AI is delivering sizable supply‑chain gains—roughly 20–35% improvements in forecasting and inventory efficiency—but Latin American firms are largely absent from the evidence base and risk being left behind because infrastructure, talent and data barriers block deployment.

Main Finding

A scoping review of 66 peer‑reviewed studies (2020–2025) shows that AI adoption in supply chain management (AI–SCM) delivers measurable operational gains—typical reported ranges include 20–35% improvements in demand‑forecast accuracy and 20–35% inventory reductions without service‑level loss—while also strengthening disruption response. However, Latin America is markedly underrepresented (only 4.5% of the corpus) and faces structural barriers (limited infrastructure, talent shortages, high implementation costs, poor data quality) that limit capture of the projected AI–SCM market growth (estimates up to USD 41–58 billion by 2030).

Key Points

• Corpus and coverage
  • 66 studies published 2020–2025, identified via a PRISMA‑ScR scoping review.
  • Geographic skew: US, China, India, UK account for ~71% of corresponding author affiliations; Latin America = 4.5%.
  • Peak publication years: 2023–2024 (53% of corpus).
• Reported quantitative benefits (ranges from included studies and industry surveys)
  • Demand forecast accuracy: +20–35% (several ML/deep learning studies; McKinsey median reductions in forecast error ~30% in surveyed CPG firms).
  • Inventory reductions: 20–35% reduction without service loss; inventory maintenance cost reductions reported 15–25%.
  • Waste reductions for perishables and better recovery from disruptions reported (various RL and hybrid models).
• Dominant AI techniques and applications
  • Techniques: classical ML and deep learning dominate; reinforcement learning (RL) for inventory control; digital twins for real‑time decisions; generative AI and AI–blockchain integrations emerging.
  • Applications: demand forecasting, inventory optimization, risk/resilience management, real‑time decision support, traceability/sustainability use cases.
• Barriers to diffusion (especially in emerging economies / Latin America)
  • Infrastructure gaps (connectivity, cloud/edge compute), poor/fragmented data, limited skilled talent, high upfront implementation costs, limited local evidence and case studies.
• Authors’ contributions beyond mapping
  • Contrast of AI implementation contexts (developed vs emerging economies).
  • Case discussion of Port of Lázaro Cárdenas to illustrate opportunities and constraints in an actual Latin American logistics hub.
  • Proposal of an AI–SCM adoption framework for Latin America and identification of empirical research priorities and policy implications.

Data & Methods

• Review type: Scoping review reported using PRISMA‑ScR; registered internally (protocol Jan 5, 2025).
• PCC framing:
  • Population: supply‑chain organizations (suppliers → last‑mile distributors), with attention to Latin American actors.
  • Concept: AI techniques (ML, deep learning, NLP, RL, generative AI, digital twins) applied to SCM.
  • Context: publications from Jan 2020 – Dec 2025.
• Search strategy:
  • Databases: Scopus, Web of Science, IEEE Xplore, Google Scholar (for gray literature), SciELO (to capture Latin American research).
  • Example search string (English): ("Artificial Intelligence" OR "Machine Learning" OR "Deep Learning" OR "Generative AI" OR "Reinforcement Learning") AND ("Supply Chain Management" OR "Supply Chain" OR "Logistics") AND ("Optimization" OR "Forecasting" OR "Resilience" OR "Decision‑Making" OR "Adoption").
  • Languages: English and Spanish.
• Screening and selection:
  • Initial hits: 775 records (Scopus 187; WoS 142; IEEE 96; Google Scholar 312; SciELO 38).
  • Duplicates removed: 255. Titles/abstracts screened: after that, 350 records excluded. Full texts assessed: 170, of which 104 excluded. Final included: 66 studies.
  • Inter‑reviewer agreement: 89% (Cohen’s κ = 0.78).
• Inclusion/exclusion highlights:
  • Included: peer‑reviewed empirical research, systematic reviews, case studies with applied/empirical content; full text available.
  • Excluded: non‑peer conference presentations, editorials/commentaries, studies lacking methodological detail, studies outside SCM.
• Data mapping and synthesis:
  • Mapping matrix (Supplementary S1) captured bibliographic info, methodology, AI technique, SCM function/sector, reported benefits, barriers, references to emerging/Latin American contexts, implications.
  • Quality check (descriptive, not a formal risk‑of‑bias): assessed clarity of question, reproducibility of methods, transparency of datasets/outcomes. Studies failing these were excluded earlier.
  • Synthesis: thematic‑narrative grouping into four dimensions — applications, benefits, barriers, contextual implications. Descriptive quantitative ranges reported where comparable.

Implications for AI Economics

• Productivity and cost effects
  • Evidence points to meaningful operational productivity gains (better forecasts, lower inventories, reduced waste). These gains can improve firm-level margins and working‑capital efficiency, especially in inventory‑intensive sectors (CPG, retail, logistics).
• Market size and growth dynamics
  • Projected market growth (USD ~41–58B by 2030) implies large investment flows into AI‑enabled SCM tools and services; rapid CAGR implies first‑mover advantages for firms and countries that can adopt at scale.
• Distributional and competitiveness consequences
  • Uneven adoption risks widening productivity gaps between developed economies (high adoption) and emerging markets (low adoption). Latin American firms may lose competitiveness in regional/global value chains unless adoption barriers are addressed.
• Capital–labor and skills implications
  • Adoption shifts skill demand toward data engineering, ML model maintenance, and digital operations roles. Shortage of such skills in Latin America implies potential wage premia for scarce talent or offshoring of AI capabilities.
• Data and platform economics
  • High returns to quality, integrated data (sales, logistics, supplier signals) create incentives for data sharing platforms and consortia; weak data governance and fragmented information systems in emerging economies reduce attainable gains.
• Policy and public‑good roles
  • Public interventions can materially affect diffusion: investments in digital infrastructure (broadband, cloud), workforce training programs, subsidies or matching grants for pilot projects, public data standards and interoperability, and incentives for public‑private partnerships in logistics hubs.
• Research and measurement priorities for AI economics
  • Need for more empirical, context‑specific studies in Latin America quantifying firm‑level ROI, labor impacts, and spillovers. Randomized pilots or phased rollouts could generate causal evidence on productivity and employment effects.
  • Investigate complementarities (organizational change, governance, supplier networks) required to realize AI gains; measure distribution of benefits across firm sizes and sectors.
• Strategic recommendations (inferred from authors’ proposed framework)
  • Latin American policy and firm strategy should prioritize: (1) data infrastructure and governance; (2) targeted skills development and talent retention; (3) financing mechanisms for SMEs (grants, tax incentives, concessional loans); (4) pilot projects in strategic nodes (ports, cold chains, agribusiness) to create demonstrative evidence; (5) international collaboration to access technology and expertise.
• Long‑run considerations
  • If adoption barriers are overcome, AI–SCM can raise export competitiveness and lower trade costs; but without policy action, the region risks technological marginalization and widening productivity dispersion.

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