Africa’s informality, institutional hybridity and resource constraints reshape supply‑chain theory and open up high‑value opportunities for AI economics research; IJOPM’s AfIn initiative aims to build local capacity and publish theory and empirical work leveraging these contexts.

Main Finding

The paper argues that Africa’s distinctive contextual features — including a large informal economy, socio-cultural diversity, weak formal institutions, abundant but underutilized resources, and high environmental constraints — create unique operations and supply chain management (OSCM) phenomena. These idiosyncrasies both challenge existing OSCM theory and offer fertile ground for novel theoretical contributions. The authors introduce IJOPM’s Africa Initiative (AfIn) to support Africa-based OSCM research and provide a structured pathway for publishing and advancing theory using African contexts.

Key Points

• Seven contextual factors shaping OSCM research in Africa:
• Informal economy and organizations
• Socio-cultural diversity and complexity
• Traditional and survival-oriented cultures
• Weak formal institutions coexisting with strong informal institutions
• Population growth potential (demographic dynamics)
• Abundant natural resources but low development/outcomes
• High environmental constraints (infrastructure, geography, climate shocks)
• Five OSCM research themes where African contexts can advance theory:
• Serving consumer markets (distribution, last-mile, demand heterogeneity)
• Managing resources (resource extraction, allocation, quality gaps)
• Managing factor market rivalry (labor, land, and capital competition, informality)
• Managing environmental hostility (resilience, adaptation to shocks, infrastructure limitations)
• Managing institutions (formal vs informal governance, regulation, trust mechanisms)
• The AfIn initiative: motivation, objectives, review process, and researcher support mechanisms intended to build capacity and increase high-quality OSCM research on/from Africa.
• The paper is conceptual and synthesizes multiple literature streams to map how Africa’s contexts can be used to refine or generate OSCM theories.

Data & Methods

• Type of study: Conceptual/theoretical synthesis (no primary empirical data collection).
• Methods: Literature review across OSCM, development studies, institutional economics, and regional studies to identify contextual factors and link them to OSCM themes.
• Outputs: Framework linking seven contextual factors to five OSCM themes; description of AfIn’s structure (review pathways and support for Africa-based researchers).
• Implicit methodological recommendations for future empirical work include leveraging diverse data sources (administrative, survey, behavioral, remote sensing) and mixed-methods designs to capture institutional and informal dynamics.

Implications for AI Economics

• Research opportunities:
  • Informal economy as a laboratory: Africa’s large informal sectors enable study of how AI-driven automation, platform markets, and pricing algorithms affect informal firms and workers — including displacement, complementarities, and informal-contract dynamics.
  • Heterogeneous markets and fairness: Socio-cultural diversity and data sparsity create challenges/opportunities for fairness-aware ML and for evaluating external validity of AI economic models across population subgroups.
  • Institutions and algorithmic governance: Weak formal institutions with strong informal norms allow investigation into how algorithmic interventions (e.g., automated enforcement, marketplaces, credit scoring) interact with informal governance and trust networks.
  • Resource-constrained optimization: High environmental constraints (limited infrastructure, shocks) motivate development and testing of robust, low-data, low-compute AI methods for supply-chain optimization, demand forecasting, and inventory management.
  • Labor markets & demographic dynamics: Rapid population growth and large informal labor pools afford study of long-run labor reallocation under AI adoption, wage dynamics, and training/skill-biased technological change in contexts with low formal schooling.
  • Natural-resource economics & AI: Abundant resources but low outcomes open avenues for AI-assisted monitoring (satellite imagery), predictive models for value-chain improvements, and incentive/contract design under extraction externalities.
• Data considerations for AI economics studies:
  • Useful data sources: mobile-phone metadata, fintech/platform transaction logs, household/business surveys, administrative records, satellite/remote sensing, crowdsourced field data.
  • Challenges: measurement error, censoring, selection bias (informal actors may be absent from official datasets), privacy and ethical concerns, and limited digital trace coverage in some regions.
  • Recommended methods: combine causal inference (RCTs, natural experiments, instrumental variables) with structural modeling and simulation; employ transfer learning, domain adaptation, and robustness checks to deal with small or nonrepresentative datasets.
• Policy and model-design implications:
  • Algorithmic design should consider informal-contract enforcement, cash-based transactions, and heterogenous preferences.
  • AI policies must be tailored to weak institutional environments — e.g., algorithmic accountability mechanisms that operate without strong legal enforcement but leverage community norms.
  • Cost-effective, explainable models are preferred where computational resources and technical capacity are limited.
• Practical research directions:
  • Study platformization impacts on informal labor markets and small suppliers using causal designs.
  • Use satellite imagery + ML to measure resource flows, crop yields, and supply-chain disruptions, linking them to market outcomes.
  • Develop ML methods robust to intermittent data, high noise, and structural breaks due to shocks.
  • Evaluate AI-enabled policies (credit scoring, logistics routing, demand forecasting) through pilots and RCTs to measure welfare and distributional effects.

Overall, Africa’s OSCM contexts present rich, underexploited settings for AI economics: they expose mechanisms hard to observe elsewhere, require methodological innovations for data-sparse and institutionally complex environments, and offer high social value through applications that can raise productivity and resilience in resource-constrained systems.