Smart Manufacturing: Lean-TPM 4.0 Model with IoT and AI to Improve Innovation and Efficiency in the Refractory Industry

Ambicho-Ponce H.; Arotinco-Quihui R.; Saenz-Moron M.; Andersone I. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2026) · DOI: 10.18687/laccei2026.1.1.1458

Resumen

Low efficiency in refractory production is a critical problem affecting the profitability and sustainability of manufacturing processes. This study proposes an innovative model integrating Total Productive Maintenance (TPM 4.0) and Lean Manufacturing tools, supported by Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The research follows an applied research typology with a pre-experimental design, utilizing non-probabilistic purposive sampling focused on the critical equipment of a refractory crucible manufacturer. The methodology evaluates the "before-and-after" impact of integrating automated data collection and predictive analytics. The model aims to increase operational efficiency, reduce equipment downtime, and mitigate high rework rates. After implementation, significant improvements were observed: the porous crucible rate was reduced by 57.69% (from 18.20% to 7.70%); the Mean Time Between Failures (MTBF) of the press increased by 25.33% (from 173.06 h to 216.91 h), while the MTBF of the mixer increased by 50.44%. Likewise, the internal setup time through Single Minute Exchange of Die (SMED) decreased by 27.58%, from 83.60 h to 60.54 h. Finally, the results demonstrate that the model is scalable and flexible, providing a practical framework for other industrial environments seeking to transition toward Smart Manufacturing. These findings validate the integration of Lean-TPM 4.0 as a robust solution for enhancing global efficiency in traditional industries.

Instituciones: Universidad Peruana de Ciencias Aplicadas