Interchangeability and cannibalization in industrial maintenance: perspectives for asset management with machine learning, big data and artificial intelligence
Results show that interchangeability reduces maintenance costs by 25–30% and cuts corrective interventions by up to 70% and cannibalization maintains operational availability above 90%.
The findings reveal that ML algorithms significantly improve fault detection, Remaining Useful Life (RUL) estimation, and maintenance decision-making, but challenges related to data quality, model interpretability, cybersecurity, and integration with legacy systems continue to affect implementation effectiveness.
P. Siva, Sankar Shunmuga, Sundaram et al.· Stanzaleaf International Jou...· 0 citations
An integrated framework uniting enterprise asset management, predictive analytics, and digital analytics for maintenance prioritisation and decision support is developed by developing an integrated framework uniting data preparation, feature engineering, modelling, evaluation, reliability translation, and decision inte...
Adeyemi Adebukunola Ishekwene· Journal of Engineering Resea...· 0 citations
It is demonstrated that artificial intelligence is a critical enabler of efficient, reliable, and proactive predictive maintenance in the automotive industry, with its greatest value perceived in reducing maintenance time and enhancing operational performance.
S. Aghamohammadi, Amir Abbas Shojaee, Ali Akbari et al.· Journal of Resource Manageme...· 0 citations
This study aims to improve maintenance reliability by strategically allocating the workforce, considering human factors and knowledge management in critical asset maintenance within the public transport sector.
The study proposes a quantitative-applied approach integrating multi-criteria decision-making (MCD...
Cristian García García, Mary Josefina Vergara Paredes, Javier Cárcel-Carrasco et al.· Journal of Quality in Mainte...· 0 citations
The study contributes an auditable AI-supported framework for explainable strategic financial planning in data-intensive organizational environments and identifies the need for future validation using anonymized multi-organizational datasets, externally audited protocols, or independently reproducible benchmarks.
Georgios Kampiotis, Georgios L. Thanasas, I. Zhyhlei et al.· Accounting and Financial Con...· 0 citations
The role of predictive maintenance in optimizing manufacturing processes is explored, focusing on how data analytics can be harnessed to streamline operations, improve workforce productivity, and reduce costs.
R. T· International Journal of App...· 0 citations
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