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By: Umang. R. Raichura, Ayush. R. Patel R. Patel, Dr. Jayesh. R. Pitroda, Dhrumil Chauhan, Ditsa. D. Chavda, Mr. Sunil Patel, and Tirthrajsinh. U. Chouhan.
Dept. of Civil Engineering Birla Vishvakarma Mahavidyalaya (BVM) Engineering College Anand
Abstract— In the evolving domain of construction management, data-driven decision-making plays a pivotal role in ensuring successful project execution. This paper presents a comprehensive approach to risk identification, analysis, and mitigation using Microsoft Power BI. By integrating Failure Mode and Effects Analysis (FMEA) and risk data extracted from periodic reports (PPR, DPR, WPR), we demonstrate how Power BI’s data visualization capabilities can transform raw datasets into actionable insights. The generated dashboards help categorize risks, track mitigation progress, and assess their severity, occurrence, and detection probability. This research highlights the significant role of Power BI in promoting efficiency, reducing delays, and enabling proactive risk management in infrastructure projects. By leveraging the advanced data processing and visualization capabilities of Power BI, large volumes of raw data are systematically converted into interactive and user-friendly dashboards. These dashboards facilitate effective classification of risks, continuous monitoring of mitigation strategies, and comprehensive evaluation based on parameters such as severity, occurrence, and detection probability. The approach enables project stakeholders to gain real-time insights into potential risks and supports timely decision-making.
Furthermore, the implementation of Power BI enhances transparency, improves communication among project teams, and minimizes the likelihood of delays and cost overruns. The findings of this study emphasize the importance of integrating business intelligence tools in construction management to achieve efficient, accurate, and proactive risk management. Overall, the research demonstrates that Power BI serves as a powerful platform for transforming conventional risk management practices into a more structured, data-centric, and intelligent system.
Citation:
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