Improving supply chain sustainability using artificial intelligence: Evidence from the manufacturing sector

Businesses and governments worldwide are implementing measures to tackle the significant sustainability challenges in the manufacturing sector, including environmental, health and safety and productivity related. As the sector moves towards Industry 4.0, Artificial Intelligence (AI) is viewed as a promising solution to address these challenges. However, current knowledge on these technologies and their interplay with the triple bottom line (TBL) sustainability dimensions is scattered and unclear. This study seeks to bridge this gap by creating a comprehensive AI implementation framework consisting of application, data and computation layers. The AI applications are categorized into virtualization, forecasting, automation, and intelligent environment, while the computation layer comprises machine learning, deep learning, computer vision and natural language processing. These in turn use multimedia, time-series manufacturing, product parameter, sensor and location-based data inputs. Moreover, the framework assesses the impact of AI technologies on enhancing TBL sustainability, covering environmental, social, and economic aspects. This novel and comprehensive framework, which is not seen in the previous literature, can support the development of policy interventions and support systems to promote AI adoption in the manufacturing sector, while also achieving TBL sustainability goals.