Prescriptive AI delivers bigger supply‑chain gains than forecasting alone: firms reporting stronger prescriptive analytics capabilities show markedly higher resilience, agility and cost efficiency, and integration across the chain amplifies these effects.

Main Finding

Prescriptive analytics capability (PrAC) delivers larger and more direct strategic value than predictive analytics capability (PAC) for end-to-end supply chain performance. In a survey of 312 supply‑chain professionals and SEM analysis, PrAC had stronger effects on resilience (β = 0.53), agility (β = 0.47), and cost efficiency (β = 0.49) (all p < 0.001). PAC remained important as a sensing capability (agility β = 0.41; resilience β = 0.36; p < 0.001). End‑to‑end integration (EEI) partially mediated relationships and increased explanatory power to R² = 0.58 for resilience and R² = 0.52 for agility.

Key Points

• Theoretical framing: dynamic capabilities theory — PAC functions as sensing/forecasting; PrAC functions as decision‑optimization (prescription/automation of trade-offs).
• Comparative effects:
  • PAC → Agility β = 0.41, PAC → Resilience β = 0.36 (both p < .001).
  • PrAC → Resilience β = 0.53, PrAC → Agility β = 0.47, PrAC → Cost Efficiency β = 0.49 (all p < .001).
• EEI (end‑to‑end digital/information integration) is a partial mediator that amplifies the benefits of analytics capabilities.
• Correlations (all p < .01): PrAC–Resilience r = 0.70; PrAC–Agility r = 0.68; PrAC strongly correlates with other performance outcomes.
• Measured outcomes: supply chain agility, resilience, cost efficiency, and service level (service level reported but key reported effects centered on agility/resilience/cost).
• Implementation barriers noted in literature and discussed: data/infrastructure gaps, model interpretability, organizational willingness, siloed analytics.

Data & Methods

• Design: Cross‑sectional quantitative survey; deductive, hypothesis‑testing approach.
• Sample: N = 312 supply‑chain, logistics, and operations managers (manufacturing 39.7%, retail 24.4%, logistics 23.1%, distribution 12.8%); purposive (non‑probability) sampling; pilot tested questionnaire.
• Constructs: Predictive Analytics Capability (PAC), Prescriptive Analytics Capability (PrAC), End‑to‑End Integration (EEI), Supply Chain Agility (SCA), Cost Efficiency (CE), Supply Chain Resilience (SCR).
• Analysis: Descriptive stats, reliability (Cronbach’s α, composite reliability), AVE/Fornell‑Larcker for validity, SEM (AMOS/SmartPLS), bootstrapping (5,000 resamples) for mediation, and model fit indices (CFI, TLI, RMSEA, SRMR).
• Key model performance: R² up to 0.58 for resilience and 0.52 for agility after accounting for mediation.
• Limitations relevant to inference: cross‑sectional design, purposive sampling, self‑reported measures — limit causal claims and generalizability; implementation heterogeneity not fully observed.

Implications for AI Economics

• Investment prioritization: Firms and economists evaluating AI investments should treat prescriptive analytics as higher‑return strategic investments (larger marginal effects on resilience, agility, and cost efficiency) once predictive capacity is established.
• Role of integration as multiplier: End‑to‑end data and systems integration materially increases realized returns to analytics investments — integration costs should be incorporated into ROI models and treated as complementary capital.
• Productivity and cost structure: PrAC’s link to cost efficiency (β = 0.49) suggests prescriptive AI can reduce operating margins and decision latency; this has implications for firm‑level productivity, pricing power, and competitive dynamics in industries with heterogeneous adoption.
• Policy and firm strategy: Economic policies and firm strategies that subsidize integration infrastructure, data standards, or lower barriers to model interpretability can accelerate value capture from prescriptive systems and improve system‑level resilience.
• Distributional and market effects: Widespread adoption of prescriptive optimization may compress operational margins and raise entry barriers (scale economies in integration/data). Economists should model second‑order effects: labor reallocation, market concentration, and changes in bargaining across supply‑chain partners.
• Research priorities for robust economic evaluation:
  • Longitudinal or quasi‑experimental studies to estimate causal effects on costs, revenues, and profits.
  • Firm‑level cost‑benefit accounting including integration and governance costs.
  • Heterogeneity analysis by firm size, sector, and data maturity to map marginal returns and adoption thresholds.
  • Modeling systemic risk: how centralized prescriptive optimization affects systemic fragility vs. resilience.

Concise recommendation: For firms aiming measurable economic gains from AI in supply chains, sequence investments — build predictive sensing, invest in end‑to‑end integration, then deploy prescriptive optimization; include integration and interpretability costs in ROI models and prioritize evaluation designs that can identify causal impacts.