Towards A Methodological Framework For Early Qualitative Assessment Of The Ecological And Economic Costs Of Digital Twins In Industrial Maintenance

Over the last decade, the use of digital twins (DTs) has expanded significantly across a variety of sectors.They often take various forms, with an emphasis on the underlying technologies (IoT, Cloud Computing, AI, virtual reality, etc.).However, one question remains: once deployed, will these digital twins be utilized in a manner that is both cost-effective and sustainable?Digital twins originated in maintenance applications, which is where these technologies are currently the most mature.Previous work shows that in the literature, most articles concern predictive maintenance applications, which implies the frequent use of artificial intelligence and therefore the management of large volumes of data.However, in recent years, we have seen the emergence of virtual reality technologies for training and augmented reality for intervention assistance, which require both significant hardware and software resources.The aim of this article is to propose a qualitative methodology for classifying industrial maintenance digital twins.This methodology will enable the assessment of the economic and environmental costs of DTs, making it possible for decision makers to ask the right questions even at the earliest stages of the DT's conception.This is especially helpful since, at those early stages, designers often lack quantitative information.This approach offers a more accessible starting point for eco-design, unlike more quantitative methods, such as Life Cycle Assessment (LCA), which require higher precision in data collection and a significant amount of time.This qualitative methodology will be applied to recent literature, providing a preliminary analysis of the digital twins currently in operation.