doi: 10.3934/jimo.2021204
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The optimal product-line design and incentive mechanism in a supply chain with customer environmental awareness

School of Business Administration, Northeastern University, Shenyang, China

* Corresponding author: Cui-hua Zhang

Received  April 2021 Revised  October 2021 Early access November 2021

Fund Project: The first author is supported by National Natural Science Foundation of China(NSFC) grant 71771044

Due to the increasing awareness of sustainable development, the manufacturer's product-line design gets wide attention. Nowadays, the traditional manufacturer that produces non-green products is considering whether to introduce upgraded green products. This paper studies the manufacturer's optimal product-line design considering the quality difference between non-green and green products. Besides, our model also investigates the difference in unit production cost, green research and development (R&D) investment, and market segmentation. The results show that, from the manufacturer's perspective, producing green products is a better choice when non-green products are of low quality. In addition, the retailer is always inclined to sell green products. Further, the consumers' preference for non-green and green products is divided. And the consumer surplus under different product-line designs is analysed. Finally, two contracts are proposed and compared to encourage the manufacturer to produce green products.

Citation: Zhi-tang Li, Cui-hua Zhang, Wei Kong, Ru-xia Lyu. The optimal product-line design and incentive mechanism in a supply chain with customer environmental awareness. Journal of Industrial and Management Optimization, doi: 10.3934/jimo.2021204
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show all references

References:
[1]

E. Alkaya and G. Demirer, Greening of production in metal processing industry through process modifications and improved management practices, Resources, Conservation and Recycling, 77 (2013), 89-96.  doi: 10.1016/j.resconrec.2013.06.004.

[2]

A. Atasu and R. Subramanian, Extended producer responsibility for e-waste: Individual or collective producer responsibility?, Production and Operations Management, 21 (2012), 1042-1059.  doi: 10.1111/j.1937-5956.2012.01327.x.

[3]

Z. Basiri and J. Heydari, A mathematical model for green supply chain coordination with substitutable products, Journal of Cleaner Production, 145 (2017), 232-249.  doi: 10.1016/j.jclepro.2017.01.060.

[4]

D. BuccellaL. Fanti and L. Gori, To abate, or not to abate? A strategic approach on green production in Cournot and Bertrand duopolies, Energy Economics, 96 (2021), 105164.  doi: 10.1016/j.eneco.2021.105164.

[5]

C. Chen, Design for the environment: A quality-based Model for green product development, Management Science, 47 (2001), 250-263.  doi: 10.1287/mnsc.47.2.250.9841.

[6]

J. ChenS. Dimitrov and H. Pun, The impact of government subsidy on supply Chains' sustainability innovation, Omega, 86 (2019), 42-58.  doi: 10.1016/j.omega.2018.06.012.

[7]

Y. ChenB. Tomlin and Y. Wang, Coproduct technologies: Product line design and process innovation, Management Science, 59 (2013), 2772-2789.  doi: 10.1287/mnsc.2013.1738.

[8]

X. ChenX. Wang and M. Zhou, Firms' green R&D cooperation behaviour in a supply chain: Technological spillover, power and coordination, International Journal of Production Economics, 218 (2019), 118-134.  doi: 10.1016/j.ijpe.2019.04.033.

[9]

M. C. CohenR. Lobel and G. Perakis, The impact of demand uncertainty on consumer subsidies for green technology adoption, Management Science, 62 (2015), 1235-1258.  doi: 10.1287/mnsc.2015.2173.

[10]

M. FergusonV. D. GuideE. Koca and G. C. Souza, The value of quality grading in remanufacturing, Production and Operations Management, 18 (2009), 300-314.  doi: 10.1111/j.1937-5956.2009.01033.x.

[11]

D. Ghosh and J. Shah, Supply chain analysis under green sensitive consumer demand and cost sharing contract, International Journal of Production Economics, 164 (2015), 319-329.  doi: 10.1016/j.ijpe.2014.11.005.

