Process mining analysis for value added services
Tutor/a - Director/a
Deschamps, Aymeric
Estudiante
Martinez, Agustina
Tipo de documento
Projecte Final de Màster Oficial
Fecha
2024
rights
Acceso restringido por acuerdo de confidencialidad
Editorial
Universitat Politècnica de Catalunya
UPCommons
Resumen
The main objective of this thesis is to apply advanced process mining techniques to a real case study of an internal revenue recognition process for a type of service offered by HP Printing and Computing Solutions, S.L.U (which must remain confidential) to identify bottlenecks, teams that need to improve their performance to avoid delays either in revenue recognition, enabling service delivery in a timely manner, and improving forecasting regarding which quarter the delivery is planned for, thus facilitating financial recognition. All of this is achieved through the application of advanced process mining techniques using the Apromore analysis program and subsequently Python for automating the mentioned process. To achieve this, and based on the data shared by the company, a significant challenge exists in evaluating and acquiring data for process mining in a more efficient manner, observe patterns, trends, understand how different categories of services behave in these cases, and make better decisions when planning and executing them. Currently, this process is performed manually, which requires more time dedication as it involves merging different datasets from various systems into an Excel file, where exceptions are processed, and patterns are individually identified. This can also lead to manual errors in the process and require each verification to be manual. Therefore, through the development of this thesis, it will be possible not only to visualize the flow of the end-to-end process by analyzing the event logs but also to implement its automation, using Python as the main program, specifically the PM4PY library, while also ensuring the data's quality and appropriateness for process mining. This study's findings indicate that preprocessing techniques significantly affect process mining tasks'efficiency. Data cleaning needs vary based on event log characteristics, such as bulkiness, a broad range in trace set size, and activity duration changes. Hence, deploying automation could drastically cut down on work time. Moreover, as this involves a revenue recognition process, information can be accessible anytime within the month, not just at its conclusion. This facilitates the enactment of improvements or action plans by identifying opportunities and mapping out the flow.
Entitat col·laboradora
HP Printing and Computing
Profesorado participante
- Deschamps, Aymeric