Data-Driven Predictive Maintenance for Industries

Sammanfattning: Due to the industry 4.0 paradigm shift, manufacturing and pro-duction industries are focusing on Predictive maintenance (PdM) to increase production efficiency and maintenance strategies. Also, adopting data-driven approaches to implement PdM as a result of increased data usage and exchange. However, facing technical and non-technical challenges. Previous studies majorly concentrated on technological aspects concerning the implementation of predictive maintenance, providing limited insight into organizational and environmental aspects. Considering this knowledge gap, this study focuses on examining and de-scribing factors impacting data-driven PdM implementation in various aspects. To answer the research question, the TOE framework was adopted. We conducted qualitative research and collected data by carrying five semi-structured interviews with data-driven PdM practitioners. Research findings show that the following factors: Data Management and Collaboration & Communication are the most influential, other factors: IT-Infrastructure, Organization Size & Type of Industry, Existing Knowledge, Awareness & Educational Efforts, Cost, Top Management Support, Model Selection, and Competitive Pressure has certain or least influence while implementing PdM. The influence of External Support is considered inconclusive.