Effect of AI-Powered Demand Sensing on Supply Chain Agility in Selected Logistics Service Providers in Nigeria

The growing unpredictability of customer demand, traffic conditions, and service expectations has increased the need for agile supply chains in the logistics sector. This study investigates the effect of AI-powered demand sensing on supply chain agility in selected logistics service providers in Nigeria. The objectives are to: determine the effect of AI-powered demand sensing on supply chain responsiveness, examine its influence on supply chain flexibility, and evaluate its impact on decision-making speed among logistics service providers. A cross-sectional survey design was adopted. The population comprised operations managers, supply chain managers, and IT managers of selected logistics service providers operating in Lagos, Ogun, and Rivers States, with an identified population of 420 staff. The sample size was determined using the Taro Yamane (1967) formula, 205 respondents were selected through proportionate sampling across the participating firms. Data were collected using a structured questionnaire and analyzed using descriptive statistics and multiple regression. Results presented in figures and tables showed that AI-powered demand sensing significantly predicts supply chain agility, explaining 68.4% of its variance (R² = 0.684). Demand sensing had significant positive effects on responsiveness (β = 0.412, p < 0.05), flexibility (β = 0.367, p < 0.05), and decision-making speed (β = 0.298, p < 0.05). Figure 1 indicated that firms with higher adoption levels achieved approximately 45% faster response time, while Figure 2 showed an average 38% reduction in demand forecast errors. The findings suggest that AI-powered demand sensing enhances real-time visibility, improves forecasting accuracy, and supports quicker operational reconfiguration among Nigerian logistics service providers. The study concludes that AI-driven demand sensing is a major capability for achieving supply chain agility in a volatile environment. It recommends increased investment in AI analytics infrastructure, staff digital upskilling, and system integration across supply chain partners to maximize agility outcomes.