Control of inventory dynamics: A survey of special cases for products with low demand. (2020)

• Record Type: Journal Article
• Authors: Lukinskiy, Valery; Lukinskiy, Vladislav; Sokolov, Boris
• Is Part Of: Annual reviews in control. Volume 49(2020)
• Page Start: 306
• Page End: 320
• Publication Date: 2020
• Subjects: Supply chains -- Inventory management -- Low demand -- Forecasting -- Poisson distribution; Automatic control -- Periodicals; Periodicals; 629.805
• DOI: 10.1016/j.arcontrol.2020.04.005
• Languages: English
• ISSNs: 1367-5788

Abstract

Abstract: 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.