stdClass Object
(
    [id] => 17822
    [paper_index] => 202510-07-024396
    [title] => A CONCEPTUAL FRAMEWORK FOR MEASURING THE INFLUENCE OF ARTIFICIAL INTELLIGENCE ON SUSTAINABLE SUPPLY CHAIN MANAGEMENT
    [description] => 
    [author] => Dr. Joseph George , Renson Tomy, Dr Athira V T
    [googlescholar] => 
    [doi] => https://doi.org/10.36713/epra24396
    [year] => 2025
    [month] => October
    [volume] => 12
    [issue] => 10
    [file] => fm/jpanel/upload/2025/October/202510-07-024396.pdf
    [abstract] => The current attempts to make global sustainable supply chains have intensified by the strategic interaction of Artificial Intelligence (AI). Simultaneously, the growing use of AI in the logistics, forecasting, and inventory management domains has demonstrated that there is a lack of systematic influence of AI capabilities on sustainable supply-chain performance in the theoretical breadth. The current research fills this gap by creating a conceptual framework explaining the connection between AI technologies, that is, machine learning, blockchain, big-data analytics, and computer vision, and the main practices in Sustainable Supply Chain Management (SSCM). The framework also presupposes such fundamental operational constructs as demand forecasting, inventory management, and logistics optimisation as mediators that transfer operationalised AI capacity into sustainability outcomes in environmental, social and economic terms taking the lens of a circular economy. The model provides a guideline-based basis a foundation to the future empirical study, informs aggregate decision-making by managers facing the implementation of AI, and assists in the creation of policies that will help to sustain sustainable development in the technologically mediated supply-chain environment.
    [keywords] => 
    [doj] => 2025-10-14
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 12
    [journal_id] => 7
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Economics, Business and Management Studies (EBMS)
    [short_code] => IJHS
    [eissn] => 2347-4378
    [pissn] => 
    [home_page_wrapper] => images/products_image/2.EBMS.png
)
Error fetching PDF file.