[12]

A. Goli and B. Malmir, A covering tour approach for disaster relief locating and routing with fuzzy demand, International J. Intelligent Transportation Systems Research, 18 (2020), 140-152.  doi: 10.1007/s13177-019-00185-2.

[13]

A. Goli, E. B. Tirkolaee and N. S. Aydin, Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors, IEEE Transactions on Fuzzy Systems, (2021), 1–1. doi: 10.1109/TFUZZ.2021.3053838.

[14]

A. GoliH. K. ZareR. T. Moghaddam and A. Sadeghieh, Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm, Numerical Algebra, 9 (2019), 187-209.  doi: 10.3934/naco.2019014.

[15]

A. Goli, H. K. Zare, R. T. Moghaddam and A. Sadeghieh, A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry, J. Industrial and Systems Engineering, 11 (2018), 190-203. doi: https://mpra.ub.uni-muenchen.de/id/eprint/101727.

[16]

A. GoliH. K. ZareR. T. MoghaddamA. SadeghiehM. Sasanian and R. M. Kordestanizadeh, An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand fordairy products: A case study, Network Computation in Neural Systems, 32 (2021), 1-35.  doi: 10.1080/0954898X.2020.1849841.

[17]

S. K. GoudaS. Jonnalagedda and H. Saranga, Design for the environment: Impact of regulatory policies on product development, European J. Oper. Res., 248 (2016), 558-570.  doi: 10.1016/j.ejor.2015.07.043.

[18]

K. Govindan and M. Hasanagic, A systematic review on drivers, barriers, and practices towards circular economy: A supply chain perspective, International J. Production Research, 56 (2018), 278-311.  doi: 10.1080/00207543.2017.1402141.

[19]

S. GuoT. M. Choi and B. Shen, Green product development under competition: A study of the fashion apparel industry, European J. Oper. Res., 280 (2020), 523-538.  doi: 10.1016/j.ejor.2019.07.050.

[20]

L. Guo and J. Zhang, Consumer deliberation and product line design, Marketing Science, 31 (2012), 995-1007.  doi: 10.1287/mksc.1120.0736.

[21]

P. HeY. HeC. ShiH. Xu and L. Zhou, Cost-sharing contract design in a low-carbon service supply chain, Computers & Industrial Engineering, 139 (2020), 106160.  doi: 10.1016/j.cie.2019.106160.

[22]

P. HeY. He and H. Xu, Channel structure and pricing in a dual-channel closed-loop supply chain with government subsidy, International Journal of Production Economics, 213 (2019), 108-123.  doi: 10.1016/j.ijpe.2019.03.013.

[23]

P. HeJ. Zhang and W. Li, The role of agricultural green production technologies in improving low-carbon efficiency in China: Necessary but not effective, J. Environmental Management, 293 (2021), 112837.  doi: 10.1016/j.jenvman.2021.112837.

[24]

Z. Hong and X. Guo, Green product supply chain contracts considering environmental responsibilities, Omega, 83 (2019), 155-166.  doi: 10.1016/j.omega.2018.02.010.

[25]

L. Hsiao and Y. Chen, Strategic motive for introducing internet channels in a supply chain, Production and Operations Management, 23 (2014), 36-47.  doi: 10.1111/poms.12051.

[26]

D. KrassT. Nedorezov and A. Ovchinnikov, Environmental taxes and the choice of green technology, Production and Operations Management, 22 (2013), 1035-1055.  doi: 10.1111/poms.12023.

[27]

Q. LiX. GuanT. Shi and W. Jiao, Green product design with competition and fairness concerns in the circular economy era, J. Operational Research, 58 (2020), 165-179.  doi: 10.1080/00207543.2019.1657249.

[28]

Q. LiT. Xiao and Y. Qiu, Price and carbon emission reduction decisions and revenue-sharing contract considering fairness concerns, J. Cleaner Production, 190 (2018), 303-314.  doi: 10.1016/j.jclepro.2018.04.032.

[29]

X. Li and Y. Li, Chain-to-chain competition on product sustainability, J. Cleaner Production, 112 (2016), 2058-2065.  doi: 10.1016/j.jclepro.2014.09.027.

