E-Closed-Loop Supply Chain Decision-Making Considering Government Regulation and Second-Hand Markets

doi:10.12068/j.issn.1005-3026.2024.09.018

Abstract

Abstract:

The E-closed?loop supply chain decision?making consisting of a manufacturer and an e?commerce platform under government regulation are considered when the e?commerce platform opens the second?hand market or not， respectively， and the impacts of government disposal fund， subsidy rate， quality of used products， and commission rate of the platform on the manufacturer’s and e?commerce platform’s decision?making and expected profits are analyzed. It is found that when the second?hand market is open， the manufacturer’s optimal selling price for new products and optimal recycling price for used products are lower， and the e?commerce platform tries harder to sell new products. When the second?hand market is not open， the manufacturer’s expected profit is higher and the e?commerce platform’s expected profit is lower. Under government regulation， an increase in the disposal fund provides an incentive to the manufacturer to recycle used products and adversely affects the manufacturer’s production of new products， whereas an increase in the subsidy rate promotes the recycling of used products and does not affect the production of new products.

Key words: second?hand market, closed?loop supply chain, quality of used products, government regulation, e?commerce platform

CLC Number:

F 224

Chun-yu BAI, Zhi-min GUAN, Ying ZHAO, Jing-yang DONG. E-Closed-Loop Supply Chain Decision-Making Considering Government Regulation and Second-Hand Markets[J]. Journal of Northeastern University(Natural Science), 2024, 45(9): 1360-1368.

Figures/Tables 9

Fig.1 E-closed?loop supply chain structure

Fig.2 Impact of commission rate on optimal prices

Fig.3 Impact of commission rate on optimal demand and expected recovery

Fig.4 Impact of commission rate and processing fund on the manufacturer’s optimal expected profit

Fig.5 Impact of commission rate and processing fund on the optimal expected profit of e?commerce platforms

Fig.6 Impact of quality benchmarks on the optimal expected recovery price and transfer price

Fig.7 Impact of quality benchmarks on the optimal expected recovery volume

Fig.8 Impact of value discount coefficient and quality benchmarks on the manufacturer’s optimal expected profits

Fig.9 Impact of value discount coefficient and quality benchmarks on the optimal expected profit of e?commerce platforms

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