Propuesta de procedimiento para optimizar la gestión de recursos mediante un estudio y análisis de costos en el área de mantenimiento.
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Date
2025
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Universidad de Concepción
Abstract
La presente memoria tiene como objetivo proponer un procedimiento que permita optimizar la gestión de recursos en el área de mantenimiento, a partir del análisis de los costos asociados a las órdenes de trabajo registradas en SAP. El estudio se enmarca en la necesidad de transformar datos operativos en información útil para la toma de decisiones, abordando problemáticas como la baja estandarización de los registros, el mal manejo de esta información y la limitada trazabilidad de costos.
Se utilizó una base de datos histórica correspondiente a órdenes de trabajo cerradas, abarcando un período de dos años excluyendo las paradas generales de planta (PGP). Para su tratamiento se emplearon herramientas de Business Intelligence (Python, Excel y Power BI), aplicando técnicas de análisis exploratorio en distintas dimensiones, agrupamiento de los registros mediante K-Means y principios como el análisis de Pareto para identificar concentraciones de costos y desviaciones.
Los resultados evidenciaron una alta concentración de costos en un conjunto reducido de equipos. Además, se identificaron problemas en la calidad de los registros, como descripciones incompletas o mal ingresadas, que dificultan el análisis técnico. También se detectaron desviaciones entre los valores contables reportados y los registrados en SAP, junto con falencias en la gestión de inventario de stock crítico.
Finalmente, a partir de estos hallazgos se propone un procedimiento estructurado en cinco etapas, orientado a formalizar un proceso que promueva la mejora continua, impulse una mejor calidad en el registro de información y sustente decisiones estratégicas. Su aplicación busca fortalecer el análisis recurrente de datos y proporcionar una base técnica replicable para mejorar la eficiencia en la gestión del mantenimiento.
This thesis aims to propose a procedure to optimize resource management in the maintenance area, based on the analysis of costs associated with work orders recorded in SAP. The study is framed by the need to transform operational data into useful information for decision-making, addressing issues such as low standardization of records, poor data handling, and limited cost traceability. A historical database of closed work orders was used, covering a two-year period and excluding general plant shutdowns (PGP). For data processing, Business Intelligence tools were employed (Python, Excel, and Power BI), applying exploratory data analysis techniques across multiple dimensions, clustering through K-Means, and incorporating principles such as Pareto analysis to identify cost concentrations and significant deviations. The results revealed a high concentration of costs in a small number of critical equipment. In addition, problems were identified in the quality of records, such as incomplete or poorly entered descriptions, which hinder technical analysis. Discrepancies were also found between the reported accounting values and those recorded in SAP, along with shortcomings in the management of critical stock inventory. Based on these findings, a structured five-stage procedure is proposed to formalize a process that promotes continuous improvement, enhances the quality of recorded information, and supports strategic decision-making. Its implementation seeks to strengthen recurrent data analysis and provide a replicable technical basis to improve efficiency in maintenance management.
This thesis aims to propose a procedure to optimize resource management in the maintenance area, based on the analysis of costs associated with work orders recorded in SAP. The study is framed by the need to transform operational data into useful information for decision-making, addressing issues such as low standardization of records, poor data handling, and limited cost traceability. A historical database of closed work orders was used, covering a two-year period and excluding general plant shutdowns (PGP). For data processing, Business Intelligence tools were employed (Python, Excel, and Power BI), applying exploratory data analysis techniques across multiple dimensions, clustering through K-Means, and incorporating principles such as Pareto analysis to identify cost concentrations and significant deviations. The results revealed a high concentration of costs in a small number of critical equipment. In addition, problems were identified in the quality of records, such as incomplete or poorly entered descriptions, which hinder technical analysis. Discrepancies were also found between the reported accounting values and those recorded in SAP, along with shortcomings in the management of critical stock inventory. Based on these findings, a structured five-stage procedure is proposed to formalize a process that promotes continuous improvement, enhances the quality of recorded information, and supports strategic decision-making. Its implementation seeks to strengthen recurrent data analysis and provide a replicable technical basis to improve efficiency in maintenance management.
Description
Tesis presentada para optar al título de Ingeniero/a Civil Industrial.
Keywords
Análisis de costos, Procesamiento de datos, Mantenimiento