Artificial Intelligence and Big Data Analytics: Transforming Supply Chain and Sustainable Manufacturing to Achieve SDGs Agenda
Abstract
Purpose: The purpose of this work is to understand the changes brought by artificial intelligence (AI) and big data analytics (BDA) to supply chain management (SCM) and sustainable manufacturing (SM) in the developing countries context. It seeks to identify key enablers, advantages, and challenges of implementing those technologies toward advancement to the UN SDGs.
Design/Methodology/Approach: Cross-sectional survey research method was adopted; specifically, structured questionnaires were administered to 356 mid to senior level managers of various industrial organizations in Pakistan. Hypothesis testing was done by using Partial Least Squares Structural Equation Modeling (PLS-SEM) to determine the relations between the four variables, namely: AI, BDA, SCM, and sustainable manufacturing. The evaluation of such key drivers also involved proving the demographic and organizational factors such as Technology Maturity and Investment.
Findings: This study further shows that AI and BDA improve the supply chain performance and manufacturing sustainability. It can be pointed out that BDA has the strongest direct effect on environmental efficiency and waste saving. But there are certain factors that limit adoption, such as budget issues, lack of skilled IT people, and organizational culture that goes against adoption.
Implications/Originality/Value: This paper presents important implications for the policy makers as well as the business strategists. AI and BDA require investment in infrastructure and development of the workforce and the human ability to cope with the change that comes with the implementation of these Two technologies. Such efforts can enhance operational reliability, cost effectiveness and sustainability of the environment. This study fills such a gap in literature by providing empirical results from a developing economy setting. It is helpful to expand the existing information about Industry 4.0 technologies and offer practical recommendations for further sustainable development of digitalization in emerging economies.
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