Year: 2026
Type: article
Abstract: The COVID-19 pandemic exposed a structural weakness in healthcare supply chains: inventory is managed facility by facility, which limits regional visibility of local needs, available stock and opportunities to redistribute it. Prior research has examined demand forecasting, inventory optimization, emergency logistics and the ethics of resource allocation, but few studies integrate these streams in a single regional, rolling-horizon decision architecture that includes governance. This conceptual and methodological paper addresses that gap by proposing a predictive inventory allocation framework for regional pandemic preparedness. The framework comprises seven operational capabilities (uncertainty-adjusted demand forecasting, regional inventory visibility, multi-objective allocation, equity safeguarding, inventory pooling, interfacility redistribution and rolling-horizon adaptation), organized in four layers and coordinated by one governance mechanism. It serves six objectives: shortage mitigation, service continuity, distribution efficiency, allocation stability, reserve preservation and protection of vulnerable facilities and populations. Drawing on systems theory, dynamic capabilities theory, resource dependence theory, and distributive and procedural justice theory, we develop four propositions that link regional visibility, coordination, need-based allocation and reserve-protected redistribution to measurable outcomes, together with indicators and moderators. A formal multi-objective model is specified, and a stylized example with four hypothetical hospitals illustrates its mechanics against three baseline allocation rules. The framework has not been empirically validated; a three-phase validation strategy is outlined. The contribution is a theoretically grounded and operationally specified architecture that links forecasting, equity-aware allocation and conditional redistribution.
DOI: 10.55708/js0511001
Author: Dion Francis Mendes
Institution: University of the Cumberlands
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W7225391338