Background: Contemporary supply chains face unprecedented levels of volatility driven by demand uncertainty, supplier disruptions, geopolitical shifts, and sudden shocks such as pandemics. Theoretical and empirical work across operations research, logistics, and management science has explored facets of resilience, inventory policy under disruption, forecasting during crises, and strategic decisions such as reshoring. However, fragmented treatments limit a comprehensive understanding of integrative strategies that span forecasting, inventory design, network configuration, and organizational memory.

Purpose: This article synthesizes seminal and recent contributions to provide an integrated, publication-ready theoretical framework for resilient supply chain design. The aim is to unify modelling approaches for inventory under disruption, strategic facility location and reshoring decisions, forecasting and planning under pandemic-like conditions, and learning mechanisms that enhance resilience. The framework emphasizes interactions among stochastic supply interruptions, demand dynamism, strategic relocation, and data-driven detection of hidden supply chain linkages.

Methods: Drawing strictly from the provided literature, the study undertakes an exhaustive conceptual synthesis, weaving robust optimisation and stochastic inventory modelling (including EOQ extensions and partial backordering), facility location robustness, forecasting under pandemic dynamics, graph-based detection of hidden links, and cognitive mapping of domino effects. The methods section elaborates purely text-based methodological constructs: model families, solution philosophies, evaluation metrics, and comparative analytical approaches, all explained without equations or visual constructs.

Results: The synthesis produces a multi-layered resilience design architecture: (1) demand-adaptive inventory policies that combine safety-stock rethinking with partial backordering accommodation and supplier diversification; (2) robust facility-location strategies integrating disruption probabilities and demand uncertainty; (3) forecasting and planning processes that adapt growth-rate estimation under rapid regime shifts; (4) machine-learning-driven network inference to reveal hidden dependencies; and (5) organizational memory and experiential learning as a core resilience capability. Each element is elaborated with theoretical implications, practical trade-offs, and counterfactual analyses based on the literature.

Conclusions: Integrative resilience requires coordinated strategies across forecasting, inventory rules, network design, and organizational learning. Firms should treat reshoring or onshoring as strategic levers to be weighed against diversification and flexibility investments rather than as singular solutions. The framework highlights open research avenues: empirical calibration of integrated models, psychological and organizational barriers to memory adoption, and scalable machine learning tools for link prediction in large, fragmented supply networks.

Supply chain resilience, disruption management, inventory with partial backordering,, facility location robustness

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