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A eficiência operacional nos centros de reparação automóvel é um fator crítico que influencia tanto
a satisfação do cliente, como a rentabilidade do negócio. Neste contexto, a presente dissertação
foca-se na criação de um algoritmo destinado a otimizar a alocação de técnicos de chapa e pintura
a viaturas a serem reparadas numa oficina de colisão.
O objetivo deste trabalho é desenvolver uma ferramenta dinâmica que possa facilitar a tomada de
decisão baseada num histórico de dados, promovendo assim uma gestão mais eficiente dos
recursos disponíveis, sendo humanos, temporais ou materiais. Através da análise de variáveis
baseada em critério como a dificuldade de reparação de chapa, a tipologia de cor, entre outros
fatores relevantes, o algoritmo proposto é capaz de sugerir a melhor correspondência entre
técnicos e tarefas, tendo em conta as competências de cada um.
Operational efficiency in automobile repair centers is a central factor that influences, not only customer satisfaction, but also, the profitability of the business. In this context, the present dissertation focuses on the creation of an algorithm aimed at optimizing the allocation of bodywork and painting technicians to vehicles. This work aims to develop a dynamic tool that can facilitate data-driven decision-making, besides promoting more efficient management of available resources, whether human, temporal, or material. Through the analysis of variables such as bodywork repair difficulty, and color typology, among other relevant factors, the proposed algorithm is capable of suggesting the best match between technicians and tasks, taking into account the specific competencies of each technician.
Operational efficiency in automobile repair centers is a central factor that influences, not only customer satisfaction, but also, the profitability of the business. In this context, the present dissertation focuses on the creation of an algorithm aimed at optimizing the allocation of bodywork and painting technicians to vehicles. This work aims to develop a dynamic tool that can facilitate data-driven decision-making, besides promoting more efficient management of available resources, whether human, temporal, or material. Through the analysis of variables such as bodywork repair difficulty, and color typology, among other relevant factors, the proposed algorithm is capable of suggesting the best match between technicians and tasks, taking into account the specific competencies of each technician.
Descrição
Palavras-chave
Algorithm Repair Efficiency Analysis Management