TECHNIQUES OF MACHINE LEARNING APPLIED TO REDUCE EMPLOYEE TURNOVER IN A COMPANY CLEANING AND DISINFECTION - Tesis de Ingeniero Industrial

Aug 5, 2026

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Employee turnover has increased in large Peruvian manufacturing industries in recent years. Although turnover can be a natural organizational effect, unwanted rotation raises training costs for new hires and negatively affects performance and workplace climate. The study aims to identify factors influencing operational staff turnover in a cleaning and disinfection manufacturing company and to predict these events through early-stage data analysis. Quantitative exploratory-explanatory methods use Orange and Python, training models such as Random Forest, Logistic Regression, Decision Tree, and SVM, evaluating performance via AUC and ROC curve metrics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Planteamiento del problema

Pregunta de investigación

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Objetivo general

Objetivos específicos

Justificación

Metodología de análisis de datos (Machine Learning)

Universidad de Lima Facultad de Ingeniería Carrera de Ingeniería Industrial TECHNIQUES OF MACHINE LEARNING APPLIED TO REDUCE EMPLOYEE TURNOVER IN A COMPANY CLEANING AND DISINFECTION Tesis para optar el Título Profesional de Ingeniero Industrial Erika Noemi Romero Rojas Código 20162488 Asesor Juan Carlos Quiroz Flores Lima – Perú Marzo de 2024 | Título
Techniques of Machine Learning applied to reduce employee turnover in a Company cleaning and disinfection | | --- | | Autor(es)
Erika Noemi Romero Rojas
[20162488@aloe.ulima.edu.pe](20162488@aloe.ulima.edu.pe)
Facultad de Ingeniería y Arquitectura, Universidad de Lima, Perú
Yvan Jesús López García
[ygarcia@ulima.edu.pe](ygarcia@ulima.edu.pe)
Facultad de Ingeniería y Arquitectura, Universidad de Lima, Perú
Juan Carlos Quiroz Flores
[j](jcquiroz@ulima.edu.pe)[cquiroz@ulima.edu.pe](jcquiroz@ulima.edu.pe)
Facultad de Ingeniería y Arquitectura, Universidad de Lima, Perú | | Resumen: La rotación de personal en las grandes industrias peruanas de manufactura ha venido incrementando en los últimos años. Si bien la rotación laboral es un efecto natural en las organizaciones, cuando es no deseada genera mayores costos de entrenamiento para el nuevo personal e impacta en el desempeño y clima laboral. Ante esta problemática nace la necesidad de poder identificar las posibles causas de rotación del personal operativo y predecirestos sucesos a través del análisis de datos en una etapa temprana para evitar y/o reducir su impacto en la compañía. El presente artículo de enfoque cuantitativo y alcance exploratorio–explicativo tiene como objetivo principal determinar los factores que influencian en la rotación de personal operativo de una empresa de manufactura del rubro de limpieza y desinfección a través de la recolección de datos empleando Machine Learning y fomentar propuestas que permitan dar soluciones ante la rotación de personal. Para el análisis de datos se empleó el software Orange, en donde los datos fueron entrenados con diferentes modelos de inteligencia como Random Forest, Logistic Regression, Decision Tree, y SCV, y Python para correr el modelo y obtener indicadores numéricos como el Áreabajo la curva (AUC) y el análisis de la curva ROC. El estudio propuesto muestra que los modelos tienen un buendesempeño en la clasificación, con altas tasas de precisión y recall, 96% y 97% respectivamente, así como una exactitud general del 96% .
Palabras Clave: Machine Learning – Rotación de personal – Manufactura – Recursos Humanos – Personal operativo
Abstract: Staff turnover in large Peruvian manufacturing industries has been increasing in recent years. While job rotation is a natural effect in organizations, it generates higher training costs for new staff and impacts work performance and climate when unwanted. Given this problem arises the need to identify the possible causes of rotation of operational personnel and predict these events through data analysis at an early stage to avoid and reduce its impact on the company. This article of quantitative approach and exploratory scope-explanatory aims to identify the propensity of rotation of the operation of a company manufacturing cleaning and disinfection through a model of forecast by collecting data using Machine Learning and encourage proposals that enable solutions to be found to the factors influencing staff turnover. MS Excel and Orange software were used for data analysis, where the data were trained with different intelligence models such as Random Forest, Logistic Regression, Decision Tree, and SVM, and Python to run the model and get numerical indicators like the Area under the curve (AUC) and the analysis of the ROC curve. The proposed study shows that the models perform well in classification, with high accuracy and recall rates, 96% and 97%, respectively, and an overall accuracy of 96% .
Keywords: Machine Learning – Staff turnover – Manufacturing – Human Resources – Operation

FAQs

Why is employee turnover a problem in Peruvian manufacturing companies?

Unwanted turnover increases training costs for new personnel and harms work performance and the workplace environment.

What is the main objective of the study?

Determine the factors that influence operational staff turnover in a cleaning and disinfection manufacturing company and propose solutions.

Which machine learning models and tools are used to analyze the data?

Data analysis uses Orange and Python, training models such as Random Forest, Logistic Regression, Decision Tree, and SVM, with evaluation using AUC and ROC curve indicators.

Aug 5, 2026

Upload by Hazel