Remanufacturing with patented technique royalty under asymmetric information and uncertain markets
Abstract
We study a dual-channel recycling closed-loop supply chain (CLSC) and investigate the royalty strategy involving cost-reducing technique for remanufacturing patented products. Facing information asymmetry and market uncertainty, we address the problem where the patent licensor (manufacturer) and licensee (remanufacturer) simultaneously compete in the sales market and the recycling market. We examine the optimal decisions of a decentralized CLSC (D-CLSC) with the manufacturer being the Stackelberg leader. Numerical examples are used to demonstrate how the patented technology (cost-reducing technique) affects the channel players’ behaviors and how to identify the optimal royalty fee. Based on the theoretical derivation and the numerical outcomes, we find that regardless of the CLSC structure (centralized or decentralized), the take-back prices and the total profits will rise with the increase of savings from the licensed technology. In the D-CLSC, (i) the expected profits of the manufacturer and the remanufacturer as well as the royalty fee will also rise with the savings from the licensed technology. (ii) In addition, the wholesale price, retail price, take-back prices, as well as the royalty fee will rise with the degree of information asymmetry. But the retailer’s expected profit will decline. (iii) Finally, the expected profit of the manufacturer will rise with the demand uncertainty and the return uncertainty. For the remanufacturer, this trend is not obvious. Our research provides guidance to resolve conflicts and intellectual property disputes between the original manufacturer and the remanufacturer of the patented product.
First published online 21 June 2019
Keyword : closed-loop supply chain, remanufacturing, fuzzy decision, royalty licensing, game theory
How to Cite
Gao, J., Liang, Z., Shang, J., & Xu, Z. (2020). Remanufacturing with patented technique royalty under asymmetric information and uncertain markets. Technological and Economic Development of Economy, 26(3), 599-620. https://doi.org/10.3846/tede.2019.10287
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165-4176. https://doi.org/10.1016/j.apm.2012.09.039
Arora, A., & Ceccagnoli, M. (2006). Patent protection, complementary assets, and firms’ incentives for technology licensing. Management Science, 52(2), 293-308. https://doi.org/10.1287/mnsc.1050.0437
Bai, C., & Sarkis, J. (2018). Evaluating complex decision and predictive environments: the case of green supply chain flexibility. Technological and Economic Development of Economy, 24(4), 1630-1658. https://doi.org/10.3846/20294913.2018.1483977
Brousseau, E., Coeurderoy, R., & Chaserant, C. (2007). The governance of contracts: Empirical evidence on technology licensing agreements. Journal of Institutional and Theoretical Economics, 163(2), 205235. https://doi.org/10.1628/093245607781261379
Cui, Y. Y., Guan, Z., Saif, U., Zhang, L., Zhang, F., & Mirza, J. (2017). Close loop supply chain network problem with uncertainty in demand and returned products: Genetic artificial bee colony algorithm approach. Journal of Cleaner Production, 162, 717-742. https://doi.org/10.1016/j.jclepro.2017.06.079
Giri, B. C., & Sharma, S. (2016). Optimal production policy for a closed-loop hybrid system with uncertain demand and return under supply disruption. Journal of Cleaner Production, 112(Part 3), 2015-2028. https://doi.org/10.1016/j.jclepro.2015.06.147
Hamdouch, Y., Qiang, Q. P., & Ghoudi, K. (2017). A closed-loop supply chain equilibrium model with random and price-sensitive demand and return. Networks and Spatial Economics, 17(2), 459-503. https://doi.org/10.1007/s11067-016-9333-y
Hashiguchi, M. S. (2008). Recycling efforts and patent rights protection in the United States and Japan. Columbia Journal of Environmental Law, 33, 169-195.
