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Optimization of OEE in a Peruvian Textile Spinning Plant: An Empirical Study Based on the Integration of TPM and 5S (#2063)

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Date of Conference

July 15-17, 2026

Published In

"Engineering without Borders: Artificial Intelligence, Knowledge, Innovation, and Alliances for a Future from the Americas"

Location of Conference

Santiago (Chile)

Authors

Alva-Ramírez, Joseph Cristopher

Arenas-Berrocal, Nayeli Patricia

Chavez-Ugaz, Rafael

Quiroz-Flores, Juan Carlos

Abstract

The Peruvian textile sector faces increasing pressure to improve its operational efficiency due to its high dependence on machinery, the inherent variability of the spinning process, and recurrent failures that interrupt production continuity. These limitations reduce the industry’s responsiveness to demand and hinder compliance with international efficiency standards. Although operational stability is critical in the spinning process, previous studies addressing these issues through TPM and 5S are still scarce or present only partial applications, highlighting the need for a more technical and structured approach. The spinning mill analyzed showed an OEE between 63.4% and 64.1%, well below the international benchmark (>80%), revealing a critical performance gap associated with mechanical and electrical failures, fluctuations in spindle flow, and a lack of standardization. The study developed an integrated Lean-based model grounded in TPM, 5S, and standardized work, with the aim of increasing equipment reliability and reducing operational variability. Validation was carried out through a hybrid strategy combining a pilot test with scenario comparison and discrete-event simulation in Arena. The results showed significant improvements: OEE increased to a range of 83.79%, achieving a gain of 20.4 percentage points, along with reductions in mechanical failures, unproductive time, and operational variability. The proposed model provides practical guidelines and a structured, replicable framework for other spinning mills seeking to improve their performance through accessible, high-impact methodologies. The study encourages future research integrating flow analysis, advanced simulation, or predictive maintenance to further strengthen the competitiveness of the textile sector.

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