Big data analytics and supply chain performance the roles of supply chain visibility and information quality

Abstract

Manufacturing operations in Ghana continue to struggle with ongoing interruptions across their supply networks, issues that consistently chip away at their operational effectiveness and erode their ability to stay competitive. Even as digital tools become more widespread and the volume of operational information grows exponentially, most companies still haven't managed to properly harness big data analytics for better supply chain coordination and smarter management choices. This investigation set out to examine how big data analytics shapes supply chain results within Ghana's manufacturing landscape, paying special attention to how visibility throughout the supply chain acts as a bridge and how information quality influences that relationship's strength. Drawing on dynamic capabilities theory, information processing theory, and contingency theory as its foundation, the researchers adopted a quantitative methodology. They gathered responses through structured surveys from 272 manufacturing businesses operating in the Greater Accra Region, then processed the information using Partial Least Squares Structural Equation Modelling. What emerged from the analysis shows that big data analytics delivers a measurable and beneficial effect on how supply chains perform. Beyond this direct connection, the study uncovered that visibility across the supply chain serves as a crucial link between analytics efforts and performance gains, primarily because it enables quicker information exchange between partners, smooths out coordination challenges, and sharpens the organization's ability to respond to changing conditions. The evidence further suggests that when information meets high standards for accuracy, timeliness, and relevance, it amplifies the positive impact analytics has on supply chain visibility. From a practical standpoint, the message here is clear: simply pouring resources into analytics technology won't automatically translate into better outcomes unless companies simultaneously build robust systems for seeing across their supply chains and maintain rigorous information standards. By bringing fresh empirical evidence to the table, this work enriches academic conversations while offering concrete direction for manufacturing leaders and policy architects working to strengthen analytical capacities, shore up data integrity, and ultimately push supply chain results and market standing to higher levels.