针对分支机构多、每种产品销售额低、非经常性订单的公司进行需求预测
Demand forecasting for companies with many branches, low sales numbers per product, and non-recurring orderings
摘要：
我们提出了新的Top-Dog-Index来量化一个商品组的许多小分支机构的供应数据与销售数据的历史偏差。一方面，文献中关于客户需求分布的常见参数假设在我们的真实世界数据集中根本得不到支持。另一方面，直接从销售分布估计不同分支机构的需求分布的合理的非参数方法只能为我们提供统计上弱且不可靠的未来需求估计。基于我们行业合作伙伴的真实销售数据，我们提供证据证明我们的Top-Dog-Index在统计上是稳健的。使用Top-Dog-Index，我们提出了一种改进供应和需求之间的分支依赖比例的方法。我们的方法不能直接估计分支依赖的需求。然而，它可以根据历史上的供应过剩或供应不足，将分支机构分为给定数量的集群。分支的这种分类可以迭代地用于适应未来的供应和需求的分支分布。
英文摘要：
We propose the new Top-Dog-Index to quantify the historic deviation of the supply data of many small branches for a commodity group from sales data. On the one hand, the common parametric assumptions on the customer demand distribution in the literature could not at all be supported in our real-world data set. On the other hand, a reasonably-looking non-parametric approach to estimate the demand distribution for the different branches directly from the sales distribution could only provide us with statistically weak and unreliable estimates for the future demand. Based on real-world sales data from our industry partner we provide evidence that our Top-Dog-Index is statistically robust. Using the Top-Dog-Index, we propose a heuristics to improve the branch-dependent proportion between supply and demand. Our approach cannot estimate the branch-dependent demand directly. It can, however, classify the branches into a given number of clusters according to an historic oversupply or undersupply. This classification of branches can iteratively be used to adapt the branch distribution of supply and demand in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
