Proposal to Enhance Productivity by Applying Lean Manufacturing Tools in a Fish Canning Company

Authors

DOI:

https://doi.org/10.26439/ciii2025.8647

Keywords:

Fish processing industry, industrial production, simulation, SMED, TPM

Abstract

This project presents the design and partial implementation of a productivity improvement model based on Lean Manufacturing principles in a fish canning company. The approach involved a technical diagnosis and root cause analysis to identify key operational inefficiencies, primarily related to frequent equipment stoppages, prolonged changeover times, and a lack of process standardization. To address these issues, Total Productive Maintenance (TPM) and Single-Minute Exchange of Die (SMED) methodologies were applied, and their impact was assessed through simulation using Arena software. Results revealed significant productivity improvements. Labor productivity increased from 6 to 7 units/h, while annual defective output decreased from 372,370 to 86,310 cans. Changeover times were reduced from 23.8 to 8.1 min, and equipment failure rates declined from 13.67% to 4.59%. These improvements contributed to a higher level of Overall Equipment Effectiveness (OEE). Additionally, the project enhanced operator skills, increased process stability, and strengthened operational discipline. The study demonstrates that Lean Manufacturing tools can be successfully adapted to the realities of small-scale fish processing operations, offering a viable pathway to boost competitiveness, reduce waste, and meet the demands of increasingly rigorous markets.

 

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Author Biographies

  • Flavia Espinoza-Montero, Carrera de Ingeniería Industrial, Universidad de Lima, Perú

    Flavia Espinoza-Montero holds a Bachelor’s degree in Industrial Engineering from Universidad de Lima. She currently works as a Key Account Executive in the Sales Department at Beiersdorf, where she manages strategic accounts and monitors key performance indicators (KPIs). She has experience in commercial strategy, data-driven decision-making, and cross-functional collaboration. Her main areas of interest include sales management, business analytics, process optimization, and continuous improvement.

  • Rodrigo R. Quintanilla-Chalan, Carrera de Ingeniería Industrial, Universidad de Lima, Perú

    Rodrigo Rafael Quintanilla-Chalan is an Industrial Engineering student at Universidad de Lima. He currently works as a Sales Intern in the retail sector, where he is involved in activities related to commercial analysis and management. His areas of interest include operations management, process improvement, industrial productivity, and commercial management.

  • 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.

  • 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.

References

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Published

2026-06-08

Issue

No. 004 (2025): IV Congreso Internacional de Ingeniería Industrial

Section

Ponencias

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