Decision Analysis for Operation and Maintenance Professionals

Download Brochure

Effective decision-making in operations and maintenance (O&M) is essential for ensuring asset reliability, cost control, and operational efficiency. This course provides practical tools and frameworks for professionals to analyze complex maintenance decisions, optimize resource allocation, and implement data-driven strategies. Using real-world case studies, risk analysis techniques, and decision-support models, participants will learn how to improve maintenance strategies, reduce downtime, and enhance operational performance. The course covers quantitative and qualitative decision-making tools, integrating best practices from risk management, reliability engineering, and maintenance planning.

Decision Analysis for Operation and Maintenance Professionals Objectives:

By the end of this course, participants will be able to:

•  Apply structured decision-making frameworks for O&M.
•  Use quantitative methods (cost-benefit analysis, decision trees, probability models) for informed choices.
•  Develop risk-based maintenance strategies.
•  Optimize asset management and resource allocation.
•  Implement Failure Mode and Effects Analysis (FMEA) and Root Cause Analysis (RCA).
•  Integrate predictive analytics and digital tools in maintenance decision-making.
•  Improve operational performance and cost-efficiency through data-driven insights.

Who Should Attend:

This course is ideal for:

• Maintenance and reliability engineers.
• Operations and plant managers.
• Asset management and facility management professionals.
• Industrial engineers and technical decision-makers.
• Risk managers and quality assurance professionals.
• Supervisors responsible for maintenance and operational strategy.

Course Outline

Day 1: Foundations of Decision Analysis in O&M

• Principles of decision analysis in maintenance and operations.
• Types of decision-making models (structured vs. intuitive).
• Key factors influencing O&M decisions (cost, risk, reliability, performance).
• Case study: Successful decision-making in maintenance strategy.

Day 2: Risk-Based Decision Making in Maintenance

• Identifying and assessing maintenance-related risks.
• Risk matrix and risk-based maintenance planning.
• Failure Mode and Effects Analysis (FMEA) for decision support.
• Workshop: Conducting an FMEA assessment.

Day 3: Cost-Benefit Analysis & Investment Justification

• Evaluating maintenance investments using cost-benefit analysis (CBA).
• Understanding life cycle cost analysis (LCA) and Return on Investment (ROI).
• Trade-off analysis: Cost vs. reliability vs. risk.
• Exercise: Creating a cost-optimized maintenance strategy.

Day 4: Decision-Support Tools & Techniques

• Using decision trees and probability models in O&M.
• Multi-Criteria Decision Analysis (MCDA) for asset management.
• Applying Monte Carlo simulation for risk and uncertainty.
• Case study: Applying decision-support tools in a real-world maintenance scenario.

Day 5: Predictive Analytics & Data-Driven Decision Making

• Leveraging AI, IoT, and Big Data in maintenance decisions.
• Predictive maintenance: How data enhances decision-making.
• Using CMMS (Computerized Maintenance Management Systems) for strategic decisions.
• Workshop: Developing a predictive maintenance decision model.

Day 6: Resource Allocation & Maintenance Optimization

• Optimizing maintenance scheduling and resource planning.
• Criticality analysis for prioritizing maintenance activities.
• Implementing reliability-centered maintenance (RCM) for efficiency.
• Exercise: Creating a resource allocation model.

Day 7: Root Cause Analysis (RCA) & Continuous Improvement

• Using RCA to enhance maintenance decision-making.
• Corrective vs. preventive actions in maintenance strategy.
• Applying Six Sigma and Lean principles in O&M.
• Case study: Implementing RCA for operational excellence.

Day 8: Scenario Planning & Decision-Making Under Uncertainty

• Understanding decision-making under uncertainty and risk.
• Scenario analysis and “what-if” modeling for maintenance decisions.
• Developing contingency plans for asset failure scenarios.
• Workshop: Designing a maintenance scenario analysis.

Day 9: Strategic Leadership in O&M Decision-Making

• Integrating decision analysis into leadership roles.
• Communicating data-driven decisions to stakeholders.
• Aligning maintenance strategies with corporate objectives.
• Case study: Effective leadership in O&M decision-making.

Day 10: Final Assessment & Certification

• Capstone project: Developing a strategic decision model for O&M.
• Peer review and expert feedback session.
• Final exam for course certification.
• Closing remarks, Q&A, and certification ceremony.