[30]

W. S. Lim, V. Mak, C. S. Tang and R. P. Kc, Adopting cost transparency as a marketing strategy: Analytical and experimental exploration, SSRN Electronic Journal, 2018 doi: 10.2139/ssrn.3252823.

[31]

Y. LiuD. SunH. WangX. WangG. Yu and X. Zhao, An evaluation of China's agricultural green production: 1978-2017, J. Cleaner Production, 243 (2020), 118483.  doi: 10.1016/j.jclepro.2019.118483.

[32]

K. J. MizgierJ. M. Pasia and S. Talluri, Multiobjective capital allocation for supplier development under risk, International Journal of Production Research, 55 (2017), 5243-5258.  doi: 10.1080/00207543.2017.1302618.

[33]

K. J. MizgierS. M. Wagner and J. A. Holyst, Modeling defaults of companies in multi-stage supply chain networks, International Journal of Production Economics, 135 (2012), 14-23.  doi: 10.1016/j.ijpe.2010.09.022.

[34]

I. MoonY. J. Jeong and S. Saha, Investment and coordination decisions in a supply chain of fresh agricultural products, Operational Research, 20 (2020), 2307-2331.  doi: 10.1007/s12351-018-0411-4.

[35]

K. MuraliM. K. Lim and N. C. Petruzzi, The effects of ecolabels and environmental regulation on green product development, Manufacturing & Service Operations Management, 21 (2018), 519-535.  doi: 10.2139/ssrn.2821252.

[36]

H. MuslemaniX. LiangK. KaesehageF. Ascui and J. Wilson, Opportunities and challenges for decarbonizing steel production by creating markets for 'green steel' products, J. Cleaner Production, 315 (2021), 128127.  doi: 10.1016/j.jclepro.2021.128127.

[37]

M. C. OlsenR. J. Slotegraaf and S. R. Chandukala, Green claims and message frames: How green new products change brand attitude, J. Marketing, 78 (2014), 119-137.  doi: 10.1509/jm.13.0387.

[38]

Y. OzinciY. Perlman and S. Westrich, Competition between organic and conventional products with different utilities and shelf lives, International J. Production Economics, 191 (2017), 74-84.  doi: 10.1016/j.ijpe.2017.05.005.

[39]

S. M. Pahlevan, S. Hosseini and A. Goli, Sustainable supply chain network design using products' life cycle in the aluminum industry, Environmental Science and Pollution Research, (2021). doi: 10.1007/s11356-020-12150-8.

[40]

J. Peuckert and R. Quitzow, Acceptance of bio–based products in the business–to–business market and public procurement: Expert survey results, Biofuels, Bioproducts and Biorefining, 11 (2017), 92-109.  doi: 10.1002/bbb.1725.

[41]

B. ShenY. Cao and X. Xu, Product line design and quality differentiation for green and non-green products in a supply chain, International J. Production Research, 58 (2020), 148-164.  doi: 10.1080/00207543.2019.1656843.

[42]

