Instructor:

John C. Goodpasture, PMP is Managing Principal at a consulting firm. Mr. Goodpasture has dedicated his career to system engineering and program management, first as program manager at the National Security Agency for a system of “national technical means”, and then as Director of System Engineering and Program Management for a division of Harris Corporation. From these experiences and others, Mr. Goodpasture has authored numerous papers, industry magazine articles, and multiple books on project management, one of which “Quantitative Methods in Project Management” forms the basis for this course.

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What You Will Learn:

• Six distributions of stochastic events and outcomes play an important role in project estimates and risk management
• All numbers are not created equal; errors in their application can be very misleading, if not downright wrong.
• Sampling can save a lot of money, shorten the schedule, and also yield good engineering data
• The good Thomas Bayes had a unique idea about probability, and it relies on real observations of real outcomes
• The phenomenon of “central tendency” may be your best friend when estimating outcomes
• It’s possible to assess the architecture of a schedule and know quickly where the failures are likely to occur
• The “project balance sheet” is about the conundrum of top down allocation and bottom up estimates.

Course Outline:

1. Number concepts for Estimating and Risk Management: Knowing which to apply among cardinals and ordinals, stochastic and deterministic, is a prerequisite to all quantitative methods in project management and system engineering
2. Sampling metrics for project estimates: Is 30 samples meaningful for project estimates, or does it take 3000? This question will be addressed with worked examples
3. Central Tendency among stochastic events: We’ll show that central tendency gives rise to approximations that simplify a myriad of complexity, thereby providing useful heuristics for everyday application
4. Risk mitigation in time and resources schedules: A few thoughts on merge bias, and the hazards of various artifacts of schedules
5. Application of Monte Carlo Simulation: Worked examples will show how the Monte Carlo simulation applies to the traditional linear equations of earned value
6. Hypothesis testing: It’s that Type 1 error that is most hazardous. We’ll work some problems to see how these are handled.
7. Predicting with regression analysis: What’s the next outcome going to be? Example problems will show what’s predictable
8. Evaluating Bayesian effects: Thomas Bayes had an entirely different idea about probability than the traditional frequency definition. His ideas permeate much of project schedule estimates
9. Quantitative decision making: Anchoring and adjustment bias, optimism bias, representative and availability bias, and other utility effects influence the otherwise rational analysis of quantitative decision making. We’ll take a look at some examples
10. The Project Balance Sheet: This not a CFO’s balance sheet, but nonetheless, double entry accounting helps balance top down allocations and bottom up estimates.

Tuition:

Tuition for this three day course is $1795 per person at one of our scheduled public courses. Onsite pricing is available. Please call us at 410-956-8805 or send an email to ATI@ATIcourses.com.

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