Improvement Proposal to Increase On Time Delivery in a Textile SME through the Implementation of Poka Yoke, SMED, and Standard Work

Autores/as

DOI:

https://doi.org/10.26439/ciii2025.8649

Palabras clave:

Lean Manufacturing, On-Time Delivery, Poka-Yoke, SMED, SME textile, Standard Work

Resumen

This study presents a process improvement project in a Peruvian textile small and medium-sized enterprise (SME) specialized in knitted garments. The company operates with an On-Time Delivery (OTD) rate of 53.05%, which is significantly below the industry benchmark of 77%. This low performance is exacerbated by excessive setup times and high defect rates, which negatively affect production efficiency and process reliability. To address these challenges, a tailored Lean model was developed by integrating Single-Minute Exchange of Die (SMED), Poka-Yoke, and Standard Work, adapted to the company’s semi-manual operations. The main contribution of this study is to demonstrate that the combined and contextualized application of these tools can effectively reduce performance gaps in resource-constrained manufacturing environments. After simulation and pilot testing, the model achieved a 24.95-point increase in OTD, reaching 78%, along with reductions in setup time and operational variability.

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Biografía del autor/a

  • Paul M. Yauri-Artica, Carrera de Ingeniería Industrial, Universidad de Lima, Perú

    Paul Martin Yauri-Artica holds a Bachelor’s degree in Industrial Engineering from Universidad de Lima, Peru, with a specialization in Business Engineering. He currently works as a Business Specialist at Pacífico Seguros, where he is part of the business division and manages mass-market products across bancassurance and strategic alliance channels. He works with strategic partners including Peru’s leading bank, Latin America’s leading e-commerce platform, and the country’s most widely used digital wallet, contributing to the development, monitoring, and optimization of products aimed at protecting millions of Peruvians and generating business profitability. He has experience in digital transformation and digital growth, with a focus on optimizing time and resources, enhancing the digital customer experience, and driving the digitalization of processes and sales, as well as in the leadership and management of strategic initiatives in corporate environments. His areas of interest include business management, process improvement, digital transformation, and resource optimization.

  • Nicol C. Melgar-Barrios, Carrera de Ingeniería Industrial, Universidad de Lima, Perú

    Nicol Cristina Melgar-Barrios holds a Bachelor’s degree in Industrial Engineering from Universidad de Lima, Peru. She currently works as a Business Specialist at Banco de Crédito del Perú (BCP), where she supports the deployment of commercial campaigns across sales channels and conducts market share and data analysis for the Cuenta Sueldo product, as well as projects targeting corporate and individual clients to support business decision-making. She has professional experience in administrative management, customer communication, quotation handling, and payment control, which has strengthened her understanding of operational and service-oriented processes. Her academic and professional interests focus on process improvement, lean manufacturing tools, operational efficiency, and data analysis applied to business contexts.

  • Gino Viacava-Campos, Carrera de Ingeniería Industrial, Universidad de Lima, Perú

    Gino Viacava-Campos holds a Master’s degree in Operations and Logistics from Universidad Peruana de Ciencias Aplicadas, Peru, and a degree in Industrial Engineering from Universidad de Lima, Peru. He is currently a professor in the Industrial Engineering Program at Universidad de Lima. He has experience in industrial and service companies, focusing on productivity improvement, business model development,
    and compensation system structuring. He has worked in human resources, operations, and logistics areas of a transnational company, both locally and overseas. As a consultant, he has experience in the construction, manufacturing, services, mining, commercial, finance, and education sectors. He has co-authored papers including “A Production Process Efficiency Improvement Model at a MSME Peruvian Metalworking Company,” published in the Proceedings of the 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). His main research interests are lean manufacturing, master production schedules, logistics, and compensation systems for small and medium-
    sized enterprises.

  • Jorge A. Corzo-Chavez, Carrera de Ingeniería Industrial, Universidad de Lima, Perú

    Jorge Antonio Corzo-Chavez holds a Master’s degree in Business Administration from Universidad ESAN, Peru; a Master’s degree in Marketing Science from ESIC Business & Marketing School, Spain; and a degree in Industrial Engineering from Universidad de Lima, Peru. He is currently a professor in the Industrial Engineering Program at Universidad de Lima and Chief Executive Officer at Conquista Lab. He has advised industrial and service companies on productivity improvement and business model development, and has worked in entrepreneurship, innovation, and commercial roles in the construction, mining, services, and education sectors. He has co-authored papers including “Improvement of Productivity by Applying 5S, Work Standardization, Ergonomic Analysis and Poka Yoke in a Metalworking Company,” published in the Proceedings of the 23rd LACCEI International Multi-Conference for Engineering, Education, and Technology; “Production Model Based on Lean Manufacturing and Systematic Layout Planning to Reduce Waste in a Company in the Poultry Sector: A Case Study,” published in the International Journal of Environmental Pollution and Remediation, volume 12; and “Model for Reducing Mean Absolute Percentage Error through Smoothing and Time Series Forecasting in a Tourism SME: A Case Study,” published in the Journal of Machine Intelligence and Data Science, volume 5. His main research interests are lean manufacturing, logistics, and machine learning for enterprises.

Referencias

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Publicado

2026-06-08

Número

Núm. 004 (2025): IV Congreso Internacional de Ingeniería Industrial

Sección

Ponencias

Licencia

Derechos de autor 2026 Congreso Internacional de Ingeniería Industrial de la Universidad de Lima

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.