A framework for performance measurement in the e-business environment
Electronic Commerce Research and Applications
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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.