Fifteen years of quantitative research show supply-chain resilience comprises distinct proactive and reactive modes with shifting enablers—visibility and organization gave way to agility, and technology/analytics now lead. The trend implies rising demand for AI/advanced analytics in supply chains, but returns depend on industry, supply‑chain position and organizational complements.

Main Finding

A systematic review of 160 quantitative journal articles (2012–2026) shows supply chain resilience is usefully split into proactive and reactive resilience with different dominant enablers; across 15 years the literature’s focus shifted from visibility to organizational aspects, then to agility, and most recently to technology (with rising interest in analytics and sustainability). Technology adoption, organizational factors, agility, learning orientation, and external factors are the most frequently cited enablers, but dominant enablers vary by industry, sector and supply-chain location.

Key Points

• Sample and scope
  • Initial pool: 1,173 articles containing the exact phrase “supply chain resilience” in the title from three top-tier journal publishers.
  • Final review: 160 articles (all quantitative; survey data or business records).
  • Time window: 2012–2026 (15 years).
• Variables and constructs
  • Two dependent variables were extracted: (1) supply chain resilience (as a construct) and (2) the impact of supply chain resilience on outcomes.
  • 24 mediating/independent variables were coded from hundreds of variable formulations across studies; these represent the enablers of resilience.
• Thematic findings
  • Dual-mode resilience: distinction between proactive resilience (anticipation/mitigation) and reactive resilience (response/recovery) — different sets of enablers dominate each.
  • Top enablers across the literature: technology implementation, organizational aspects, agility orientation, learning orientation, and external factors.
  • Temporal shift in dominant enablers by period:
  • 2012–2014: visibility orientation emphasized.
  • 2015–2017: organizational aspects became dominant.
  • 2018–2023: agility orientation dominated (six-year span).
  • 2024–2026: technology implementation leads; analytics discussion begins in the 2018–2023 period and sustainability orientation rises strongly in 2024–2026.
• Heterogeneity
  • Dominant enablers vary by industry type, sector, and supply-chain location (e.g., upstream vs downstream functions).
• Future research directions highlighted by the review
  • Analytics implementation (AI/advanced analytics) and sustainability orientation as under-explored enablers worthy of further study.

Data & Methods

• Study design: systematic literature review of peer-reviewed journal articles (three top-tier publishers) over 2012–2026.
• Inclusion criteria: articles with the exact phrase “supply chain resilience” in the title; quantitative empirical studies using surveys or business records.
• Screening process: 1,173 title matches → 160 articles selected for detailed extraction and coding.
• Coding framework:
  • Two dependent variables coded (resilience construct; resilience impact).
  • 24 mediating/independent variables derived by grouping and harmonizing hundreds of variant measures into common enabler categories.
• Temporal analysis: periodization into four blocks (2012–2014, 2015–2017, 2018–2023, 2024–2026) to detect shifts in research emphasis.
• Limitations of the review (implicit in methods):
  • Restriction to three publishers and title-based search risked missing relevant studies that use synonymous terms or place “supply chain resilience” in abstract/keywords only.
  • Exclusion of qualitative research and non-top-tier outlets may bias findings toward measurable, surveyable constructs.
  • Heterogeneity in measurement across studies required aggregation into broad enabler categories, possibly smoothing nuance.

Implications for AI Economics

• Demand for analytics and AI investment:
  • The recent rise of technology and analytics as resilience enablers implies growing firm demand for AI tools, personnel, and data infrastructure—affecting capital and labor allocation in supply-chain-related industries.
• Sectoral heterogeneity in returns to AI:
  • Variation in dominant enablers by industry suggests returns to AI/analytics investment will be sector-specific; economic models should allow heterogeneous productivity gains across sectors and supply-chain positions.
• Complementarities and organizational capital:
  • Organizational aspects and learning orientation consistently matter; AI adoption likely has higher returns where organizational practices, managerial capability, and learning systems complement the technology—important for models of adoption and diffusion.
• Proactive vs reactive resilience and AI value:
  • AI and predictive analytics may disproportionately raise proactive resilience (anticipation and mitigation), whereas other investments (redundancy, flexibility) influence reactive resilience—policy and firm investment analysis should distinguish these channels.
• Measurement and causal inference:
  • Existing literature is mostly observational/survey-based; AI economics should prioritize causal identification (experiments, natural experiments, panel causal methods) to estimate the causal effect of analytics/AI on resilience and firm performance.
• Policy and market design:
  • If AI improves resilience, public policy (subsidies, data-sharing platforms, standards) could accelerate socially valuable adoption, especially where market failures (data externalities, coordination problems) exist.
• Sustainability interactions:
  • Rising attention to sustainability orientation suggests interactions between green investments and AI-driven resilience; economic analysis should evaluate trade-offs and complementarities (e.g., carbon intensity vs resilience gains).
• Data and infrastructure constraints:
  • AI-driven resilience depends on data availability, interoperability, and compute—economic models should incorporate data frictions, privacy regulation, and platform-market dynamics.
• Research agenda pointers for AI economists:
  • Quantify heterogeneous productivity gains from AI across supply-chain roles (procurement, logistics, inventory).
  • Evaluate organizational complements to AI: which management practices amplify AI’s impact on resilience.
  • Study distributional effects: how AI-induced resilience affects firm survival, market competition, workers, and small suppliers.
  • Explore public-good characteristics of resilience-enhancing AI investments and the case for policy intervention.

If you want, I can (a) turn these observations into testable hypotheses and empirical specifications for causal estimation, or (b) outline a research design to measure AI’s causal effect on proactive vs reactive resilience.