Decision support framework for inventory management combining fuzzy multicriteria methods, genetic algorithm, and artificial neural network

Abstract

Decision support tools, within the Industry 4.0 perspective, have increasingly impacted different operations and supply chain management (OSCM) areas, such as inventory management. Within the digital transformation era, multicriteria decision-making (MCDM) and machine learning (ML) can be used to improve inventory management decisions. Despite their importance, the literature lacks empirical studies involving advanced solutions that combine both approaches to support practitioners in real-life settings. This is especially relevant for maintenance, repair, and operation (MRO) inventories, which usually present several SKUs with irregular demand patterns and difficult forecasting. Therefore, this study proposes a decision support framework for inventory management, combining MCDM and ML approaches, and applies it to a railway logistics operator to assist its MRO inventory management decision-making process. The first stage of the framework consists of applying a hybrid MCDM method that combines fuzzy logic with the analytic hierarchy process (AHP) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods to rank and select SKUs according to importance and criticality. Once the most critical SKUs are revealed, a second stage of the framework is introduced to forecast the demand for these SKUs through an ML model, which combines a genetic algorithm and an artificial neural network (GA-ANN). Research findings point to a considerable improvement in the accuracy of the demand forecast for SKUs compared to the previous forecast by the company, and the forecasting methods support vector machine (SVM) and exponential smoothing. The results of the combined approaches are consolidated into a management dashboard, which improves the agility and quality of the analyzed company's decision-making process in inventory management. Therefore, practitioners can also take stock of the proposed framework as a semi-automatic management artefact to enhance decision-making from a digital transformation view in a vital area of OSCM.