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Business Process Modelling to Improve Incident Management Process

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Trends and Innovations in Information Systems and Technologies (WorldCIST 2020)

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Abstract

Business process management (BPM) is an approach focused on the continuous improvement of business processes, providing for this a collection of best practices. These best practices enable the redesign of business processes to meet the desired performance. IT service management (ITSM) defines the management of IT operations as a service. There are several ITSM frameworks available, consisting in best practices that propose standardizing these processes for the respective operations. By adopting these frameworks, organisations can align IT with their business objectives. The constant need for change, competitiveness and innovation in organisations compels managers to analyse its business processes and find improvement opportunities. Therefore, the objective of this research is to understand how BPM can be used to improve of ITSM processes. An exploratory case study in a multinational company based in Lisbon, Portugal, is conducted for the improvement of the time performance of an incident management process. Data were gained through documentation, archival records, interviews and focus groups with a team involved in IT support service. The results of this study demonstrate how BPM highlighted the current incident management process incongruences. The preliminary evidences of this research indicate that with the application of the remain BPM lifecycle phases may be possible to improve incident management process. During the next months the authors intend to identify the main problems and simulate the appropriate BPM heuristics to understand the impact in the business organisation.

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Pereira, R., Bianchi, I., Martins, A.L., de Vasconcelos, J.B., Rocha, Á. (2020). Business Process Modelling to Improve Incident Management Process. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1159. Springer, Cham. https://doi.org/10.1007/978-3-030-45688-7_68

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