Data-Driven Sustainability: How Predictive Analytics ShapeSupply Chain Performance - Ismoth Zerine; Younis Ali Biswas; Md Zulkernain Doha; Humayra Mehreen Meghla; Mohammad Rashed Hasan Polas (Annals of Management and Organization Research, 2025)
Purpose: The integration of predictive analytics into supply chains has emerged as a critical  driver of sustainability in the manufacturing sector. This study explores the role of predictive analytics in enhancing sustainable supply chain performance, with a particular focus on manufacturing firms in Dhaka, Bangladesh. Research Methodology: This study adopts a positivist paradigm with a hypothetical deductive approach and employs a cross-sectional design. Data were collected from 211 manufacturing firms using stratified random sampling and structured questionnaire surveys.   Results: The findings revealed that supply chain transparency, predictive analytics accuracy, data integration, and organizational sustainability goals positively and significantly influenced sustainable supply chain performance. However, no significant relationship was found between technology readiness and sustainable supply chain performance, indicating the need for further investigation into factors beyond technological capabilities. Conclusions: This study concludes that while predictive analytics and aligned organizational practices are key drivers of sustainable supply chain performance, technology readiness alone is insufficient, highlighting the importance of integrated strategies beyond infrastructure capability. Limitations: This study is limited to manufacturing firms in Dhaka and adopts a cross-sectional design, which restricts the generalizability of the findings. Future research should explore longitudinal studies and incorporate other industries to provide a broader perspective on sustainable supply chain performance. Contribution: These results highlight the importance of fostering transparent practices, enhancing predictive analytics capabilities and aligning organizational goals with sustainability objectives. The practical implications include strategies for improved data integration and analytics adoption to drive sustainable outcomes