A framework for cost evaluation in product service system configuration, International Journal of Production Research, 55 (2017), 6120-6144.  doi: 10.1080/00207543.2017.1325528.  Google Scholar

[29]

Z. Shuai, X. Song, W. Zhang, D. J. Yu and C. Kai, A hybrid approach combining an extended bbo algorithm with an intuitionistic fuzzy entropy weight method for qos-aware manufacturing service supply chain optimization, Neurocomputing, 272 (2017), 439-452.   Google Scholar

[30]

J. Sun, T. Qu, D. Nie and P. Li, Research on "location-inventory" problem of spare parts supply chain based on product service system, Procedia CIRP, 83 (2019), 819-825.  doi: 10.1016/j.procir.2019.05.024.  Google Scholar

[31]

Y. Wang, L. Sun, R. Qu and G. Li, Price and service competition with maintenance service bundling, Journal of Systems Science & Systems Engineering, 24 (2015), 168-189.  doi: 10.1007/s11518-015-5267-z.  Google Scholar

[32]

C.-H. Wu, Price and service competition between new and remanufactured products in a two-echelon supply chain, International Journal of Production Economics, 140 (2012), 496-507.  doi: 10.1016/j.ijpe.2012.06.034.  Google Scholar

[33]

W. Xie, Y. Zhao, Z. Jiang and P.-S. Chow, Optimizing product service system by franchise fee contracts under information asymmetry, Ann. Oper. Res., 240 (2016), 709-729.  doi: 10.1007/s10479-013-1505-2.  Google Scholar

[34]

D. Yang, J. Jiao, Y. Ji, G. Du, P. Helo and A. Valente, Joint optimization for coordinated configuration of product families and supply chains by a leader-follower stackelberg game, European J. Oper. Res., 246 (2015), 263-280.  doi: 10.1016/j.ejor.2015.04.022.  Google Scholar

[35]

E. A. Zhang, Research on cross-chain coordination mechanism of logistics service supply chain considering operational risks, Highway Transportation Science and Technology, 36 (2019), 135-143.   Google Scholar

[36]

D. Zhao, X. Zhang, T. Ren and H. Fu, Optimal pricing strategies in a product and service supply chain with extended warranty service competition considering retailer fairness concern, Math. Probl. Eng., 2019 (2019), Art. ID 8657463, 15 pp. doi: 10.1155/2019/8657463.  Google Scholar

