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From predictive to prescriptive analytics: A data-driven multi-item newsvendor model
Decision Support Systems ( IF 7.5 ) Pub Date : 2020-06-10 , DOI: 10.1016/j.dss.2020.113340
Sushil Punia , Surya Prakash Singh , Jitendra K. Madaan

This paper considers a multi-item newsvendor problem with a capacity constraint (Z). The problem has already been addressed in the literature using the classical newsvendor problem. However, provided solutions made assumptions for demand distributions, which are often incorrect and led to errors in the inventory optimization. This research proposes a distribution-free and completely data-driven solution approach to Z. The proposed approach uses sample demand data as input, and machine (and deep) learning methods with empirical risk minimization principle to find order quantities. A heuristic is developed using hierarchies of the retail products to perform multi-item inventory optimization when a capacity constraint is active. The proposed approach is tested on a real-world dataset of retail products. The results from the proposed method are compared with data-driven max-min and empirical inventory optimization methods, and it outperformed all of them. The machine (and deep) learning-based demand forecasting methods (part of the proposed approach) providing better results than neural networks, multiple regression, arima, arimax, etc. Finally, a comparison of total inventory cost from the proposed, max-min, and empirical inventory optimization methods are carried out, and it is observed that the proposed data-driven approach leads to a significant reduction in inventory cost.



中文翻译:

从预测性分析到规范性分析:数据驱动的多项目新闻供应商模型

本文考虑了一个具有容量约束(Z)的多项目新闻供应商问题。该问题已在文献中使用经典新闻卖主问题解决。但是,提供的解决方案对需求分配进行了假设,这些假设通常是不正确的,并且会导致库存优化中的错误。这项研究提出了一种无分布且完全由数据驱动的Z解决方案方法。该方法使用样本需求数据作为输入,并采用经验最小化原理的机器(和深度)学习方法来查找订单数量。当容量约束处于活动状态时,使用零售产品的层次结构开发启发式算法,以执行多项目库存优化。所提出的方法在零售产品的真实数据集上进行了测试。将该方法的结果与数据驱动的最大-最小和经验库存优化方法进行了比较,其性能均优于所有方法。基于机器(和深度)学习的需求预测方法(所提议方法的一部分)比神经网络,多元回归,arima,arimax等提供了更好的结果。最后,比较了所提议的总库存成本max-min ,并进行了经验性的库存优化方法,并且观察到,所提出的数据驱动方法可显着降低库存成本。

更新日期:2020-07-29
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