Operational Risk Assessment with Agentic AI in Production Plants
MCCM Innovations
AI Consultancy Team
82%
Accuracy Improvement
54%
Processing Time Reduction
2x
Risk Identification Rate
Operational Risk Assessment with Agentic AI in Production Plants
In the construction materials industry, efficient and proactive management of operational risks is essential to ensure safety, business continuity, and regulatory compliance. Traditional risk assessment methods are often manual, slow, and poorly adaptable to the increasing complexity of industrial processes and the integration of new technologies.
The Challenge
Our client, a multinational company with over 300 production plants, faced critical challenges:
- Manual and decentralized risk assessments
- Difficulty integrating data from multiple sources
- Inconsistencies in risk evaluation and prioritization criteria across different plants and teams
- The need to anticipate emerging risks such as machinery failures, workplace accidents, or regulatory non-compliance
Our Approach
We developed and implemented an agentic AI system that automates and optimizes real-time risk assessment across all plant operations. The system:
- Aggregates and processes large volumes of data (operator reports, safety alerts, regulations, etc.)
- Utilizes specialized AI agents addressing different risk dimensions:
- Industrial safety risk agent
- Maintenance and failure risk agent
- Environmental risk agent
- Regulatory compliance agent
- Operational risk agent (unplanned downtime, bottlenecks)
- Provides explainable analyses detailing the key factors behind each risk assessment, facilitating informed decision-making
Technology Stack
The solution leverages:
- Natural language models for analyzing reports and technical documentation
- Integration with client systems
- Vector databases for searching and correlating historical incidents
- Agent orchestration using frameworks like LangChain and workflow automation with n8n
Results and Impact
The implementation delivered substantial improvements:
- 54% reduction in risk assessment time
- 38% increase in early detection of critical risks
- Decrease in operational incidents related to human errors in evaluation
- Losses prevented (not yet quantified) thanks to proactive risk identification
- Significant improvement in regulatory compliance and decision traceability
Key Learnings
- Multi-agent systems effectively address the complexity and diversity of industrial risks
- Integration of heterogeneous data sources enhances the system's predictive and preventive capabilities
- Explainability and traceability are fundamental for acceptance and trust among operational teams
- Collaboration between AI and humans remains key for risk management in critical environments
Looking Ahead
Following the success of the proof of concept, potential improvements are underway, including:
- Integrating real-time risk analysis with plant control systems
- Incorporating scenario simulations and impact analysis for contingency planning
- Enhancing visualization and reporting for audits and international certifications
- Creating an automated workflow for managing requests and permits required to carry out maintenance actions, streamlining coordination between departments and ensuring compliance with safety protocols and regulations
This use case illustrates how agentic AI can transform risk management in the construction materials industry by combining automation, advanced analytics, and decision support in critical operations.