Design-Science Framework for Decision-Centric Supply Chain Resilience

Abstract

Geoeconomic shocks—including tariff escalations, sanctions, export controls, and commodity dislocations—have increased disruption frequency and exposed limitations of steady-state supply chain planning. This paper develops a design-science, governance-centred framework for resilient supply chain decision-making by synthesising institutional resilience guidance and published empirical findings on analytics-enabled supply chain performance. The framework is operationalised as a modular operating model spanning (i) (i) sensing and early warning, (ii) predictive disruption modelling under regime shifts, (iii) prescriptive optimisation with human-in-the-loop controls, (iv) digital-twin simulation for scenario stress-testing, and (v) execution orchestration with auditability and compliance alignment. The paper provides an end-to-end reference architecture (Figure 1), a resilience analytics control matrix linking modules to prerequisites and governance controls (Table 1), and maturity-aligned implementation pathways with measurable resilience KPIs, including time-to-detect, time-to-decide, and time-to-recover. The findings emphasise that resilience improvements require both robust models and disciplined governance to ensure adoption and accountable execution.