Digital transformation bolsters supply‑chain resilience and lifts operational and market performance, especially when firms build capabilities rather than merely buying tools. The benefits concentrate in manufacturing, among SMEs and mature firms, and operate mainly through reactive (response/recovery) resilience rather than proactive anticipation.

Main Finding

Digital transformation (DT) is positively associated with supply chain resilience (SCR), and SCR in turn is positively associated with firm performance. The DT→SCR link is stronger for digital capabilities than for digital technologies and stronger for reactive than for proactive dimensions of SCR. Contextual moderators (firm size and age, sector, country development, and national culture) significantly shape these relationships. SCR’s positive effects on performance are strongest for operational and market outcomes and weaker for financial performance.

Key Points

• Evidence base: systematic review and meta-analysis of 200 empirical studies.
  • 124 studies analyzed the DT → SCR relationship.
  • 104 studies analyzed the SCR → performance relationship.
• DT → SCR
  • Positive overall association.
  • Stronger effects for digital capabilities (skills, processes, routines) than for discrete digital technologies (tools, platforms).
  • Stronger association with reactive SCR (ability to respond/recover) than proactive SCR (anticipation/absorption).
  • Larger effects among SMEs and mature firms.
  • Stronger in manufacturing sectors and in developing-country contexts.
  • Moderated by national culture: stronger where power distance is high, individualism is low, and long-term orientation is stronger. Uncertainty avoidance showed no significant moderating effect.
• SCR → Performance
  • Positive overall association.
  • Stronger effects on operational performance (efficiency, continuity) and market performance (reputation, customer retention) than on financial performance.
• Contribution: integrates antecedents, boundary conditions, and performance outcomes to provide a context-sensitive picture of how DT supports supply chain resilience.

Data & Methods

• Approach: systematic literature review combined with quantitative meta-analysis.
• Sample: 200 empirical studies collected and coded for effect sizes and moderators.
  • Subsets: 124 DT–SCR effect estimates, 104 SCR–performance effect estimates (studies may overlap).
• Moderation analyses: tested heterogeneity across DT forms (capabilities vs technologies), SCR dimensions (reactive vs proactive), firm characteristics (size, age), industry (manufacturing), country development level, and national culture (power distance, individualism, long-term orientation, uncertainty avoidance).
• Robustness checks (reported): heterogeneity assessments and moderator tests; standard meta-analytic safeguards (e.g., publication-bias checks) were used though specific statistics are not reported here.

Implications for AI Economics

• AI as capability vs as technology: The finding that digital capabilities matter more than discrete technologies suggests economic value from AI investments will be higher when firms build complementary organizational capabilities (skills, processes, governance) rather than merely purchasing AI tools. Economic analyses should distinguish AI adoption (technology) from AI-enabled capabilities.
• Reactive vs proactive resilience and AI’s role: Empirical evidence shows stronger DT effects on reactive SCR. Economists should test whether AI primarily improves rapid detection/response (reactive—inventory reallocation, demand forecasting correction) versus anticipation/adaptation (proactive—scenario planning, supply network redesign). Policy and investment priorities may differ depending on which function AI most effectively enhances.
• Heterogeneous returns to AI across firms and countries:
  • SMEs and mature firms appear to gain more DT→SCR benefits—AI policies (subsidies, training, shared platforms) targeted at SMEs in supply chains could yield outsized resilience gains.
  • Greater DT→SCR effects in manufacturing and developing countries imply that AI diffusion in manufacturing value chains of developing economies could materially improve resilience and market performance, but requires capability-building.
• Performance channels: Because SCR links more strongly to operational and market performance than to short-term financials, cost–benefit models of AI in supply chains should incorporate operational continuity, service reliability, and reputational/market-share effects, not only immediate financial returns.
• Cultural and institutional context matters: Cross-country economic models of AI investment returns should account for cultural moderators (power distance, individualism, long-term orientation). Blanket extrapolation from one country to another risks biased welfare or adoption forecasts.
• Research priorities for AI economists:
  • Causal identification: more longitudinal, quasi-experimental, or randomized studies to isolate causal effects of AI-based DT on SCR and downstream performance.
  • Mechanisms: unpack which AI capabilities (predictive analytics, prescriptive optimization, anomaly detection, autonomous execution) drive reactive vs proactive resilience.
  • Distributional effects: assess how AI-enabled resilience affects supplier bargaining power, labor demand in supply chains, and inequality across firms and regions.
  • Cost-effectiveness and externalities: quantify costs of capability building, potential market concentration, and welfare implications of resilience improvements.
  • Measurement: standardize metrics for AI adoption, digital capability maturity, and SCR dimensions to improve comparability across studies.
• Policy takeaway: promoting AI capability development (skills, data governance, integration practices) and targeting support to SMEs and manufacturing in developing contexts is likely to deliver stronger resilience and operational/market performance gains than technology subsidies alone.