Bibliographic citations
Barra, O., Tataje, A. (2022). Modelo de aprendizaje supervisado para la derivación automatizada de tickets de service desk [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/661413
Barra, O., Tataje, A. Modelo de aprendizaje supervisado para la derivación automatizada de tickets de service desk [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2022. http://hdl.handle.net/10757/661413
@misc{renati/400697,
title = "Modelo de aprendizaje supervisado para la derivación automatizada de tickets de service desk",
author = "Tataje Garcia, Alexa Grissel",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2022"
}
The purpose of this project is to design a supervised learning model for the automatic derivation of tickets from the help desk of a mass consumption company. Which is born before the problem due to the delay in the attention of the tickets (requests and incidents) due to the derivation of the resolution groups. Given that it is difficult to know all the functions of the problem-solving groups and the high turnover of help desk analysts, which also results in user dissatisfaction. For the realization of this project, we investigated about Machine Learning solutions applied to the attention of help desk tickets, we were able to identify the main challenges and causes that prevent the implementation of this type of solution within organizations. The proposed model seeks to improve the process of derivation of help desk tickets using Machine Learning tools that allow automatically assign the resolution groups of the suppliers that support the mass consumption company. For the validation we used the tickets records provided by the company and executed tests that allowed us to validate the model. The records obtained were used to train the Machine Learning model, which provided us with the necessary information to classify and assign the tickets to the problem-solving groups according to their type: incident and request. Additionally, when analyzing the results obtained from the validation, the capacity of the model in reducing the assignment time of the problem-solving groups in comparison with the current situation of the help desk was evidenced.
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