Mapping Resilience: Modeling Supply Chain System Diagnosis and Policy Response in the UK Based on a Mixed Study

Amidst the dual shocks of Brexit and the COVID-19 pandemic, this paper constructs a supply chain diagnostic framework integrating policy, labour, logistics, and ESG factors. Employing a mixed-methods approach: quantitatively, it synthesises six real-world cases via knowledge graphs, covering a primary observation window of 2021 with a ±12-month robustness interval. Using the ‘store/route-multi-granularity data’ mechanism to identify impact pathways, it applies hierarchical regression and double-difference analysis to identify marginal effects, while incorporating system dynamics simulation to evaluate strategic trade-offs. Core metrics include: - Disruption rate: Store-level ‘route/multi-granularity data’ disruption frequency route/factory" multi-granularity data to characterise impact mechanisms. Hierarchical regression and difference-in-differences identify marginal effects, supplemented by system dynamics simulations to evaluate strategic trade-offs. Core metrics comprise: Delivery timeliness: average transit hours from warehouse to store. Qualitatively, multi-case comparisons and policy text analysis explain vulnerability drivers and governance logic, triangulating with quantitative findings. Key insights: labour availability is the primary resilience driver, followed by transport capacity/cost; policy uncertainty and ESG events exert dual amplification effects on supply security and brand trust. Under baseline assumptions of ‘relatively stable demand, oil prices within predictable ranges, and pandemic intensity under control’, the supply improvement ranges for five policy/corporate scenarios are: S1 Quarantine Exemption ≈ 5–8%, S2 Visa/ Shortage List ≈ 8–12%, S3 Prison Labour (ROTL) ≈ 10–20%, S4 Rail/Micro-Warehouse Substitution ≈ 10–15%, S5 ESG Tightening (primarily reducing brand and compliance risks, indirectly improving supply). Key coefficients and scenario effects were statistically assessed using conventional 5% significance thresholds and/or 95% posterior intervals (detailed estimations and robustness checks are presented in the formal report). This analysis proposes a four-dimensional governance framework anchored by labour, underpinned by transport capacity, fortified by compliance, and powered by data as the operating system. This framework supports systematic decision-making and pathway selection for governments and enterprises across diverse scenarios. Limitations: Publicly available reports exhibit discrepancies in reporting standards. The availability and extrapolation potential of micro-level operational data from enterprises require further validation in subsequent research.