Taxonomy of Inventory Uncertainty in Multi-Echelon Supply Chain: A Literature Review
Abstract
Inventory management in multi-echelon supply chains plays a central role in improving efficiency and profitability. However, it remains complex because demand variability, lead time fluctuations, and supply disruptions affect multiple stages. Although the field is supported by quantitative models, stochastic approaches, and digital technologies, current studies still show limited integration of sustainability, emerging digital tools, and comprehensive risk assessments in dynamic multi-level systems. This paper evaluates research performance on inventory uncertainty in multi-echelon supply chains and identifies major trends, research gaps, and future directions. The study uses a bibliometric approach and analyzes 100 Scopus-indexed publications collected on November 13, 2024, including forthcoming 2025 articles. The review follows the PRISMA framework for eligibility, screening, and inclusion, with keywords related to inventory uncertainty and multi-echelon systems. Data were processed through Biblioshiny in R for Scientometric analysis, including visualizations of source relevance, author productivity, institutional output, country contributions, citation patterns, keyword co-occurrences, and thematic evolution. The results show strong growth in publications after 2014, with significant contributions from Iranian universities and applications in the food industry. Leading journals include Computers and Industrial Engineering, which shows high relevance and impact. Dominant themes focus on supply chains, stochastic systems, inventory control, and optimization. Emerging interests include uncertainty analysis and reinforcement learning. Research gaps remain in integrating risk management with blockchain, predictive analytics, and sustainability across supply chain tiers. This study highlights the need for adaptive inventory strategies that help managers strengthen resilience and support new research on innovative models for complex uncertainty.