How do you leverage PSM data and analytics for decision making and learning?

To leverage PSM (Process Safety Management) data and analytics for decision-making and learning: 1. Collect and analyze data on process safety incidents. ?? 2. Identify trends and patterns to understand root causes. ?? 3. Use predictive analytics to anticipate potential risks. ?? 4. Implement corrective actions based on data insights. ??? 5. Share learnings across teams for continuous improvement. ?? 6. Incorporate data-driven decision-making into safety protocols. ??

In my experience, creating a solid database of the process safety data is the first step in the journey of a data-driven process safety. The database shall store the process safety reference such as: 1. Process Safety Informations 2. HAZOPS results 3. Inspection results 4. Operating Procedures 5. Management of Change records 6.Audit results 7. Incident Investigation reports And also some of the more advanced data: 1. SECE status 2. SECE overrides 3. Number of Near Misses 4. Program Participants Having these data will help us derive insights for decision making related to process safety

1. **Sprint Performance Analysis**: Analyze data related to sprint velocity, burndown charts, and other metrics to assess the team's performance in each sprint. Identify trends, bottlenecks, and areas for improvement. Use this data to make informed decisions about adjusting sprint planning, resource allocation, or process improvements. 2. **Retrospective Insights**: Use data from sprint retrospectives to identify recurring issues, root causes of problems, and areas of improvement. Analyze retrospective data to track the team's progress in addressing identified issues and implementing improvements over time

Leverage Process Safety Management (PSM) data and analytics to inform decision-making and learning by identifying trends, assessing risks, optimizing processes, and implementing preventive measures to enhance safety performance.

High data quality is paramount—ensuring accuracy, completeness, consistency, timeliness, and accessibility. Addressing data quality enhances the reliability of PSM analyses, facilitating informed decision-making and proactive risk management. It's the foundation for optimizing safety, compliance, and operational efficiency within complex industrial processes.

Utilize descriptive, diagnostic, predictive, or prescriptive analytics to gain insights into your PSM system's current state, root causes, future scenarios, or optimal actions. Techniques like trend analysis, benchmarking, and risk assessment uncover patterns, anomalies, and opportunities. Effective communication relies on clear data visualization—charts, graphs, dashboards—to highlight key findings and recommendations, enabling stakeholders to act decisively for enhanced safety and operational excellence.

Leveraging PSM data and analytics enables informed decision-making aligned with strategic goals and objectives. It is imperative to adhere to regulatory frameworks and industry standards, integrating them into the decision-making process. Considering trade-offs, uncertainties, and assumptions is vital for robust PSM decisions, along with evaluating potential impacts and outcomes. Collaboration with relevant stakeholders and experts enhances transparency and ensures a well-informed approach. By incorporating these principles, organizations can strengthen their PSM framework, promoting safety, efficiency, and regulatory adherence in their operations.

Align decisions with strategic objectives and regulatory standards, integrating insights from analytics to navigate uncertainties and trade-offs. Evaluate potential impacts and involve stakeholders to ensure transparency and collaboration. Engaging experts and soliciting feedback enhances decision quality, fostering a proactive approach that enhances safety, compliance, and organizational resilience in complex industrial environments.

PSM data and its analytics are very crucial information for senior management to take appropriate decision making. To start with capturing PSM data, we needs to define PSM leading & lagging indicators inline with API 754, which can be captured based maturity of PSM system implementation in any organization. Few example of indicator are as stated below: Leading Indicators: 1. Nos. of alarms operating outside SOC 2. Nos. of safety critical interlocks activated on demand 3. Safe Operating Limit deviation 4. Nos. of recommendations pending from PHA, PSSR & PSM Audit Lagging indicators: 1. Process Safety Incident Tier-1/2/3 Analysis of this indicator can be discuss with line representative and senior management in PSMSC and Apex Meeting.