Productividad en transporte de muestras de Laboratorio Bueso Arias: un enfoque de investigación de operaciones.
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Date
2026-02-01
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Publisher
Universidad Tecnológica Centroamericana UNITEC
Abstract
El presente estudio tuvo como propósito evaluar y optimizar la productividad del sistema de transporte de muestras clínicas del Laboratorio Bueso Arias, analizando el efecto de variables internas, tipo de vehículo, competencia del conductor y tiempo de servicio, y externas como condiciones ambientales, horarios y distancia recorrida. El estudio empleó herramientas de Investigación de Operaciones y análisis estadístico, comparando rutas reales con escenarios simulados mediante el Método General de Transporte, el Problema del Viajante (TSP), ANOVA y Tukey-Kramer. Los resultados evidenciaron que el tipo de vehículo, la competencia del conductor, el tiempo de servicio y las condiciones ambientales influyen significativamente en la productividad, mientras que el tiempo de viaje y los horarios no mostraron efectos relevantes, manteniéndose la eficiencia general por encima del 90 %. A partir de estos hallazgos se identificó que la principal oportunidad de mejora radica en reducir tiempos improductivos y estandarizar actividades administrativas. Se formularon cuatro alternativas orientadas a maximizar el desempeño operativo, sub contratación de taxis, contratación de un mecánico de planta, uso de contenedores térmicos automatizados y digitalización del sistema logístico, todas evaluadas mediante indicadores clave y análisis beneficio–costo, con valores B/C mayores a 1. Finalmente, se diseñó una propuesta integral de implementación que articula mantenimiento preventivo, fortalecimiento de competencias, control térmico y un sistema digital de gestión, orientada a mejorar la trazabilidad, elevar la eficiencia global y asegurar la sostenibilidad operativa del transporte de muestras clínicas.
The purpose of this study was to evaluate and optimize the productivity of the clinical sample transportation system at Laboratorio Bueso Arias by analyzing the effect of internal variables vehicle type, driver competence, and service time and external variables such as environmental conditions, schedules, and distance traveled. The study employed Operations Research tools and statistical analysis, comparing real routes with simulated scenarios using the General Transportation Method, the Traveling Salesman Problem (TSP), ANOVA, and Tukey–Kramer tests. The results showed that vehicle type, driver competence, service time, and environmental conditions significantly influence productivity, while travel time and schedules did not present relevant effects, with overall efficiency remaining above 90%. Based on these findings, the main improvement opportunity was identified in reducing non-productive time and standardizing administrative activities. Four alternatives were formulated to maximize operational performance —taxi subcontracting, hiring an in-house mechanic, implementing automated thermal containers, and digitalizing the logistics system—all evaluated through key performance indicators and benefit–cost analysis, obtaining B/C values greater than 1. Finally, an integrated implementation proposal was developed, combining preventive maintenance, skills enhancement, thermal control, and a digital management system, aimed at improving traceability, increasing overall efficiency, and ensuring the operational sustainability of the clinical sample transportation process.
The purpose of this study was to evaluate and optimize the productivity of the clinical sample transportation system at Laboratorio Bueso Arias by analyzing the effect of internal variables vehicle type, driver competence, and service time and external variables such as environmental conditions, schedules, and distance traveled. The study employed Operations Research tools and statistical analysis, comparing real routes with simulated scenarios using the General Transportation Method, the Traveling Salesman Problem (TSP), ANOVA, and Tukey–Kramer tests. The results showed that vehicle type, driver competence, service time, and environmental conditions significantly influence productivity, while travel time and schedules did not present relevant effects, with overall efficiency remaining above 90%. Based on these findings, the main improvement opportunity was identified in reducing non-productive time and standardizing administrative activities. Four alternatives were formulated to maximize operational performance —taxi subcontracting, hiring an in-house mechanic, implementing automated thermal containers, and digitalizing the logistics system—all evaluated through key performance indicators and benefit–cost analysis, obtaining B/C values greater than 1. Finally, an integrated implementation proposal was developed, combining preventive maintenance, skills enhancement, thermal control, and a digital management system, aimed at improving traceability, increasing overall efficiency, and ensuring the operational sustainability of the clinical sample transportation process.
Keywords
Productividad, Logística, Optimización, Análisis Operativo