Artificial Intelligence in Remanufacturing Contexts: Current Status and Future Opportunities

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), rapidly evolving in both academia and practice, allow for improved manufacturing processes thanks to data analysis. These technologies provide benefits to production systems in several ways by enabling resilience and improving sustainable growth. However, the manufacturing challenges and issues need to be revised according to the new trends provided by remanufacturing, i.e., an emerging and new “mode” of manufacturing able to bring used products to a “like-new” state, potentially profitable and less environmentally harmful compared to the classical manufacturing systems. This research work, methodologically based on a scoping literature review, provides the state-of-the-art related to AI, ML, and DL use in remanufacturing contexts, identifying the main field of applications and their challenges and limitations. The findings revealed an increasing interest in the topic in the last three years. Most of the studies focused on disassembly and inspection processes, whereas further applications (e.g., repair, demand forecasting, cost prediction, etc.) have not been fully investigated and need further research. DL represents the most widely used technique (followed by ML). Even though the literature confirmed that AI-based methods could increase productivity and lower time and costs, further attention needs to be paid to real industrial case study applications.