Hong, X. P., Govindan, K., Xu, L., & Du, P. (2017). Quantity and collection decisions in a closed-loop supply chain with technology licensing. European Journal of Operational Research, 256(3), 820-829. https://doi.org/10.1016/j.ejor.2016.06.051
Keshavarz Ghorabaee, M., Amiri, M., Olfat, L., & Khatami Firouzabadi, S. A. (2017). Designing a multiproduct multi-period supply chain network with reverse logistics and multiple objectives under uncertainty. Technological and Economic Development of Economy, 23(3), 520-548. https://doi.org/10.3846/20294913.2017.1312630
Khaksar, E., Abbasnejad, T., Esmaeili, A., & Tamošaitienė, J. (2016). The effect of green supply chain management practices on environmental performance and competitive advantage: A case study of the cement industry. Technological and Economic Development of Economy, 22(2), 293-308. https://doi.org/10.3846/20294913.2015.1065521
Khatami, M., Mahootchi, M., & Farahani, R. Z. (2015). Benders’ decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties. Transportation Research Part E: Logistics and Transportation Review, 79, 1-21. https://doi.org/10.1016/j.tre.2015.03.003
Kim, J., Do Chung, B., Kang, Y., & Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of Cleaner Production, 196, 1314-1328. https://doi.org/10.1016/j.jclepro.2018.06.157
Lin, L. H., & Kulatilaka, N. (2006). Network effects and technology licensing with fixed fee, royalty, and hybrid contracts. Journal of Management Information Systems, 23(2), 91-118. https://doi.org/10.2753/MIS0742-1222230205
Liu, B. D. (2009). Theory and practice of uncertain programming (STUDFUZZ, Vol. 239). Berlin: Springer. https://doi.org/10.1007/978-3-540-89484-1
Liu, B. P. W. (2014). Toward a patent exhaustion regime for sustainable development. Berkeley Journal of International Law, 32(2), 6.
Liu, S. K., & Xu, Z. S. (2014). Stackelberg game models between two competitive retailers in fuzzy decision environment. Fuzzy Optimization and Decision Making, 13(1), 33-48. https://doi.org/10.1007/s10700-013-9165-x
Mohammed, F., Selim, S. Z., Hassan, A., & Syed, M. N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transportation Research Part D: Transport and Environment, 51, 146-172. https://doi.org/10.1016/j.trd.2016.10.033
Nagaoka, S. (2009). Does strong patent protection facilitate international technology transfer? Some evidence from licensing contracts of Japanese firms. The Journal of Technology Transfer, 34(2), 128144. https://doi.org/10.1007/s10961-007-9071-x
Oraiopoulos, N., Ferguson, M. E., & Toktay, L. B. (2012). Relicensing as a secondary market strategy. Management Science, 58(5), 1022-1037. https://doi.org/10.1287/mnsc.1110.1456
Park, S. Y., & Keh, H. T. (2003). Modelling hybrid distribution channels: A game-theoretic analysis. Journal of Retailing and Consumer Services, 10(3), 155-167. https://doi.org/10.1016/S0969-6989(03)00007-9
Ramezani, M., Kimiagari, A. M., Karimi, B., & Hejazi, T. H. (2014). Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems, 59, 108-120. https://doi.org/10.1016/j.knosys.2014.01.016
Savaskan, R. C., Bhattacharya, S., & Van Wassenhove, L. N. (2004). Closed-loop supply chain models with product remanufacturing. Management Science, 50(2), 239-252. https://doi.org/10.1287/mnsc.1030.0186
Shi, J., Zhang, G., & Sha, J. (2011). Optimal production planning for a multi-product closed loop system with uncertain demand and return. Computers & Operations Research, 38(3), 641-650. https://doi.org/10.1016/j.cor.2010.08.008
Shi, J., Zhang, G., Sha, J., & Amin, S. H. (2010). Coordinating production and recycling decisions with stochastic demand and return. Journal of Systems Science and Systems Engineering, 19(4), 385-407. https://doi.org/10.1007/s11518-010-5147-5
Wei, J., Govindan, K., Li, Y., & Zhao, J. (2015). Pricing and collecting decisions in a closed-loop supply chain with symmetric and asymmetric information. Computers & Operations Research, 54, 257-265. https://doi.org/10.1016/j.cor.2013.11.021
Wei, J., & Zhao, J. (2011). Pricing decisions with retail competition in a fuzzy closed-loop supply chain. Expert Systems with Applications, 38(9), 11209-11216. https://doi.org/10.1016/j.eswa.2011.02.168
