A working playbook synthesized from Vanderbilt Owen MGT 6478 (Business Project Management, Mod I, Fall 2025, Professor Sina Khorasani) and the canonical literature of project delivery: PMI's PMBOK Guide 7th Edition, the Standard for Program Management 5th Ed, the Risk Management Practice Guide, Larson and Gray's Project Management: The Managerial Process, plus the HBR / MIT Sloan canon — Pisano's Hard Truth About Innovative Cultures, Flyvbjerg on Megaprojects, Bojinov's Keep Your AI Projects on Track, Sting et al. on Encouraging Help, Girotra and Netessine on Risk in Business Models, Ferrazzi on Virtual Teams, Mankins on Your Scarcest Resource, Rigby/Sutherland/Noble on Agile at Scale, plus IDEO on Design Thinking and the WSJ on common execution pitfalls.

Plus the team's own integrative deliverable: the AI & Predictive Analytics in Megaprojects final project, treated as the worked end-to-end application of the full course toolkit. And the Project Management Simulation (Scope/Resources/Schedule v3) from HBP, the CrashingProjects Python optimization notebook, the NPV and Assignment Problem Excel work, and 8 practice problem sets that anchor the quantitative work.

Why this masterclass exists

Most MBA programs treat project management as a footnote inside an operations course — a few lectures on Gantt charts, a Critical Path example, a wave at risk management, done. But every executive job, every consulting engagement, every venture, every transformation is structurally a project. Operations run the organization. Projects change the organization. The skill of taking a fuzzy objective, a constrained budget, a heterogeneous team, and a disrupted environment, and delivering a defined outcome on time — that is the discipline that separates managers who move things forward from managers who watch things drift.

MGT 6478 (Vanderbilt Owen, Professor Sina Khorasani) treats project management with that delivery-first stance. The course is built on three integrated layers — (1) methodologies for organizing effort contributions (PMBOK 7, predictive vs. agile, organizational structures), (2) quantitative tools for planning and monitoring projects (network scheduling, crashing, resource allocation, earned value management), and (3) interpersonal skills for leading and communicating with stakeholders (innovative cultures, virtual teams, the law of reciprocity, project leadership). The course's pedagogical method is hands-on: classroom exercises (the spaghetti-and-marshmallows skyscraper, planning poker, problem-solving game), the Project Management Simulation from HBP, 8 practice problem sets with full solutions, three quizzes, and the integrative final project where teams research and present an emerging trend. This masterclass also leans hard on the team's own AI & Predictive Analytics in Megaprojects final deliverable as the integrative case.

This masterclass synthesizes that course's frameworks with the canonical literature — 14 anchor readings plus 5 PMI/textbook reference works — and the team's hands-on artifacts. The result is approximately 30,000 words organized into 10 modules plus a Master Playbook.

By the end, you should be able to:

• Define a project's scope, priorities, and acceptance criteria using a SMART scope statement and a project priority matrix
• Build a Work Breakdown Structure that supports both top-down and bottom-up estimation
• Construct a project network diagram (AON), perform forward/backward pass, identify the critical path, and distinguish total slack from free slack
• Run a quantitative project crashing exercise with Python optimization to find the cost-minimizing schedule under a deadline constraint
• Schedule resources under both time-constrained and resource-constrained conditions using the parallel method, smoothing, and leveling
• Run the four-step risk management process (identify, assess, respond, control) with a risk register, severity matrix, and contingency plan
• Compute Earned Value Management metrics (PV, EV, AC, SPI, CPI, EAC, ETC) and read them as a project-control system
• Diagnose and intervene on project execution pathologies (scope creep, Brooks's Law, Parkinson's Law, the student syndrome, planning fallacy)
• Build and lead high-performing project teams (Tuckman, psychological safety, the law of reciprocity, virtual-team patterns)
• Decide between predictive (waterfall) and adaptive (agile/Scrum) approaches and operate either at scale
• Apply AI and predictive analytics across the project lifecycle (planning, execution, risk, closure) with appropriate guardrails

The course's defining stance