Two-way information sharing of uncertain demand forecasts in a dual-channel supply chain

This paper considers a dual-channel supply chain composed of a manufacturer and a retailer in which each member obtains heterogeneous forecasting information about uncertain demand through big data technology investment. We study the decision-making problem of supply chain members in four information sharing sce-narios including non-information sharing, forward information sharing, reverse information sharing and full information sharing respectively. Our model identifies the effects generated by forecasting information on wholesale price decision and product quantity decisions, which we term the strategy and the agility effect. Furthermore, we examine the influence of big data technology investment on supply chain members' expected profits and information strategies. The results show that two-way information sharing benefits the manufacturer but hurts the retailer when the competition between two channels is less intense. However, when there is a fierce competition between two channels, the manufacturer is instead reluctant to communicate information with the retailer if the retailer invests at a low level in big data technology but the manufacturer's big data technology investment level is high. Finally, we find that in the dual-channel supply chain, the equilibrium information strategy only occurs in the non-information sharing case and forward information sharing case, so the infor-mation symmetry cannot be achieved because the retailer has no incentive to reveal the information. Moreover, the numerical study is conducted to illustrate the information sharing mechanism under different channel competition conditions.