Figure 1.  Large complex equipment PSSC
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Figure 2.  GA encoding for PSSC
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Figure 3.  Algorithm flowchart
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Figure 4.  Searching process of optimal solution by genetic algorithm
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Figure 5.  Searching process of optimal service price and level of genetic algorithm
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Figure 6.  Brief PSSC network diagram
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Figure 7.  Search process of the classic double nested genetic algorithm
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Figure 8.  Search process of the adaptive genetic algorithm
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Table 1.  Supply chain nodes and main function
Node Main Function
product supplier Providing products, meeting the product demand of the regional warehouse node
service integrator Setting up offices at the service warehouse node to provide services, selling products and giving product demand order to product supplier
regional warehouse Meeting the needs of service warehouse node products
service warehouse Distributing products to customers, provide customers with presale services
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Node Main Function
product supplier Providing products, meeting the product demand of the regional warehouse node
service integrator Setting up offices at the service warehouse node to provide services, selling products and giving product demand order to product supplier
regional warehouse Meeting the needs of service warehouse node products
service warehouse Distributing products to customers, provide customers with presale services
Table 2.  Regional warehouse node parameter table
Parameters Regional Warehouse
Node 1.5 1 1 1.3 1
Unit inventory cost 38 42 40 35 45
Mean demand 1 1 1 1 1
Lead time 2 2 2 2 2
Counting cycle 4 4.5 5.5 5 5
Distribution cost 155 156 200 160 170
Facility fixed cost 1.5 1 1 1.3 1
$ Z_{\alpha} $ 0.9 0.9 0.9 0.9 0.9
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Parameters Regional Warehouse
Node 1.5 1 1 1.3 1
Unit inventory cost 38 42 40 35 45
Mean demand 1 1 1 1 1
Lead time 2 2 2 2 2
Counting cycle 4 4.5 5.5 5 5
Distribution cost 155 156 200 160 170
Facility fixed cost 1.5 1 1 1.3 1
$ Z_{\alpha} $ 0.9 0.9 0.9 0.9 0.9
Table 3.  Service warehouse node S1-S7 parameter table
Parameters Service Warehouse
Node S1 S2 S3 S4 S5 S6 S7
Unit inventory cost 1.2 1.5 1.5 1 1.1 1.7 1.5
Unit replenishment cost 2.7 2.7 2.7 2.9 2.9 2.9 3
Mean demand 15 14 16 20 18 17 15
Lead time 1 1 1 1 1 1 1
Counting cycle 2 2 2 2 2 2 2
Facility fixed cost 150 157 160 155 160 170 190
$ \beta $ 1 1 1 1 1 1 1
$ Z_{\alpha} $ 0.9 0.9 0.9 0.9 0.9 0.9 0.9
$ \mu $ 0.65 0.65 0.65 0.65 0.65 0.65 0.65
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Parameters Service Warehouse
Node S1 S2 S3 S4 S5 S6 S7
Unit inventory cost 1.2 1.5 1.5 1 1.1 1.7 1.5
Unit replenishment cost 2.7 2.7 2.7 2.9 2.9 2.9 3
Mean demand 15 14 16 20 18 17 15
Lead time 1 1 1 1 1 1 1
Counting cycle 2 2 2 2 2 2 2
Facility fixed cost 150 157 160 155 160 170 190
$ \beta $ 1 1 1 1 1 1 1
$ Z_{\alpha} $ 0.9 0.9 0.9 0.9 0.9 0.9 0.9
$ \mu $ 0.65 0.65 0.65 0.65 0.65 0.65 0.65
Table 4.  Service warehouse node S8-S14 parameter table
Parameters Service Warehouse
Node S8 S9 S10 S11 S12 S13 S14
Unit inventory cost 0.9 1 1 1.5 1.3 1.3 1
Unit replenishment cost 3 3 2.9 2.9 3 3 3
Mean demand 16 14 15 17 15 20 18
Lead time 1 1 1 1 1 1 1
Counting cycle 2 2 2 2 2 2 2
Facility fixed cost 157 160 180 170 165 170 160
$ \beta $ 1 1 1 1 1 1 1
$ Z_{\alpha} $ 0.9 0.9 0.9 0.9 0.9 0.9 0.9
$ \mu $ 0.65 0.65 0.65 0.65 0.65 0.65 0.65
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Parameters Service Warehouse
Node S8 S9 S10 S11 S12 S13 S14
Unit inventory cost 0.9 1 1 1.5 1.3 1.3 1
Unit replenishment cost 3 3 2.9 2.9 3 3 3
Mean demand 16 14 15 17 15 20 18
Lead time 1 1 1 1 1 1 1
Counting cycle 2 2 2 2 2 2 2
Facility fixed cost 157 160 180 170 165 170 160
$ \beta $ 1 1 1 1 1 1 1
$ Z_{\alpha} $ 0.9 0.9 0.9 0.9 0.9 0.9 0.9
$ \mu $ 0.65 0.65 0.65 0.65 0.65 0.65 0.65
Table 5.  Unit delivery cost from regional warehouse node to service warehouse node
Node R1 R2 R3 R4 R5 Node R1 R2 R3 R4 R5
S1 1 4 2.3 3 3.5 S8 4.3 2.5 5 4 2.5
S2 2 3.5 3 4 3 S9 3 4 2.5 3.5 2
S3 1 2 3.5 1.3 4 S10 1.5 3 1.5 2.6 4
S4 1.3 3 2 5 4 S11 2.3 3 1.5 4 5
S5 3.5 2 1.3 4 5 S12 4 3 1.5 1.5 1.5
S6 3 1.5 2 2.6 1 S13 2 4 2.6 3 1.6
S7 2 2.6 1 3.3 4 S14 4 2.5 3 5 1
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Node R1 R2 R3 R4 R5 Node R1 R2 R3 R4 R5
S1 1 4 2.3 3 3.5 S8 4.3 2.5 5 4 2.5
S2 2 3.5 3 4 3 S9 3 4 2.5 3.5 2
S3 1 2 3.5 1.3 4 S10 1.5 3 1.5 2.6 4
S4 1.3 3 2 5 4 S11 2.3 3 1.5 4 5
S5 3.5 2 1.3 4 5 S12 4 3 1.5 1.5 1.5
S6 3 1.5 2 2.6 1 S13 2 4 2.6 3 1.6
S7 2 2.6 1 3.3 4 S14 4 2.5 3 5 1
Table 6.  Algorithm result analysis table
Population size Genetic algebra The optimal value Optimal value first out of modern number Operation time/s
10 400 643 365 1.283
500 643 365 3.568
600 643 415 5.433
20 400 643 370 0.711
500 643 427 2.546
600 643 227 4.653
30 400 586 130 1.263
500 643 380 2.374
600 643 370 4.538
40 400 643 270 0.843
500 597 355 2.176
550 643 343 4.136
50 400 643 350 0.834
450 643 275 1.571
500 643 325 2.283
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Population size Genetic algebra The optimal value Optimal value first out of modern number Operation time/s
10 400 643 365 1.283
500 643 365 3.568
600 643 415 5.433
20 400 643 370 0.711
500 643 427 2.546
600 643 227 4.653
30 400 586 130 1.263
500 643 380 2.374
600 643 370 4.538
40 400 643 270 0.843
500 597 355 2.176
550 643 343 4.136
50 400 643 350 0.834
450 643 275 1.571
500 643 325 2.283
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