Wei, J., & Zhao, J. (2013). Reverse channel decisions for a fuzzy closed-loop supply chain. Applied Mathematical Modelling, 37(3), 1502-1513. https://doi.org/10.1016/j.apm.2012.04.003
Xu, Y., Patnayakuni, R., Tao, F., & Wang, H. (2015). Incomplete interval fuzzy preference relations for supplier selection in supply chain management. Technological and Economic Development of Economy, 21(3), 379-404. https://doi.org/10.3846/20294913.2013.876688
Yang, D. Y., & Xiao, T. J. (2017). Pricing and green level decisions of a green supply chain with governmental interventions under fuzzy uncertainties. Journal of Cleaner Production, 149, 1174-1187. https://doi.org/10.1016/j.jclepro.2017.02.138
Yildizbaşi, A., Çalik, A., Paksoy, T., Farahani, R. Z., & Weber, G. W. (2018). Multi-level optimization of an automotive closed-loop supply chain network with interactive fuzzy programming approaches. Technological and Economic Development of Economy, 24(3), 1004-1028. https://doi.org/10.3846/20294913.2016.1253044
Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems (pp. 394-432). Selected Papers by Lotfi A Zadeh. World Scientific. https://doi.org/10.1142/9789814261302_0021
Zarandi, M. H. F., Sisakht, A. H., & Davari, S. (2011). Design of a closed-loop supply chain (CLSC) model using an interactive fuzzy goal programming. The International Journal of Advanced Manufacturing Technology, 56(5-8), 809-821. https://doi.org/10.1007/s00170-011-3212-y
Zhang, C. T., & Ren, M. L. (2016). Closed-loop supply chain coordination strategy for the remanufacture of patented products under competitive demand. Applied Mathematical Modelling, 40(13-14), 6243-6255. https://doi.org/10.1016/j.apm.2016.02.006
Zhang, P., Xiong, Y., Xiong, Z., & Yan, W. (2014). Designing contracts for a closed-loop supply chain under information asymmetry. Operations Research Letters, 42(2), 150-155. https://doi.org/10.1016/j.orl.2014.01.004
Zhao, D., Chen, H. M., Hong, X. P., & Liu, J. F. (2014). Technology licensing contracts with network effects. International Journal of Production Economics, 158, 136-144. https://doi.org/10.1016/j.ijpe.2014.07.023
Zhao, J., Wei, J., & Sun, X. (2016). Coordination of fuzzy closed-loop supply chain with price dependent demand under symmetric and asymmetric information conditions. Annals of Operations Research, 257(1-2), 469-489.
Zimmermann, H. J. (2000). An application-oriented view of modeling uncertainty. European Journal of Operational Research, 122(2), 190-198. https://doi.org/10.1016/S0377-2217(99)00228-3
Arora, A., & Ceccagnoli, M. (2006). Patent protection, complementary assets, and firms’ incentives for technology licensing. Management Science, 52(2), 293-308. https://doi.org/10.1287/mnsc.1050.0437
Bai, C., & Sarkis, J. (2018). Evaluating complex decision and predictive environments: the case of green supply chain flexibility. Technological and Economic Development of Economy, 24(4), 1630-1658. https://doi.org/10.3846/20294913.2018.1483977
Brousseau, E., Coeurderoy, R., & Chaserant, C. (2007). The governance of contracts: Empirical evidence on technology licensing agreements. Journal of Institutional and Theoretical Economics, 163(2), 205235. https://doi.org/10.1628/093245607781261379
Cui, Y. Y., Guan, Z., Saif, U., Zhang, L., Zhang, F., & Mirza, J. (2017). Close loop supply chain network problem with uncertainty in demand and returned products: Genetic artificial bee colony algorithm approach. Journal of Cleaner Production, 162, 717-742. https://doi.org/10.1016/j.jclepro.2017.06.079
Giri, B. C., & Sharma, S. (2016). Optimal production policy for a closed-loop hybrid system with uncertain demand and return under supply disruption. Journal of Cleaner Production, 112(Part 3), 2015-2028. https://doi.org/10.1016/j.jclepro.2015.06.147
Hamdouch, Y., Qiang, Q. P., & Ghoudi, K. (2017). A closed-loop supply chain equilibrium model with random and price-sensitive demand and return. Networks and Spatial Economics, 17(2), 459-503. https://doi.org/10.1007/s11067-016-9333-y
Hashiguchi, M. S. (2008). Recycling efforts and patent rights protection in the United States and Japan. Columbia Journal of Environmental Law, 33, 169-195.
