Advancing Sustainable Manufacturing through AI, IIoT, and Digital Twins: A Systematic Review of Emerging Technologies

Abstract

This systematic literature review investigates the possibilities of artificial intelligence, the Industrial Internet of Things, and digital twins for promoting sustainable manufacturing by utilizing the interrelated digital capabilities, operational mechanisms, and socio-technical conditions. Based on the 59 studies included in the predefined review window, and using the PRISMA approach, this review identifies, screens, appraises, codes, and synthesizes the studies. The results show that the IIoT provides the functions of constant sensing and connectivity, and that artificial intelligence transforms industrial data into the realm of the predictable and optimal. Finally, digital twins aid in the modeling, contextualization, simulation, and reasoning of a system’s lifecycle. The collaborative usage of these technologies improves the dimensions of sustainable manufacturing such as predictive maintenance, process optimization, quality control, and the efficient use of resources with an emphasis on waste and circularity. The gain of these improvements is conditional on a plethora of factors. Most notably, data governance, interoperability, and cybersecurity, as well as the validity of models and the capability of the workforce. The literature predominantly focuses on operational gains. Therefore, this review synthesizes an integrated taxonomy of capabilities, mechanisms, outcomes, and enabling conditions and suggests longitudinal, multi-site, lifecycle-focused studies with sustainability indicators along with sustainable technology configurations in diverse manufacturing contexts. It also identifies gaps in the existing literature by clarifying the state of existing evidence.