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Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 24th Aug 2026, 05:30:40am America, Santiago

Daily Overview

Session

33D: Engineering Education
Virtual location: VIRTUAL: Agora Meetings

Presentation 3

11:56am - 12:04pm

Intelligent Manufacturing Model Based on Standard Work, TPM and SMED with IoT under Industry 4.0 to Improve Productive Efficiency .

Calogello Sanchez-GonzalesVigil¹, Xiomara Gonzales-Portocarrero², Víctor Aparicio Lora³

1: Universidad Peruana de Ciencias Aplicadas - (PE), Perú; 2: Universidad Peruana de Ciencias Aplicadas - (PE); 3: Universidad Peruana de Ciencias Aplicadas - (PE)

Resumen— In the textile sector, productive efficiency is a key factor for the competitiveness and sustainability of companies. In this context, the present study was carried out in a Peruvian textile company dedicated to garment manufacturing, where a low level of productive efficiency was identified, reaching 31.52%, compared to the sector’s average of 50.3%, which reveals a technical gap of 18.78%. This problem arises mainly from the lack of process standardization, the high variability of production times, and long changeover times between product references. To address this situation, an intelligent manufacturing model is proposed that integrates the tools Standard Work, TPM and SMED, complemented with IoT technologies within the framework of Industry 4.0, with the purpose of optimizing the efficiency of the garment manufacturing process. Based on the application of these methodologies, it is expected to achieve a reduction of more than 40% in non-productive times, a reduction of at least 35% in set-up times, and an increase in the overall efficiency of the process above 50%, reaching industry benchmarks. These results will help close the identified technical gap, improve productivity and process traceability, and contribute to the digital transformation of the Peruvian textile industry.