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Figure 1.  The flow diagram of implement methodology
Figure 2.  Product-line design and the sequence of events
Figure 3.  Impact of $ q_g $ and $ q_n $ on product-line selection
Figure 4.  Impact of $ q_g $ and $ q_n $ on consumer surplus
Figure 5.  Impact of $ q_g $ on the level of green innovation effort
Figure 6.  Impact of $ q_g $ on the profit under the contract
Figure 7.  Impact of $ k $ on the profit
Figure 8.  Impact of $ \gamma $ on the profit
Figure 9.  Impact of $ \theta $ on the profit
Figure 10.  Revenue Share of Green Products in Philips Business Division 2017-2019 (Unit: million Euros)
Table 1.  Practices of the enterprise's green production
Industry The practice of green production
Automobile SAIC General Motors promotes the strategy of "greening the future". Buick and Chevrolet's 1.2L-1.6L vehicles are equipped with EcotecDVVT and S-TEC engines. Compared with the products of the same level, the manual gearboxes are high performance and fuel-efficient(https://www.saic-gm.com/www/web/saic-gm/satep.).
Household appliance Philips uses green technology ranging from energy-saving lighting to TV. Compared with traditional products, its energy-saving bulbs can save 80% of electrical energy and provide sustainable lighting solutions(https://www.philips.com.cn/a-w/about-philips/sustainability.html.)
Commodity Procter & Gamble reduces the use of materials by 10% through the optimization of packaging material and the weight of each diaper is greatly decreased by technological innovation (https://www.pg.com.cn/Csr/Product.aspx.).
Agriculture The role of agricultural green production technologies (AGPTs) adoption rates improves low-carbon efficiency in China[8]. Green production is widely mentioned in the field of agriculture [9,10]
Steel production Decarburization technology leads the development of green steel [11].
Metal processing Environmental performance in the metal processing industry could be improved by green technologies (process modifications and management practices) [12].
Fashion apparels H & M, Marks & Spencer, and Levi's, have produced low-carbon products using new technology to reduce carbon emissions during production [13,14].
Industry The practice of green production
Automobile SAIC General Motors promotes the strategy of "greening the future". Buick and Chevrolet's 1.2L-1.6L vehicles are equipped with EcotecDVVT and S-TEC engines. Compared with the products of the same level, the manual gearboxes are high performance and fuel-efficient(https://www.saic-gm.com/www/web/saic-gm/satep.).
Household appliance Philips uses green technology ranging from energy-saving lighting to TV. Compared with traditional products, its energy-saving bulbs can save 80% of electrical energy and provide sustainable lighting solutions(https://www.philips.com.cn/a-w/about-philips/sustainability.html.)
Commodity Procter & Gamble reduces the use of materials by 10% through the optimization of packaging material and the weight of each diaper is greatly decreased by technological innovation (https://www.pg.com.cn/Csr/Product.aspx.).
Agriculture The role of agricultural green production technologies (AGPTs) adoption rates improves low-carbon efficiency in China[8]. Green production is widely mentioned in the field of agriculture [9,10]
Steel production Decarburization technology leads the development of green steel [11].
Metal processing Environmental performance in the metal processing industry could be improved by green technologies (process modifications and management practices) [12].
Fashion apparels H & M, Marks & Spencer, and Levi's, have produced low-carbon products using new technology to reduce carbon emissions during production [13,14].
Table 2.  Relative running time of the considered filters
Paper Product-line design Green level Pricing strategy Quality-based model Coordination Game theory
Chen et al. [17] Y N Y N N N
Yenipazarli and Vakharia. [33] N N Y N N Y
Ozinci et al. [34] N N Y N N Y
Zhang et al. [18] Y Y Y N N N
Shen et al. [15] Y N Y Y N Y
Zhang et al. [35] N N Y N N Y