Hong, X. P., Govindan, K., Xu, L., & Du, P. (2017). Quantity and collection decisions in a closed-loop supply chain with technology licensing. European Journal of Operational Research, 256(3), 820-829. https://doi.org/10.1016/j.ejor.2016.06.051
Keshavarz Ghorabaee, M., Amiri, M., Olfat, L., & Khatami Firouzabadi, S. A. (2017). Designing a multiproduct multi-period supply chain network with reverse logistics and multiple objectives under uncertainty. Technological and Economic Development of Economy, 23(3), 520-548. https://doi.org/10.3846/20294913.2017.1312630
Khaksar, E., Abbasnejad, T., Esmaeili, A., & Tamošaitienė, J. (2016). The effect of green supply chain management practices on environmental performance and competitive advantage: A case study of the cement industry. Technological and Economic Development of Economy, 22(2), 293-308. https://doi.org/10.3846/20294913.2015.1065521
Khatami, M., Mahootchi, M., & Farahani, R. Z. (2015). Benders’ decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties. Transportation Research Part E: Logistics and Transportation Review, 79, 1-21. https://doi.org/10.1016/j.tre.2015.03.003
Kim, J., Do Chung, B., Kang, Y., & Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of Cleaner Production, 196, 1314-1328. https://doi.org/10.1016/j.jclepro.2018.06.157
Lin, L. H., & Kulatilaka, N. (2006). Network effects and technology licensing with fixed fee, royalty, and hybrid contracts. Journal of Management Information Systems, 23(2), 91-118. https://doi.org/10.2753/MIS0742-1222230205
Liu, B. D. (2009). Theory and practice of uncertain programming (STUDFUZZ, Vol. 239). Berlin: Springer. https://doi.org/10.1007/978-3-540-89484-1
Liu, B. P. W. (2014). Toward a patent exhaustion regime for sustainable development. Berkeley Journal of International Law, 32(2), 6.
Liu, S. K., & Xu, Z. S. (2014). Stackelberg game models between two competitive retailers in fuzzy decision environment. Fuzzy Optimization and Decision Making, 13(1), 33-48. https://doi.org/10.1007/s10700-013-9165-x
Mohammed, F., Selim, S. Z., Hassan, A., & Syed, M. N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transportation Research Part D: Transport and Environment, 51, 146-172. https://doi.org/10.1016/j.trd.2016.10.033
Nagaoka, S. (2009). Does strong patent protection facilitate international technology transfer? Some evidence from licensing contracts of Japanese firms. The Journal of Technology Transfer, 34(2), 128144. https://doi.org/10.1007/s10961-007-9071-x
Oraiopoulos, N., Ferguson, M. E., & Toktay, L. B. (2012). Relicensing as a secondary market strategy. Management Science, 58(5), 1022-1037. https://doi.org/10.1287/mnsc.1110.1456
Park, S. Y., & Keh, H. T. (2003). Modelling hybrid distribution channels: A game-theoretic analysis. Journal of Retailing and Consumer Services, 10(3), 155-167. https://doi.org/10.1016/S0969-6989(03)00007-9
Ramezani, M., Kimiagari, A. M., Karimi, B., & Hejazi, T. H. (2014). Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems, 59, 108-120. https://doi.org/10.1016/j.knosys.2014.01.016
Savaskan, R. C., Bhattacharya, S., & Van Wassenhove, L. N. (2004). Closed-loop supply chain models with product remanufacturing. Management Science, 50(2), 239-252. https://doi.org/10.1287/mnsc.1030.0186
