IMPROVEMENT OF INCIDENT MANAGEMENT MODEL USING MACHINE LEARNING METHODS

Abstract

Technical support of IT infrastructure is a crucial aspect of organizational operations, with the most challenging task being ensuring service continuity. Quality support guarantees high IT efficiency, but complex incidents reduce support quality and require effective management. Incident management includes configuration processes and control of technical solutions. To improve technical support, adhering to both quantitative and qualitative standards and considering system specifics is necessary. According to service level agreements (SLA), the resolution time of incidents is important. „Service Desk“ tools, applying machine learning methods, can help optimize these processes. Incorrectly classified user requests lead to additional work for the IT team and delay incident resolution. Machine learning methods, such as K-means clustering, Random Forest regression, and classification, can optimize incident management and speed up resolution time. The research analyzes „Service Desk“ incident data to model resolution times and improve incident management.