In this paper, we revisit the problem of demand propagation in a multi-stage supply chainin which the retailer observes ARMA demand. In contrast to previous work, we show how eachplayer constructs the order based upon its best linear forecast of leadtime demand given itsavailable information. In...

We introduce a class of new sharing arrangements in a multi-stage supplychain in which the retailer observes stationary autoregressive movingaverage demand with Gaussian white noise (shocks). Similar to previousresearch, we assume each supply chain player constructs its best linearforecast of...

We consider the problem of assessing value of demand sharing in a multi-stage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best...

We consider the problem of assessing value of demand sharing in a multi-stage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best...

It is common for firms to forecast stationary demand using simple exponential smoothing due to the ease of computation and understanding of the methodology. In this paper we show that the use of this methodology can be extremely costly in the context of inventory in a two-stage supply chain when...

We study a two-stage supply chain where the retailer observes two demand streams coming from two consumer populations. We further assume that each demand sequence is a station- ary Autoregressive Moving Average (ARMA) process with respect to a Gaussian white noise sequence (shocks). The shock...