Data-Driven Optimization Models and Performance Enhancement in Pakistan’s Supply Chain Networks

Abstract

Demand of supply chain networks to incorporate data-driven optimization models has been brought about by the growing complexity of global and domestic markets in a bid to improve performance and competitiveness. The supply chains in Pakistan have structural inefficiencies, fluctuating demand, infrastructural bottlenecks, and a lack of digitalization. This paper will discuss how data-driven optimization models (including predictive analytics, big data analytics and algorithm-based decision systems) can be used to enhance the performance of supply chains in Pakistan manufacturing, logistics, and agro-industrial industries. Applying the analytical and empirical framework, the study measures the role played by data-enabled decision-making in cost efficiency, accuracy in demand forecasting, flexibility of operation, and resiliency of the network. The results emphasize that companies with data-driven optimization achieve a great enhancement of supply chain responsiveness and total performance, though issues connected with data characteristics, technological preparedness and skills deficiency remain. The research offers strategic recommendations to managers and policy makers interested in transforming the supply chain competitiveness of Pakistan by using analytics.