Abstract

In view of the reality that manufacturers invest in green technologies to produce green products and sell them directly to consumers through the Internet and traditional channels, three dual-channel supply chain game models are constructed under different power structures: manufacturer-led Stackelberg game, retailer-led Stackelberg game and equal Nash game. The optimal strategy and maximum profit of manufacturers and retailers under three power structures are compared, and the sensitivity of key factors is analyzed. Comparative analysis is carried out by numerical examples. The results show that the optimal strategy and performance of each power subject under the three games are affected by the green degree of product, retailer's strategic inventory and other key factors. In order to achieve higher profit margins, retailers have always tended to hold on to strategic inventory. Retailers’ decisions improve manufacturers’ profits and the greening of their products; In addition, the greener the product, the smaller the retailer’s strategic inventory, and the higher the profit of the manufacturer and retailer. Therefore, when retailers hold strategic inventory, manufacturers can choose to increase the green investment level of products to enhance their voice and negotiate with retailers. Retailers can also play games with manufacturers by holding strategic inventory to achieve pareto efficiency. Manufacturers and retailers benefit.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.