Impact of customers reaction to autocorrelated price sensitive demand on supply chains


We investigate the dynamics of a supply chain with a price-sensitive, autocorrelated, stochastic, linear demand model. We assume the exogenous market price follows a first order auto-regressive AR(1) process. The demand process is a weighted function (w) of the current and previous market price, the market potential (a), and the positive demand sensitivity coefficient (b). We assume that a supplier faces five different types of customers in the market: responsive, selective, naïve, speculative, and slow customers. A weighting factor w determines how each of the customers react to period-to-period price changes. The supply chain profit, as a function of the market size, variable costs, inventory costs and capacity costs was investigated. We found that supply chain profit was maximized with i.i.d. demand and slow customers, concurring with the maximum market size and minimum inventory and order variances. We also revealed a traditional linear price-sensitive demand model with an AR(1) price process is identical to a first-order auto-regressive moving average, ARMA(1,1), demand model.

53rd Annual Conference of the Decision Sciences Institute, November 19th-21st, Houston, USA