A MULTI-METHOD APPROACH TO SUPPLY CHAIN RESILIENCE IN THE FAST-MOVING CONSUMER GOODS INDUSTRY: EVIDENCE FROM AN EMERGING ECONOMY

Abstract

This study investigates the impact of linear programming formulations, system dynamics simulations, and predictive analytics approaches on supply chain resilience and performance at Dangote Plc. Utilizing a mixed-method approach, the research analyzes employee perceptions to evaluate the effectiveness of these strategies from a sample of 83 respondents. Ordinal logistic regression and cross-correlation analysis were employed to examine the relationships between key variables. The findings reveal that supply network optimization and capacity constraint management significantly improved on-time delivery percentages, system dynamics simulations demonstrated complex, time-lagged relationships between transportation optimization and various supply chain factors, and predictive analytics approaches, particularly demand forecasting accuracy and real-time data analytics, played crucial roles in enhancing organizational service responsiveness. Practical recommendations include guidance for prioritizing supply chain improvement initiatives, emphasizing the importance of dynamic modeling approaches, and highlighting the need for advanced analytical capabilities in an increasingly complex business environment.