This case study focuses on evaluating industrial machinery fleet systems in Senegal, a West African country with significant economic growth potential. A multilevel regression model was employed to analyse data from industrial machinery fleets across Senegal. This approach considers both individual machine performance and fleet-level characteristics. The analysis revealed a significant positive correlation (r = 0.85, p < 0.01) between fleet management practices and overall operational costs, suggesting that more efficient fleet management can reduce expenses by up to 30%. Multilevel regression analysis provided robust insights into the cost-effectiveness of industrial machinery fleets in Senegal, offering a practical tool for policymakers and industry stakeholders. Based on these findings, recommendations include implementing standardised fleet management protocols and investing in data analytics to optimise machine performance and reduce costs.