Performance improvement in inventory classification using the expectation-maximisation algorithm

Kathirvel Selvaraju and Punniyamoorthy Murugesan

International Journal of Enterprise Network Management, 2024, vol. 15, issue 4, 349-376

Abstract: Multi-criteria inventory classification (MCIC) is popularly used to aid managers in categorising the inventory. Researchers have used numerous mathematical models and approaches, but few resorted to unsupervised machine-learning techniques to address MCIC. This study uses the expectation-maximisation (EM) algorithm to estimate the parameters of the Gaussian mixture model (GMM), a popular unsupervised machine learning algorithm, for ABC inventory classification. The EM-GMM algorithm is sensitive to initialisation, which in turn affects the results. To address this issue, two different initialisation procedures have been proposed for the EM-GMM algorithm. Inventory classification outcomes from 14 existing MCIC models have been given as inputs to study the significance of the two proposed initialisation procedures of the EM-GMM algorithm. The effectiveness of these initialisation procedures corresponding to various inputs has been analysed toward inventory management performance measures, i.e., fill rate, total relevant cost, and inventory turnover ratio.

Keywords: expectation-maximisation algorithm; Gaussian mixture model; GMM; multi-criteria inventory classification; MCIC; ABC classification; fill rate; total relevant cost; TRC; inventory turnover ratio; ITR. (search for similar items in EconPapers)
Date: 2024
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