Developing Adaptable and End-to-End Performance Measurement in Maintenance

Performance measurement is an essential part of maintenance management in the field of production and manufacturing. Existing literature shows that different angles on maintenance performance measurement frameworks and indicators have been researched, and various performance measures have been applied in maintenance operations in practice. However, there are still a number of challenges that undermine the decision-support capability of maintenance performance measurement, including the considerable number and variety of components in complex systems, limited historical maintenance data, misalignment between performance objectives and performance indicators, and lack of a holistic overview of process performance.

Within this context, this research aims to develop performance measurement for maintenance performance managers to make informed decisions and facilitate continuous improvement. In particular, the research focuses on the adaptability and endto- end coverage of performance measurement in maintenance. The adaptability of performance measurement is discussed from three perspectives: adapting and aligning existing performance measurement frameworks and indicators, adapting limited historical maintenance data, and adapting new methods implemented in maintenance processes. The end-to-end performance measurement is discussed regarding the complete maintenance process coverage from beginning to end and the comprehensive view of performance dimensions overseeing each process.

This paper-based thesis presents a series of methods and approaches to support adaptable and end-to-end performance measurement in maintenance through four academic papers. Paper A explores an interval-based grouping approach for periodic maintenance measurement in complex systems, with a focus on improving the identification process using limited historical maintenance data. Paper B extends the scope of the investigation and proposes an overarching maintenance performance diagnostic framework. The framework provides a structured approach to identify performance deficiencies, prioritize performance issues in an end-to-end process view, and derive performance indicators considering data availability. Building upon the framework, Paper C further develops a structured process mining approach to quantify process efficiency and compliance and investigates the automation potential of process performance measurement. Paper D examines the early-stage involvement of performance measurement in process configuration and evaluates its effect on continuous improvement. Empirical case studies were conducted to demonstrate the merit and validity of the approaches. By applying adaptable and end-to-end maintenance performance measurement, organizations can prioritize resources and efforts for implementation, increase management efficiency, and set sail toward continuous improvement.