The Value of Hybrid MTS/MTO Supply Chain Sharing Demand Forecasts under Big Data

Abstract

Big data technology provides convenience for all entities in the supply chain to obtain demand forecasts and share the information. This article considers a supply chain composed of a manufacturer and two competitive retailers and analyzes the value of sharing demand information in the supply chain. In this supply chain, the manufacturer has a hybrid MTS/MTO production system and sells products to the MTS retailer and the MTO retailer. Both the manufacturer and the retailers have private demand information. We established a no-information sharing model, a full information-sharing model, and two partial-information sharing models, to study the value of sharing information. The results show that the full information sharing strategy cannot benefit all entities. However, if the demand forecasts of the two retailers are very different and lower than the manufacturer’s forecast, sharing information between the manufacturer and the retailer who has high demand prediction can benefit all entities.