The Role of Artificial Intelligence in Green Supply Chain Management
Abstract: As environmental concerns continue to grow, industries are compelled to implement sustainable supply chain processes. Green supply chain management (GSCM) has become a key strategy for reducing the negative effects of supply chains on the environment. It includes anything from energy-efficient logistics to environmentally friendly product design. Simultaneously, artificial intelligence (AI) is transforming supply chain management through improved decision-making, optimization, and efficiency capabilities. Businesses have a great chance to achieve sustainability objectives while preserving operational performance at the nexus of AI and GSCM. This study aims to investigate how AI applications are currently used in green supply chain management, identify possible advantages and difficulties, and offer predictions about recent advances in the field. The results show that supply chain sustainability can be significantly increased by using AI applications. More precise demand forecasting and improved waste management techniques that reduce resource use are two major advantages. AI such as machine learning and predictive analytics, enable businesses to automate labour-intensive procedures and make smart judgements instantly while monitoring environmental performance. In conclusion, AI has a lot of potential to promote environmentally friendly supply chain management approaches that also improve operational effectiveness.
Authors
- Courage Obofoni Esechie
- Sodiq Fowosere
- Sarah Namboozo
- Friday Anwansedo
Institutions
- Southern University and Agricultural and Mechanical College (US)
- Christian Dior (France) (FR)
- Institut für Bauwerkserhaltung und Sanierung (Germany) (DE)
Publication Details
- Journal
- International Journal of Latest Technology in Engineering Management & Applied Science
- Published
- 2025-03-24
- DOI
- https://doi.org/10.51583/ijltemas.2025.14020033
- Citations
- 10
- Primary Topic
- Sustainable Supply Chain Management
- Type
- article
- Field-Weighted Citation Impact
- 22.26