Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Social Research
Chalid Sahuri, Afandi
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Utilization of Business Intelligence Dashboards for Continual Improvement of It Services and Efficient Workforce Demand Prediction Based on Service Desk Ticket Data Chalid Sahuri, Afandi; Muklason, Ahmad
Journal of Social Research Vol. 5 No. 4 (2026): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v5i4.3077

Abstract

In the digital age, organizations must provide efficient and reliable IT services to ensure business continuity. The implementation of ITIL 4 as a framework for IT service management has become essential in many organizations, including PT XYZ. However, challenges persist in service performance reporting, particularly due to manual, time-consuming processes and the absence of predictive analytics. This research focuses on the design and development of a Business Intelligence (BI) dashboard that integrates ITIL 4 principles to automate reporting, track SLA trends, and predict IT workforce needs. Using historical ticket data, this study employs ARIMA forecasting to predict future ticket volumes and optimize workforce planning. The BI dashboard provides a visual, real-time overview of service performance, ticket status, and SLA compliance, replacing traditional manual processes. Interviews with IT managers from various regions of PT XYZ inform the dashboard's design, ensuring it meets operational and strategic needs. The results indicate that the BI dashboard significantly improves reporting efficiency, enhances SLA monitoring, and supports data-driven decision-making. The integration of descriptive and predictive analytics provides a robust decision-support framework, promoting continual improvement in IT service management. Future research will enhance the system by incorporating external variables and hybrid forecasting models.
Co-Authors Ahmad Muklason