Information sharing in a supply chain under ARMA demand

In this paper we study how the time-series structure of the demand process affects the value of information sharing in a supply chain. We consider a two-stage supply chain model in which a retailer serves autoregressive moving-average (ARMA) demand and a manufacturer fills the retailer's orders. We characterize three types of situations based on the parameters of the demand process: (i) the manufacturer benefits from inferring demand information from the retailer's orders; (ii) the manufacturer cannot infer demand, but benefits from sharing demand information; and (iii) the manufacturer is better off neither inferring nor sharing, but instead uses only the most recent orders in its production planning. Using the example of ARMA(1,1) demand, we find that sharing or inferring retail demand leads to a 16.0% average reduction in the manufacturer's safety-stock requirement in cases (i) and (ii), but leads to an increase in the manufacturer's safety-stock requirement in (iii). Our results apply not only to two-stage but also to multistage supply chains.