This research studies the impact of improved forecasts on the members of a two-stage supply chain. The supplier builds capacity based on original forecast information, and the manufacturer places its order after observing improved (but imperfect) demand information. We study three types of wholesale price purchasing arrangements: (1) when the wholesale price is determined to be exogenous to the supply chain; (2) when the supplier sets the wholesale price; and (3) when the manufacturer sets the wholesale price. Although improved demand information reduces the demand uncertainty that the manufacturer faces, the manufacturer is constrained by the supplier's capacity decision. In all three cases, we show that improved information can decrease the supply chain's expected profit, even as the supplier's capacity increases with improved information. Because improved demand information always increases the centralized supply chain's expected profit, we present a contract that coordinates the channel and provides flexibility in dividing systemwide profit.inventory, forecasting, forecast revisions, decentralized supply chains

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Managers often engage in forecast updating with the expectation that forecast updating reduces expected shortage and inventory costs. One undesirable effect of forecast updating is that it may lead to the bullwhip effect, a phenomenon where the variability of demand increases as one moves up the supply chain. The bullwhip effect can be undesirable for the supplier because more volatile orders from the downstream stage can be very costly to the supplier. It can make it more difficult for the supplier to forecast demand, and it can lead to large fluctuations in supplier production levels from period to period. Using "stale" or old forecasts may sound foolish, but their judicious use in a two-stage supply chain can improve fulfillment from the upstream stage to the downstream stage and reduce the fluctuations in production levels. We study a two-stage supply chain where the demand process is nonstationary and both stages use an adaptive base stock policy. We propose a policy that uses old forecasts to set base stock levels at the downstream stage while using current forecasts to communicate upcoming orders from the downstream stage to the upstream stage. We study a decentralized supply chain setting, and we find that our policy can reduce the expected supply chain inventory and shortage costs and significantly reduce the fluctuations in production levels compared to that of using current information. We also study a cooperative supply chain setting and, surprisingly, we find in numerical examples that our proposed policy results in very small increases in the expected systemwide inventory and shortage costs compared to a systemwide optimal policy, while reducing the fluctuations in production levels.inventory control, forecasting, forecast revision, bullwhip effect, production smoothing

In this paper, we consider a periodic review inventory problem where demand in each period is modeled by linear regression. We use a Bayesian formulation to update the regression parameters as new information becomes available. We find that a state-dependent base-stock policy is optimal and we give structural results. One interesting finding is that our structural results are not analogous to classical results in Bayesian inventory research. This departure from classical results is due to the role that the independent variables play in the Bayesian regression formulation. Because of the computational complexity of the optimal policy, we propose a combination of two heuristics that simplifies the Bayesian inventory problem. Through analytical and numerical evaluation, we find that the heuristics provide near-optimal results.Bayesian regression, inventory production, stochastic models, approximations heuristics