ALFA DT: a digital twin for assembly line fitness analytics

This paper presents ALFA DT, a Digital Twin (DT) designed for Assembly Line Fitness Analytics (ALFA) to support descriptive, predictive, and prescriptive analytics for assembly line performance. The paper details the digitalisation of the assembly line process, focussing on generating digital models of operator walk patterns and creating task precedence constraints. Two algorithms are proposed, one called Walk Pattern (WP) for the digitalisation of operator walk patterns in the line and another called BiDirectional Precedence Miner (BDPM) algorithm for the precedence graph generation considering quick and frequent product model evolution, which are two of main challenges in current industries. The implementation of ALFA DT is based on the UNITY game engine, leveraged for its visualisation and animation capabilities, multi-platform deployment, and collaborative environment. The proposed DT provides an efficient tool for analyzing, forecasting, and optimising assembly line performance. ALFA DT integrates real-time data and learns from aggregated system behaviour to support predictive insights and proactive decision-making. By serving as a single source of relevant data with an interactive visual interface, it facilitates advanced analytics applications, allowing users to easily validate and modify proposed solutions. ALFA DT has been applied on our real automotive assembly setting and the findings indicate that it improves operational decision-making by improving data accessibility and visualisation quality. The paper discusses several use cases of ALFA DT, including operator work pattern analysis, workcells' layout optimisation, simultaneous assembly line balancing and layout adjustment, and predicting operator overload.