A game theoretic approach for green supply chain management in a big data environment considering cost-sharing models

Today's competitive world has forced all businesses and supply chains to use all their capacity to create a competitive advantage. Thanks to the advancement of technology, millions of data are generated daily during business processes. Planning to extract the knowledge available in this big data can be the basis for the progress of companies. Considering the increasing importance and popularity of big data, this research examines the impact of big data investment on the equilibrium variables of decision and equilibrium profit of members in a three-level supply chain including supplier, manufacturer, and retailer under different power structures. In this research, all members of the chain invest in big data analysis and receive benefits according to this investment. The demand function of the problem is reviewed according to the impact of big data investment on its components. To better examine the impact of members' decisions, game theory tools are used. Finally, the use of the cost-sharing contract is evaluated. In general, the results show that the Nash scenario in this supply chain benefits all chain members and customers. In the next degree, the retailer's leadership scenario can be effective in satisfying customers. The use of the cost-sharing contract also improves the conditions only in the supplier leadership scenario and in other scenarios this contract will not have the necessary effectiveness.