Course Details

Decision Science for Project Managers Training Course: Applying quantitative methods to complex project decisions

Introduction

Elevate your project decision-making from intuition to analytical precision with our "Decision Science for Project Managers" training course. In projects fraught with uncertainty, multiple stakeholders, and high stakes, traditional approaches often fall short. This intensive 10-day program equips project professionals with powerful quantitative methods from the field of decision science, enabling you to systematically analyze alternatives, quantify risks, optimize resource allocation, and make more rational, evidence-based choices. Learn to build robust decision models, assess trade-offs, and navigate complexity with confidence, ensuring your projects consistently deliver superior outcomes and strategic value.

Duration

10 Days

Target Audience

This course is essential for experienced project managers, program managers, PMO directors, portfolio managers, risk managers, business analysts, and senior leaders who regularly face complex, high-stakes decisions within their projects and organizations. It is particularly beneficial for those who:

• Manage projects with significant uncertainty, multiple objectives, or conflicting stakeholder interests.
• Need to justify project choices with rigorous, data-driven analysis.
• Are looking to improve consistency and objectivity in project decision-making.
• Work in environments where resource optimization and risk quantification are critical.
• Seek to enhance their analytical and strategic thinking skills.

Course Objectives

Upon successful completion of the "Decision Science for Project Managers" training course, participants will be able to:

• Understand the fundamental principles of decision science and its relevance to project management.
• Differentiate between various decision-making frameworks and select the most appropriate for project contexts.
• Apply quantitative methods to structure complex project decisions under uncertainty.
• Master techniques for eliciting and incorporating expert judgment and subjective probabilities.
• Develop decision trees, influence diagrams, and simulation models to analyze project alternatives.
• Conduct sensitivity analysis and explore trade-offs to identify optimal project strategies.
• Understand the psychological biases that impact decision-making and strategies to mitigate them.
• Leverage decision science tools to optimize resource allocation, scheduling, and risk responses.
• Communicate complex decision analysis findings clearly and persuasively to stakeholders.
• Formulate a comprehensive action plan for integrating decision science principles into their project management practices.

Course Modules

Module 1: Introduction to Decision Science for Projects

• Defining Decision Science: A systematic approach to making better choices.
• Why traditional project decision-making often falls short: Biases, complexity, uncertainty.
• The value proposition of decision science in project management: Improved outcomes, reduced risk, greater transparency.
• Overview of key decision science tools and techniques.
• Case studies of impactful decision science applications in project settings.

Module 2: Structuring Complex Project Decisions

• Problem framing: Clearly defining the decision, objectives, and alternatives.
• Identifying stakeholders and their preferences and values.
• Breaking down complex problems into manageable components.
• Establishing clear decision criteria and metrics for success.
• The role of decision boundaries and scope definition.

Module 3: Decision-Making Under Uncertainty

• Understanding different types of uncertainty: Aleatory (inherent randomness) vs. Epistemic (lack of knowledge).
• Probability basics: Subjective vs. objective probabilities in project contexts.
• Expected Value (EV) and Expected Monetary Value (EMV) for decision alternatives.
• Risk tolerance and risk attitude in project decision-making.
• Incorporating uncertainty into project schedules (e.g., PERT, Monte Carlo).

Module 4: Decision Trees and Influence Diagrams

• Constructing and analyzing decision trees for sequential decisions.
• Calculating expected values and identifying optimal paths in decision trees.
• Introduction to Influence Diagrams: Visualizing relationships between decisions, uncertainties, and outcomes.
• Using decision tree and influence diagram software for complex analysis.
• Practical exercises: Building decision models for project scenarios.

Module 5: Utility Theory and Multi-Criteria Decision Analysis (MCDA)

• Beyond monetary value: Incorporating non-monetary objectives and stakeholder preferences.
• Introduction to Utility Theory: Quantifying preferences for risky outcomes.
• Multi-Criteria Decision Analysis (MCDA) frameworks (e.g., AHP, ELECTRE, PROMETHEE).
• Weighting criteria and scoring alternatives based on multiple objectives.
• Facilitating group decision-making with MCDA tools.

Module 6: Simulation Modeling for Decision Support

• Introduction to Monte Carlo Simulation for analyzing project uncertainty.
• Building simulation models to evaluate project alternatives under varying conditions.
• Analyzing simulation outputs: Probability distributions of project costs, durations, and returns.
• Using simulation to identify critical success factors and sensitivities.
• Leveraging simulation software (e.g., @RISK, Crystal Ball) for project decisions.

Module 7: Behavioral Biases in Project Decision-Making

• Common cognitive biases that affect project managers (e.g., optimism bias, confirmation bias, sunk cost fallacy, anchoring).
• Understanding the impact of emotional factors on rational decision-making.
• Strategies for debiasing: Structured decision processes, devil's advocate, diversity of thought.
• The role of psychological safety in fostering open discussion of alternatives.
• Ethical considerations in quantitative decision-making.

Module 8: Optimizing Resource Allocation and Scheduling

• Applying optimization techniques to project resource allocation.
• Linear programming basics for constrained project problems.
• Heuristics and approximation algorithms for complex scheduling.
• Using decision science to evaluate trade-offs between cost, time, and quality.
• Resource leveling and smoothing with an optimization lens.

Module 9: Sensitivity Analysis and Value of Information

• Performing sensitivity analysis: How changes in inputs affect project outcomes.
• Identifying key drivers of project success or failure.
• The concept of "Value of Information": Deciding whether to gather more data before making a decision.
• Quantifying the benefit of reducing uncertainty.
• Practical application of sensitivity and VoI in real project scenarios.

Module 10: Implementing Decision Science in Project Management & Action Plan

• Challenges and success factors for integrating decision science into PMO and project processes.
• Building internal capabilities: Data literacy, analytical skills, software proficiency.
• Communicating complex analytical results persuasively to non-technical stakeholders.
• Fostering a culture of data-driven, rational decision-making.
• Personalized action plan: Identifying specific project decisions for applying decision science techniques and a roadmap for implementation.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com