AI-Powered MIS for Risk Detection in Industrial Engineering Projects

Industrial engineering projects are highly complex, involving multiple stakeholders, large budgets, and safety-sensitive operations. These projects are often exposed to risks such as delays, equipment failures, safety hazards, and cost overruns, which can significantly impact outcomes. Traditional Management Information Systems (MIS) support project managers by centralizing data and generating reports, but they remain largely reactive, documenting risks only after they occur rather than predicting them in advance. This paper proposes an AI-powered MIS framework designed to transform risk management in industrial engineering projects. The framework integrates predictive analytics, anomaly detection, and natural language processing into MIS dashboards to provide proactive risk detection. By analyzing both structured data, such as financial records, project schedules, and sensor outputs, and unstructured data like safety logs and incident reports, the system can identify anomalies, forecast delays, and anticipate budget overruns. Simulation-based evaluations demonstrate that the AI-powered MIS improves risk visibility, enabling project managers to detect emerging issues 25-30% earlier than traditional systems. This early detection enhances decision-making, reduces financial losses, and strengthens workplace safety. The modular design also ensures scalability across industries, from manufacturing and construction to energy and transportation. By embedding AI-driven intelligence into MIS workflows, the proposed framework evolves project management systems from static reporting tools into dynamic, predictive platforms. The results highlight its potential to reduce cost overruns, improve safety outcomes, and enhance overall project resilience in data-intensive environments.