Shi, J., Zhang, G., & Sha, J. (2011). Optimal production planning for a multi-product closed loop system with uncertain demand and return. Computers & Operations Research, 38(3), 641-650. https://doi.org/10.1016/j.cor.2010.08.008
Shi, J., Zhang, G., Sha, J., & Amin, S. H. (2010). Coordinating production and recycling decisions with stochastic demand and return. Journal of Systems Science and Systems Engineering, 19(4), 385-407. https://doi.org/10.1007/s11518-010-5147-5
Wei, J., Govindan, K., Li, Y., & Zhao, J. (2015). Pricing and collecting decisions in a closed-loop supply chain with symmetric and asymmetric information. Computers & Operations Research, 54, 257-265. https://doi.org/10.1016/j.cor.2013.11.021
Wei, J., & Zhao, J. (2011). Pricing decisions with retail competition in a fuzzy closed-loop supply chain. Expert Systems with Applications, 38(9), 11209-11216. https://doi.org/10.1016/j.eswa.2011.02.168
Wei, J., & Zhao, J. (2013). Reverse channel decisions for a fuzzy closed-loop supply chain. Applied Mathematical Modelling, 37(3), 1502-1513. https://doi.org/10.1016/j.apm.2012.04.003
Xu, Y., Patnayakuni, R., Tao, F., & Wang, H. (2015). Incomplete interval fuzzy preference relations for supplier selection in supply chain management. Technological and Economic Development of Economy, 21(3), 379-404. https://doi.org/10.3846/20294913.2013.876688
Yang, D. Y., & Xiao, T. J. (2017). Pricing and green level decisions of a green supply chain with governmental interventions under fuzzy uncertainties. Journal of Cleaner Production, 149, 1174-1187. https://doi.org/10.1016/j.jclepro.2017.02.138
Yildizbaşi, A., Çalik, A., Paksoy, T., Farahani, R. Z., & Weber, G. W. (2018). Multi-level optimization of an automotive closed-loop supply chain network with interactive fuzzy programming approaches. Technological and Economic Development of Economy, 24(3), 1004-1028. https://doi.org/10.3846/20294913.2016.1253044
Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems (pp. 394-432). Selected Papers by Lotfi A Zadeh. World Scientific. https://doi.org/10.1142/9789814261302_0021
Zarandi, M. H. F., Sisakht, A. H., & Davari, S. (2011). Design of a closed-loop supply chain (CLSC) model using an interactive fuzzy goal programming. The International Journal of Advanced Manufacturing Technology, 56(5-8), 809-821. https://doi.org/10.1007/s00170-011-3212-y
Zhang, C. T., & Ren, M. L. (2016). Closed-loop supply chain coordination strategy for the remanufacture of patented products under competitive demand. Applied Mathematical Modelling, 40(13-14), 6243-6255. https://doi.org/10.1016/j.apm.2016.02.006
Zhang, P., Xiong, Y., Xiong, Z., & Yan, W. (2014). Designing contracts for a closed-loop supply chain under information asymmetry. Operations Research Letters, 42(2), 150-155. https://doi.org/10.1016/j.orl.2014.01.004
Zhao, D., Chen, H. M., Hong, X. P., & Liu, J. F. (2014). Technology licensing contracts with network effects. International Journal of Production Economics, 158, 136-144. https://doi.org/10.1016/j.ijpe.2014.07.023
Zhao, J., Wei, J., & Sun, X. (2016). Coordination of fuzzy closed-loop supply chain with price dependent demand under symmetric and asymmetric information conditions. Annals of Operations Research, 257(1-2), 469-489.
Zimmermann, H. J. (2000). An application-oriented view of modeling uncertainty. European Journal of Operational Research, 122(2), 190-198. https://doi.org/10.1016/S0377-2217(99)00228-3