The Extension of Total Productive Maintenance with Digital Intelligence for Data-Driven Maintenance in Smart Manufacturing

Somto Kenneth Onukwuli¹, Charles Chikwendu Okpala² and Paul Chika Okpala³

¹ Researcher, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.

² Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.

³ Lecturer, Mechanical Engineering Department, Madonna University, Akpugo, Nigeria.  

Abstract

The increasing digitalization of manufacturing systems presents new opportunities to rethink maintenance as a strategic enabler of both operational excellence and sustainability. While Total Productive Maintenance (TPM) remains a widely adopted maintenance philosophy, its traditional implementation relies on static schedules and experience-based decision-making, which limits its effectiveness in data-intensive smart manufacturing environments. This study proposes a Digital Intelligence–Enabled Total Productive Maintenance (DI-TPM) framework that systematically integrates Industrial Internet of Things (IIoT) data, advanced analytics, and sustainability-aware decision models into the core pillars of TPM. The proposed framework is supported by a scalable data architecture that consolidates multisource operational, condition, energy, and maintenance data to enable predictive and prescriptive maintenance strategies. Methodological innovations include machine learning–based degradation modeling and multi-objective maintenance optimization that explicitly incorporates energy intensity, material usage, and emissions as decision variables. Empirical validation through multidisciplinary case studies in discrete and process manufacturing demonstrates that DI-TPM reduces unplanned downtime by up to 39%, lowers energy intensity by 10-18%, decreases material waste by approximately 30%, and achieves maintenance-related emissions reductions of up to 18% without compromising production output or equipment availability. Through the preservation of TPM’s human-centric philosophy while augmenting it with transparent digital intelligence, the proposed approach enhances decision quality, organizational learning, and sustainability performance. The findings position data-driven maintenance as a critical pathway for the alignment of smart manufacturing initiatives with environmental objectives, and also provide robust evidence to support the adoption of maintenance-centric sustainability strategies across industrial sectors.

Keywords: Data-driven maintenance, Total productive maintenance, Smart manufacturing, Predictive maintenance, Sustainability metrics, Industrial internet of things (IIoT), Energy efficiency

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