Implications of Analytics and Visualization of Torque Tightening Process Data on Decision Making : An automotive perspective

Sammanfattning: In recent years, there is an increased focus on integrating digital technologies in industrial processes, also termed ”Industry 4.0”. Out of the many challenges for the transition, one is to understand how to find useful insights from data collected over large periods of time, predominantly in industrial IT systems. Automotive assembly plant X is currently undergoing a digital transformation to leverage such technologies. There is an emphasis to understand the implications of data analytics and visualization and how it could be leveraged for process optimization. The torque tightening assembly process at plant X was chosen to carry out the study as there were opportunities to access the process data from the tool management system database. The purpose of this master thesis was thus to find the implications of data analytics on the torque tightening operations in assembly plant X. In addition, the thesis also aimed to understand how visualization of key performance indicators (KPIs) can improve traceability of operational deviations. In other words, the study aims to validate how data analytics and visualization of KPIs facilitate data-driven decision making, improve traceability of operational deviations. The research is based on an inductive, exploratory case study approach. The study was carried out by understanding the current state through a series of interviews and then followed by the development of the framework and dashboard for visualization of operational deviations. Further, a discussion on how data analytics and visualization could help in decision-making for continuous improvement efforts is presented.