Decision Analysis for Operation & Maintenance Professionals Event, 13.Oct.2025

Course Description

This intensive 5-day course equips operation and maintenance professionals with advanced decision analysis skills. Participants will learn to apply structured decision-making techniques, utilize decision trees, and incorporate risk assessment in their daily operations. The course combines theoretical concepts with practical applications, ensuring immediate value in the workplace.

Learning Objectives

• Apply decision analysis frameworks to complex O&M scenarios
• Construct and interpret decision trees for operational choices
• Integrate risk assessment techniques into maintenance planning
• Utilize probabilistic modeling for equipment reliability analysis
• Implement value of information concepts in maintenance strategies

Course Modules

Day 1: Foundations of Decision Analysis in O&M

• Introduction to decision analysis principles
• Structuring O&M decisions
• Framing problems in maintenance contexts
• Decision quality framework for operations

Day 2: Decision Trees and Probabilistic Modeling

• Constructing decision trees for maintenance choices
• Probability assessment in operational uncertainties
• Expected value calculations for equipment decisions
• Sensitivity analysis in O&M contexts

Day 3: Risk Assessment and Management in O&M

• Risk identification in operational environments
• Quantitative risk analysis techniques
• Risk mitigation strategies for maintenance
• Integrating risk in decision-making processes

Day 4: Value of Information in Maintenance Decisions

• Concepts of perfect and imperfect information
• Calculating value of information for maintenance data
• Applying VOI to condition monitoring decisions
• Information gathering strategies in O&M

Day 5: Advanced Topics and Practical Applications

• Multi-criteria decision analysis for O&M
• Group decision-making in operational teams
• Cognitive biases in maintenance decisions
• Case studies and real-world applications

Practical Wins for Participants

• Improved ability to make data-driven maintenance decisions
• Enhanced risk management in daily operations
• Increased efficiency in resource allocation for maintenance
• Better justification of O&M investments to management