A data-driven framework for supporting the total productive maintenance strategy

Maintaining machinery efficiency is a challenge in modern manufacturing, with companies adopting Total Productive Maintenance (TPM) strategies and using Overall Equipment Effectiveness (OEE) as a key metric for operational improvements. Industry 4.0 features have provided advanced data processing tools to assess machine performance, but effectively leveraging this data to enhance OEE remains challenging due to their multisource nature. To fully leverage the vast amount of data, new analytical methodologies are essential, not only to identify production events negatively impacting OEE but also to enhance positive events, thereby supporting strategic decision-making for TPM implementation. This study addresses this need by introducing a new data-driven methodology that integrates lean TPM principles with I4.0 analytical tools. Specifically, the proposed framework provides company managers with a structured approach, employing a dual application of Association Rule Mining (ARM) and Network Analysis (NA) to discover hidden relationships within OEE factors—availability, performance, and quality—and between key factors and operational variables. By assessing the impacts on equipment effectiveness, this methodology aims to prevent negative domino effects from adverse events while promoting positive events. Finally, a data-driven decision support system provides a long-term roadmap for determining which TPM pillars and associated actions should be prioritized for improvement. A case study demonstrates the framework's effectiveness, leading to key outcomes: implementation of 24 kaizen actions, achievement of 7 out of the 8 TPM pillars, and a 14% improvement in OEE.