LEVERAGING DATA ANALYTICS FOR E-COMMERCE SUPPLY CHAIN TRANSFORMATION: AN EMPIRICAL STUDY OF THE PAKISTANI MARKET

Abstract

Responsive and data-driven supply chain models are in greater demand because of the fast development of the field of e-commerce. However, a number of companies are still functioning under older logistics systems, resulting in inefficient forecasting, inventory control, and customer satisfaction. This paper explores how data analytics, particularly descriptive, predictive, and prescriptive analytics, and analytics tools affect e-commerce supply chain performance in Pakistan. The research was based on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT) and focused on explaining the impact of improving operational efficiency, agility, and decision-making through these analytics elements.

The research involved a quantitative research design whereby a structured questionnaire was developed and administered to a sample group of 350 respondents with different responsibilities in the supply chain within the e-commerce industry. The data was analyzed using SPSS and SmartPLS 4.0. The findings were that all four independent variables had significant positive connections with supply chain performance. Particularly, predictive analytics had the highest effects (beta = 0.412, p < 0.001), followed by prescriptive analytics and analytics tools.

The research says that e-commerce companies should invest in advanced analytics infrastructure, develop the functions within organizations, and integrate decision support throughout the chain. These results have theoretical contributions and have practical implications toward enhancing the responsiveness of supply chains and maintaining competitive advantage within the digital commerce setting.