Year: 2025
Type: article
Abstract: This study explores the role of predictive analytics in optimizing sustainable supply chain performance. With the increasing focus on environmental sustainability, organizations are leveraging advanced data analytics tools to enhance operational efficiency, reduce waste, and minimize their environmental footprint. This research highlights how predictive models improve demand forecasting, transportation optimization, and supplier selection based on sustainability criteria. The study also identifies key challenges to integrating predictive analytics, such as data quality issues, high implementation costs, and system compatibility, which hinder organizations from fully realizing the potential of these tools. Despite these barriers, the findings emphasize the long-term benefits of predictive analytics in achieving both economic and environmental goals. The study concludes that organizations investing in predictive analytics can achieve better resource management, cost savings, and improved ESG (Environmental, Social, Governance) performance. The paper provides recommendations for overcoming integration challenges and maximizing the impact of predictive analytics in sustainable supply chain management.
Institution: University of New Haven
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4408121007