Simulación y Optimización del Balanceo de una Línea de Producción de Envases Plásticos mediante Python y Metodologías Lean Six Sigma, 2025

Jorge Luis Delgado Batista; Jorge Luis Martinez Ramirez

doi:10.48204/reicit.v6n1.10118

Submitted June 6, 2026

Published 2026-07-06

Artículos

Vol. 6 No. 1 (2026): REICIT

Simulation and Optimization of Line Balancing in a Plastic Container Production Line Using Python and Lean Six Sigma Methodologies, 2025

Jorge Luis Delgado Batista⁺⁻
Jorge Luis Martinez Ramirez⁺⁻

DOI https://doi.org/10.48204/reicit.v6n1.10118

PDF (Español (España))

References

DOI: 10.48204/reicit.v6n1.10118

Published: 2026-07-06

How to Cite

Delgado Batista, J. L. and Martinez Ramirez, J. L. (2026) “Simulation and Optimization of Line Balancing in a Plastic Container Production Line Using Python and Lean Six Sigma Methodologies, 2025”, REICIT, 6(1), pp. 95–110. doi: 10.48204/reicit.v6n1.10118.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

The plastic packaging industry faces increasing demands in terms of productivity, process stability, and quality control, which require the implementation of continuous improvement methodologies supported by quantitative analysis and digital simulation tools. The objective of this research is to analyze the overall performance of a plastic container production line with a capacity of 1,200 units per 8-hour shift by simulating its operational behavior and evaluating key performance indicators (KPIs), in order to identify performance gaps relative to industry benchmark values and determine improvement opportunities to enhance productivity, process stability, and system efficiency. The methodology integrates simulation tools developed in Python using the Google Colab environment, combined with analytical approaches derived from Lean Manufacturing and Six Sigma methodologies. The simulation model allowed the evaluation of operational variables such as Takt Time, Cycle Time, line balancing efficiency, throughput, scrap rate, First Pass Yield (FPY), Process Capability Index (Cpk), physical availability, as well as maintenance reliability and labor efficiency indicators. Comparative scenarios were developed to analyze system performance against established industrial targets. The results reveal imbalances among workstations, bottleneck constraints, and variability in cycle times that negatively affect the overall efficiency of the production line. KPI analysis indicates deviations from benchmark values in metrics such as Overall Equipment Effectiveness (OEE), line balancing efficiency, and statistical process stability, confirming the presence of significant opportunities for operational optimization through workload redistribution, reduction of non-value-added activities, and improvements in equipment reliability. The study concludes that digital simulation combined with Lean Six Sigma approaches constitutes an effective tool for diagnosing the performance of production systems, supporting data-driven decision-making, and strengthening the competitiveness of the plastic packaging manufacturing industry in environments characterized by high demand and operational variability.

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