Exploring current and future trends of predictive maintenance 4.0 in Tanzania: A systematic literature review

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Fred Peter
Beatus A. T. Kundi
Juliana Machuve


predictive maintenance, industry 4.0, Predictive maintenance 4.0 maturity, Manufacturing industries, factors for adoption, decision support tool


Manufacturing industry systems necessitates a good engineering strategy, improved maintenance and optimal operations that are necessary to keep the systems in its ideal condition. Industry 4.0 predictive maintenance (PdM 4.0) centers on the planning of maintenance tasks in accordance with the system real condition of health, targeting at providing an exact signal of when to do maintenance and whenever necessary. PdM 4.0 is employed by means of integration of several industry 4.0 pillars that incorporate models to attain diagnostics and prognostics activities. As far as industry 4.0 concern, Tanzania manufacturing industries (TMI) they hardly manage to fulfill all requirements for PdM 4.0. It is uncommon to find research works that have addressed on PdM 4.0 maturity levels, factors that influences its adoption and approaches or tools to overcome complexity of PdM 4.0 adoption by considering the advantages and disadvantages of each approaches and addressing the best of them. Decision support tools for PdM 4.0 adoption have not yet extensively addressed by previous review studies in manufacturing industries. Besides, the abundance of maintenance decision support tools but the ones for PdM 4.0 adoption remains unexplored; this provides opportunities for architecting PdM 4.0 in manufacturing systems. This systematic literature review aims at presenting the current and future trends of PdM 4.0 in TMIs through giving special attention to decision support tools for its adoption and summarizing the current and future trends for TMIs in transition

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