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Control of inventory dynamics: A survey of special cases for products with low demand
Annual Reviews in Control ( IF 7.3 ) Pub Date : 2020-05-26 , DOI: 10.1016/j.arcontrol.2020.04.005
Valery Lukinskiy , Vladislav Lukinskiy , Boris Sokolov

Around 30% to 70% of products in retail and services experience low demand, including spare parts and components for nearly all types of machinery and equipment industries. A detailed analysis of stock forecasting methods for the low demand represents a research gap in inventory management. The existing clustering methods, that is, ABC analysis and XYZ analysis (based on coefficient of variation), do not allow identification of the consumption process dynamics and, therefore, cannot be used for the classification and improvement of forecasting models for stock consumption. This paper surveys special cases of inventory management with low demand. The results of one- and two-dimensional stock classifications are presented. The limitations of the economic order quantity (EOQ) model for inventory management strategies are determined. Methods of inventory parameter calculations for products with low demand are suggested. Integrated time series forecasting models, along with algorithms to estimate the inventory forecasting parameters, are proposed with regard to products with low demand. The basis for the suggested models is the following concept: all the available sources of quantitative and qualitative information should be used for managerial decision-making under uncertainty and risk. Calculations for time series with low demand are conducted for testing purposes. The obtained results confirm the adequateness of the suggested concept, aimed at solving the problem of cost reduction in supply chains.



中文翻译:

控制库存动态:对低需求产品特殊情况的调查

零售和服务业大约30%至70%的产品需求低,包括几乎所有类型的机械和设备行业的备件和组件。针对低需求的库存预测方法的详细分析代表了库存管理方面的研究空白。现有的聚类方法,即ABC分析和XYZ分析(基于变异系数),无法识别消耗过程动态,因此无法用于分类和改进库存消耗的预测模型。本文调查了需求低的库存管理的特殊情况。给出了一维和二维股票分类的结果。确定了库存管理策略的经济订单数量(EOQ)模型的局限性。建议低需求产品的库存参数计算方法。针对需求量低的产品,提出了集成的时间序列预测模型以及估计库存预测参数的算法。所建议模型的基础是以下概念:在不确定性和风险下,应使用所有可用的定量和定性信息资源进行管理决策。出于测试目的,对低需求的时间序列进行了计算。获得的结果证实了所提出的概念的适当性,旨在解决供应链中的成本降低问题。针对需求量低的产品,提出了估计库存预测参数的算法。建议模型的基础是以下概念:在不确定性和风险下,应使用所有可用的定量和定性信息资源进行管理决策。出于测试目的,对低需求的时间序列进行了计算。获得的结果证实了所提出的概念的适当性,旨在解决供应链中的成本降低问题。针对需求量低的产品,提出了估计库存预测参数的算法。所建议模型的基础是以下概念:在不确定性和风险下,应使用所有可用的定量和定性信息资源进行管理决策。出于测试目的,对低需求的时间序列进行了计算。获得的结果证实了所提出的概念的适当性,旨在解决供应链中的成本降低问题。出于测试目的,对低需求的时间序列进行了计算。获得的结果证实了所提出的概念的适当性,旨在解决供应链中的成本降低问题。出于测试目的,对低需求的时间序列进行了计算。获得的结果证实了所提出的概念的适当性,旨在解决供应链中的成本降低问题。

更新日期:2020-05-26
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