Methods to generate realistic non-stationary demand scenarios are a key component for analyzing and optimizing decision policies in supply chains. Typical forecasting techniques recommended in standard inventory control textbooks consist of some form of exponential smoothing for both the estimates for the mean and standard deviation. We propose and study a class of demand generating processes (DGPs) that yield non-stationary demand scenarios, and that are consistent with SES, meaning that SES yields unbiased estimates when applied to the generated demand scenarios. As demand in typical practical settings is discrete and non-negative, we study consistent DGPs on the non-negative integers, and derive conditions under which the existence of such DGPs can be guaranteed. Our subsequent simulation study gains further insights into the proposed DGP. It demonstrates that from a given initial forecast, our DGPs yields a diverse set of demand scenarios with a wide range of properties. To show the applicability of the DGP, we apply it to generate demand in a standard inventory problem with full backlogging and a positive lead time. We find that appropriate dynamic base-stock levels can be obtained using a new and relatively simple algorithm, and we demonstrate that this algorithm outperforms relevant benchmarks.

翻译：生成逼真的非平稳需求场景是分析和优化供应链决策策略的关键组成部分。标准库存控制教材推荐的典型预测技术通常包括对均值和标准差估计值进行某种形式的指数平滑。我们提出并研究了一类能够生成非平稳需求场景的需求生成过程（DGPs），这些过程与简单指数平滑（SES）相一致，即SES在应用于生成的需求场景时能产生无偏估计。由于实际场景中的需求通常是离散且非负的，我们在非负整数集上研究一致的需求生成过程，并推导出确保此类过程存在的条件。随后的仿真研究进一步揭示了所提出需求生成过程的特性。研究表明，从给定的初始预测出发，我们的需求生成过程能生成具有广泛属性的多样化需求场景。为展示该过程的适用性，我们将其应用于一个允许完全缺货且具有正提前期的标准库存问题。我们发现，采用一种新颖且相对简单的算法可以获得合适的动态基础库存水平，并证明该算法优于相关基准方法。