Streamlining Inventory Control: A Strategic Approach to Managing Slow-Moving Items; Systematic Review Approach

Abstract

Across all sectors, efficiently managing slow-moving inventory is still a major difficulty that results in higher holding costs, waste, and inefficiencies in supply chain operations. Even with improvements in inventory management, many companies still have trouble maintaining demand-driven procurement while maintaining stock levels. Modern data-driven strategies are frequently not completely incorporated into existing inventory models, which limits their ability to reduce excess stock and boost operational performance. There is an urgent need to investigate how new technologies might improve the management of slow-moving inventory given the quick developments in machine learning, IoT-enabled tracking, and predictive analytics. To cut expenses, decrease losses, and simplify inventory procedures, businesses need technology-driven, optimized tactics. To determine the best inventory optimization models, analyze market trends, and provide an integrated framework for improved efficiency in managing slow-moving inventory, this study offers a thorough analysis of previous research. The goal of this study is to review and summarize the most recent research on slow-moving inventory management, with an emphasis on optimization strategies, predictive modelling, and technology advancements. The objective is to offer a methodical analysis of strategies like multi-echelon optimization, AI-based forecasting, simulation models, and EOQ models to evaluate their efficacy across various sectors. 350 research publications from academic databases such as Scopus and Emerald Insight were analyzed as part of a comprehensive literature review. 100 papers (28.5%) were included in the final evaluation after 129 papers (369.9%) were screened and 29 papers (8.3%) were eliminated for lack of relevance, duplication, or methodological issues. The study groups its findings according to efficiency results, industrial applications, and inventory management strategies. The assessment points to a discernible trend toward data-driven inventory optimization strategies, IoT-based tracking, and AI-powered prediction models. With 44% of the assessed articles, the 2023–2025 era had the most research contributions. 2020–2022 (29%), 2015–2016 (20%), and 2017–2019 (7%), came next. Results show that slow-moving inventory efficiency is greatly increased by combining machine learning, fuzzy logic, and real-time monitoring, which lowers operating expenses and increases stock turnover. The study emphasizes how crucial it is to integrate technology into inventory management and suggests using hybrid optimization models, real-time tracking, and AI-driven analytics. Future studies should concentrate on creating flexible inventory frameworks that use automation, blockchain, and big data to boost supply chain sustainability and decision-making.