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Autonomous maintenance (AM), one of the pillars of total productive maintenance (TPM), aims to achieve performance toward zero defects and zero breakdowns. AM system equipped with comprehensive lean tools provides continuous improvement during the AM activities. Despite its long duration, establishing a lean AM system with a robust guideline would provide significant benefits such as high quality and short lead time. Therefore, AM design approach should be provided in a holistic and detailed manner. This study aims to develop a framework for AM design, including preliminary, reactive, preventive and proactive steps using the axiomatic design (AD).

Requirements and technical parameters of the AM system are explored with AD. An extensive literature review and a real-life application are presented.

The proposed design was validated by adapting the proposed roadmap to a textile manufacturing system in Turkey. The application results justify the established AM system design with an average downtime improvement of 69.2% and the average elapsed time between two failures improvement of 65.1% for apparel department.

This study has the novelty of establishing an overall AM system design with all of its stages stepwise. It presents a comprehensive guideline in terms of integration of lean philosophy into AM design by generating maintenance-related use cases for lean tools. The developed approach facilitates creating and analyzing complex systems to improve maintenance implementations while reducing nonvalue-added operations.

The purpose of this paper is to support total productive maintenance implementers by providing a roadmap for autonomous maintenance (AM) preparation phase.

The authors use the axiomatic design (AD) methodology with lean philosophy as a paradigm.

This is an exploratory research to find the most important factors in AM preparation phase. A decoupled AD design ensures an effective usage of training within industry (TWI) and the introduction of standardized work (SW). TWI provides value in importance it assigns to leaders, with its “train the trainers” approach and in preparing a training program. Besides being an effective training method, TWI job instruction (TWI JI) provides needed information infrastructure to front load operators SW and equipment trainings.

Although AD, TWI and lean artifacts are generally field proven, the research is limited due to the lack of an industrial application.

In many real-life projects, companies do not know where to start and how to proceed, which leads to costly iterations. The proposed roadmap minimizes iterations and increases the chance of project success.

The authors apply AD for the first time to AM preparation phase despite it is used in the analysis of lean manufacturing. AD permits to structure holistically the most relevant lean manufacturing solutions to obtain a risk free roadmap. TWI has emerged as a training infrastructure; TWI JI-based operator SW training and the adaptation of JI structure to equipment training are original additions.