Should firms invest in demand forecasting? Benefits of improving forecasting accuracy on order smoothing, dual-sourcing and multi-stage supply chain problems
研究了从恒定需求预测转向最小均方误差需求预测对库存管理的影响，发现仅在少数AR(1)需求过程中有显著改进，且双源采购和订单平滑协作可降低对高预测精度的需求。
We investigate how improvements in demand forecasting–defined as transitioning from constant demand forecasting to minimal-mean-squared-error demand forecasting–affect managing inventory using the proportional order-up-to replenishment policy. While the existing literature extensively explores the impacts of demand forecasting under the normal order-up-to policy, its impacts under the proportional order-up-to policy remain largely unexplored. Our analysis covers order-smoothing, dual-sourcing, and multi-stage models. We emphasise the impact of auto-correlated demand processes, which are common in practice, on the desirability of minimal-mean-squared-error forecasting. Our results reveal that minimal-mean-squared-error demand forecasting only yields significant performance improvements for a small subset of AR(1) demand processes and can even lead to deteriorated performance in certain cases. Additionally, strategies such as dual-sourcing and collaboration through order smoothing reduce the need for high forecasting accuracy.