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Application of TPM and Lean Manufacturing tools to increase OEE. An empirical investigation in the Peruvian textile industry

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Date
2023
Author(s)
Ávila Pisco, Henry Alexander
Chiok Gonzales, Diego Armando
Flores Pérez, Alberto Enrique
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Abstract
In the manufacturing industry, the OEE indicator is used to measure the efficiency of the equipment within an operating process, which is why a high OEE is necessary to remain competitive in the industry, especially in the textile manufacturing industry. In this sense, information was collected from the production process of a company belonging to the textile industry, resulting in 78,01% of OEE compared to 85,00% of the world average. This is due to the poor implementation of maintenance methodologies and tools, generating unplanned machine stoppages. Focusing on the implementation of the TPM methodology in its preventive and autonomous variants, as well as the implementation of the Poka-Yoke tool belonging to the Lean Manufacturing methodology, a high number of downtime hours due to machine-related failures was identified as the main problem within the production process in this textile manufacturing company in Peru. The study was validated through simulations in Arena 16.1 software, where the implementation of these methodologies could mean a gain close to PEN 250.000,00 per year and a reduction of downtime of up to 22,85%, increasing the company’s OEE by 7,83%.
URI
https://hdl.handle.net/20.500.12724/19923
DOI
https://doi.org/10.1109/CONIITI61170.2023.10324213
How to cite
Ávila Pisco, H., Chiok Gonzales, D. A., & Flores Pérez, A. E. (2023). Application of TPM and Lean Manufacturing tools to increase OEE. An empirical investigation in the Peruvian textile industry. Congreso Internacional de Innovación y Tendencias en Ingeniería Facultad de Ingeniería, Universidad de Lima. https://doi.org/10.1109/CONIITI61170.2023.10324213
Publisher
IEEE
Subject
Pendiente
Event
9th International Conference on Innovation and Trends in Engineering, CONIITI 2023
Collections
  • Ingeniería Industrial [182]


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