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Supply chain transparency has emerged as a strategic imperative for organizations navigating heightened regulatory scrutiny, consumer demand for provenance accountability, and the operational risks exposed by recent global disruptions. Internet of Things (IoT)-enabled tracking systems encompassing RFID, GPS telematics, sensor networks, and blockchain-integrated asset monitoring represent a transformative class of technologies with the potential to deliver real-time, end-to-end supply chain visibility. Yet empirical evidence on the magnitude, boundary conditions, and organizational prerequisites of IoT-driven transparency gains remains fragmented and largely confined to single-industry case studies. This study evaluates the effect of IoT-enabled tracking system adoption on supply chain transparency across four dimensions: information availability, information accuracy, information timeliness, and stakeholder accessibility. Employing a mixed-methods research design, quantitative data were collected from 214 supply chain professionals across manufacturing, food and beverage, pharmaceutical, and logistics sectors using a validated structured questionnaire, supplemented by 28 in-depth semi-structured interviews. Structural Equation Modeling (SEM) was used to test hypothesized relationships between IoT adoption depth, organizational integration capability, and the four transparency dimensions. Findings reveal that IoT adoption depth exerts a strong positive effect on all four transparency dimensions, with the largest effect observed for information timeliness (β = 0.61, p < 0.001) and information accuracy (β = 0.57, p < 0.001). The relationship between IoT adoption and transparency is significantly moderated by organizational integration capability, such that firms with mature ERP-IoT integration architectures realize transparency gains approximately 2.3 times greater than those deploying IoT in isolated, disconnected deployments. Sector-level analysis reveals that pharmaceutical and food supply chains demonstrate the highest transparency gains, driven by regulatory compliance imperatives and the availability of dense sensor data. Qualitative findings identify interoperability deficits, data governance immaturity, and supplier resistance as the primary implementation barriers. A three-tier IoT Transparency Maturity Model is proposed, offering practitioners a structured pathway from reactive tracking to proactive, intelligence-driven supply chain transparency.