Equitable railway corridor investment under demand uncertainty: A two-stage distributionally robust bi-objective framework for sustainable regional development
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Sustainable Transport Systems, Railway Infrastructure Assessment, GeoAI and Spatial Modelling, Regional Accessibility Analysis, Transport Logistics Sustainability
Abstract
Purpose. This study aims to synthesise empirical and modelling evidence on inventory optimisation methods for raw materials, work-in-process, and finished goods in production and trading enterprises, and to translate that evidence into a practical, class-differentiated implementation framework deployable within standard warehouse management and enterprise resource planning systems. Methodology. A systematic review and meta-analytic synthesis of 31 peer-reviewed studies published between 2004 and 2025 was conducted following the PRISMA 2020 protocol. A random-effects model estimated by restricted maximum likelihood was applied to pool percentage cost-reduction effect sizes across 18 studies admissible to quantitative synthesis, complemented by a narrative synthesis of the remaining 13 studies. Pre-specified subgroup and moderator analyses examined the role of inventory class, demand pattern, and network complexity as effect-size moderators. Results. Distributional safety stock methods outperform classical normal approximations by a pooled mean of 9.3% (95% CI: 5.8–12.7%) at equivalent service levels, with the advantage being largest for high-variability SKU segments. Multi-echelon coordination yields a pooled mean cost reduction of 11.4% (95% CI: 6.9–15.9%), increasing significantly with network complexity and lead-time variability. Learning-based control methods deliver up to 16% cost reductions under complex network conditions but require substantial data and governance infrastructure. Commercial demand drivers systematically distort finished-goods inventory targets and require integration with sales-and-operations planning for accurate calibration. Theoretical contribution. The study provides the first cross-class synthesis covering raw materials, work-in-process, and finished goods within a unified evaluative framework, positioning machine learning and deep reinforcement learning methods alongside classical policy families and quantifying the boundary conditions for each approach. Practical implications. A six-phase, stepwise implementation framework is proposed, covering ABC-XYZ segmentation, forecast model selection, safety stock calibration, replenishment policy assignment, simulation-based parameter tuning, and KPI governance, enabling enterprises to achieve 9–16% reductions in inventory costs within existing WMS and ERP architectures.
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Copyright (c) 2026 Stabak Roy, Saptarshi Mitra
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