AI-Driven Supply Chain Resilience in Nigerian E-Commerce: A Simulation-Based Framework for Demand Sensing, Inventory Optimization, and Last-Mile Logistics
Year: 2026
Type: article
Abstract: African e-commerce faces a paradox: rapid growth in digital commerce alongside infrastructure deficits, volatile demand, and fragmented logistics networks.AI is widely promoted as a solution for building supply chain resilience, yet firm-level evidence from African contexts remains scarce.This paper develops and tests a simulation-based framework integrating three AI-driven modules -demand sensing, inventory optimisation, and last-mile logisticsusing publicly available, literature-parameterised synthetic datasets for Nigerian retail and e-commerce.Drawing on Dynamic Capabilities Theory and Contingency Theory, the framework shows how AI builds resilience under imperfect data, infrastructure constraints, and demand volatility.Machine-learning demand sensing reduced forecast error by 28.8% relative to a seasonal naive baseline (MAPE 1.80% vs. 2.53%; MASE 0.70 vs. 0.99).AI-driven inventory policies outperformed fixed reorder points in three of four disruption scenarios, reducing stockouts by up to 100% under supplier delay and cutting total cost by 8.9-29.6%;however, under a 40% demand spike, the AI policy underperformed, and the forecast was found to underestimate demand by 28.1%, revealing that forecast-driven seizing fails when the sensing model is not recalibrated to structural demand breaks.Route optimisation reduced simulated delivery distance by 30.0%, 41.3%, and 43.9% across 50-, 100-, and 200-customer benchmarks -a reduction that scales with network size.The paper contributes an integrated, reproducible framework, a contingency-sensitive extension of Dynamic Capabilities Theory, and a methodological template for simulation-based resilience research where primary data access is constrained.An ETA prediction model was attempted but achieved no better than chance on the synthetic data, identifying a boundary of the framework.
Institutions: Imo State Polytechnic Omuma, University of Nigeria
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7225751484