Zou et al. [16] Y N Y Y N Y
Tirkolaee et al. [39] N Y N N N N
Sinayi and Rasti-Barzoki. [41] N Y Y N Y Y
Chen et al. [42] N Y Y N Y Y
Yu et al. [43] N N Y N Y Y
Zhu et al. [44] N Y Y N N Y
Zu et al. [45] N N Y N N Y
Chen. [47] N N Y Y N Y
Zhang et al. [48] N N Y Y N Y
Gouda et al. [49] Y N Y Y N Y
Murali et al. [50] N N Y Y N Y
Xu et al. [19] N N Y N Y Y
Zhou and Ye. [20] N N Y N Y Y
He et al. [21] N N Y N Y Y
Li et al. [22] N N Y N Y Y
Yang et al. [23] N N Y N Y Y
Moon et al. [24] N N Y N Y Y
Our paper Y Y Y Y Y Y
Paper Product-line design Green level Pricing strategy Quality-based model Coordination Game theory
Chen et al. [17] Y N Y N N N
Yenipazarli and Vakharia. [33] N N Y N N Y
Ozinci et al. [34] N N Y N N Y
Zhang et al. [18] Y Y Y N N N
Shen et al. [15] Y N Y Y N Y
Zhang et al. [35] N N Y N N Y
Zou et al. [16] Y N Y Y N Y
Tirkolaee et al. [39] N Y N N N N
Sinayi and Rasti-Barzoki. [41] N Y Y N Y Y
Chen et al. [42] N Y Y N Y Y
Yu et al. [43] N N Y N Y Y
Zhu et al. [44] N Y Y N N Y
Zu et al. [45] N N Y N N Y
Chen. [47] N N Y Y N Y
Zhang et al. [48] N N Y Y N Y
Gouda et al. [49] Y N Y Y N Y
Murali et al. [50] N N Y Y N Y
Xu et al. [19] N N Y N Y Y
Zhou and Ye. [20] N N Y N Y Y
He et al. [21] N N Y N Y Y
Li et al. [22] N N Y N Y Y
Yang et al. [23] N N Y N Y Y
Moon et al. [24] N N Y N Y Y
Our paper Y Y Y Y Y Y
Table 3.  Notations for model parameters
Abbreviations Description
$ N/n $ The production of the non-green product
$ G/g $ The production of the green product
$ m $ The manufacturer
$ r $ The retailer
Variables
$ w_i $ The unit wholesale price of the product $ i $, where $ i=n,g $
$ p_i $ The unit retail price of the product $ i $, where $ i=n,g $
$ e $ Green effort of the green product
Parameters
$ q_i $ Product quality for the product $ i $, where $ i=n,g $
$ c_i $ The unit production cost for the product $ i $, where $ i=n,g $
$ v $ Consumer's willingness to pay for the product
$ k $ Coefficient of manufacturer's green R&D effort cost
$ \gamma $ Sensitivity coefficient of green R&D effort to market demand, $ \gamma > 0 $ denotes consumers' green preference towards the green effort.
$ \theta $ The proportion of green consumers in the market
Functions
$ U_{ns}^j $ Consumer's utility obtained from the product $ j $ in the non-green segment, where $ j=N,G $
$ U_{gs}^{j} $ Consumer's utility obtained from the product $ j $ in the green segment, where $ j=N,G $
$ D_i $ Demand for the product $ i $, where $ i=n,g $
$ \pi_m^j $ The profit of the manufacturer from producing the product $ j $, where $ j=N,G $
$ \pi_r^j $ The profit of the retailer from selling the product $ j $, where $ j=N,G $
$ CS^j $ Consumer surplus obtained from purchasing the product $ j $, where $ j=N,G $
Abbreviations Description
$ N/n $ The production of the non-green product
$ G/g $ The production of the green product
$ m $ The manufacturer
$ r $ The retailer
Variables
$ w_i $ The unit wholesale price of the product $ i $, where $ i=n,g $
$ p_i $ The unit retail price of the product $ i $, where $ i=n,g $
$ e $ Green effort of the green product
Parameters
$ q_i $ Product quality for the product $ i $, where $ i=n,g $
$ c_i $ The unit production cost for the product $ i $, where $ i=n,g $
$ v $ Consumer's willingness to pay for the product
$ k $ Coefficient of manufacturer's green R&D effort cost
$ \gamma $ Sensitivity coefficient of green R&D effort to market demand, $ \gamma > 0 $ denotes consumers' green preference towards the green effort.
$ \theta $ The proportion of green consumers in the market
Functions
$ U_{ns}^j $ Consumer's utility obtained from the product $ j $ in the non-green segment, where $ j=N,G $
$ U_{gs}^{j} $ Consumer's utility obtained from the product $ j $ in the green segment, where $ j=N,G $
$ D_i $ Demand for the product $ i $, where $ i=n,g $
$ \pi_m^j $ The profit of the manufacturer from producing the product $ j $, where $ j=N,G $
$ \pi_r^j $ The profit of the retailer from selling the product $ j $, where $ j=N,G $
$ CS^j $ Consumer surplus obtained from purchasing the product $ j $, where $ j=N,G $
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