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Advanced Inventory Management: A Systematic Review of Control Techniques, Warehouse Design and Digital Transformation in Modern Supply Chains


Authors : Adebowale A. Adedokun

Volume/Issue : Volume 11 - 2026, Issue 8 - August


Google Scholar : https://tinyurl.com/3baarrws

Scribd : https://tinyurl.com/mtfz3cx7

DOI : https://doi.org/10.38124/ijisrt/26aug468

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : Inventory management remains a cornerstone of operational excellence in contemporary supply chain ecosystems. This paper presents a comprehensive systematic review of advanced inventory management frameworks, synthesizing classical control techniques with emerging digital technologies. Drawing on an extensive corpus of peer-reviewed literature spanning operational research, warehouse engineering, and information systems, the study examines the strategic role of inventory control, the optimization of warehouse design and layout, the integration of Industry 4.0 technologies, and the application of lean principles and work study methodologies in store operations. The analysis reveals that while traditional models such as Economic Order Quantity (EOQ), Just-in-Time (JIT), and ABC analysis continue to provide foundational utility, their efficacy is substantially amplified when integrated with Internet of Things (IoT) architectures, artificial intelligence-driven predictive analytics, and digital twin simulations. Furthermore, the study identifies a critical research gap concerning the adaptation of these advanced systems within developing economy contexts, particularly in Sub-Saharan Africa, where infrastructural constraints and institutional voids necessitate context-specific hybrid models. The paper contributes to the literature by proposing an integrated conceptual framework that aligns classical inventory theory with digital transformation imperatives, offering actionable implications for scholars and practitioners engaged in procurement and supply chain management.

Keywords : Inventory Management, Supply Chain Resilience, Warehouse Optimization, Lean Inventory, Operations Research, Digital Twins, Industry 4.0, Cycle Counting, Developing Economies.

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Inventory management remains a cornerstone of operational excellence in contemporary supply chain ecosystems. This paper presents a comprehensive systematic review of advanced inventory management frameworks, synthesizing classical control techniques with emerging digital technologies. Drawing on an extensive corpus of peer-reviewed literature spanning operational research, warehouse engineering, and information systems, the study examines the strategic role of inventory control, the optimization of warehouse design and layout, the integration of Industry 4.0 technologies, and the application of lean principles and work study methodologies in store operations. The analysis reveals that while traditional models such as Economic Order Quantity (EOQ), Just-in-Time (JIT), and ABC analysis continue to provide foundational utility, their efficacy is substantially amplified when integrated with Internet of Things (IoT) architectures, artificial intelligence-driven predictive analytics, and digital twin simulations. Furthermore, the study identifies a critical research gap concerning the adaptation of these advanced systems within developing economy contexts, particularly in Sub-Saharan Africa, where infrastructural constraints and institutional voids necessitate context-specific hybrid models. The paper contributes to the literature by proposing an integrated conceptual framework that aligns classical inventory theory with digital transformation imperatives, offering actionable implications for scholars and practitioners engaged in procurement and supply chain management.

Keywords : Inventory Management, Supply Chain Resilience, Warehouse Optimization, Lean Inventory, Operations Research, Digital Twins, Industry 4.0, Cycle Counting, Developing Economies.

Paper Submission Last Date
31 - August - 2026

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