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:

• It’s possible to assess the architecture of a schedule and know quickly where the failures are likely to occur
• Six distributions of stochastic events and outcomes play an important role in project estimates and risk management
• The phenomenon of “central tendency” may be your best friend when estimating outcomes
• 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
• Decision making is not always rational in spite of a strong dose of quantitative methods; biases of all kinds intrude.
• Testing hypotheses, especially during pilot or prototype activities, is a good way to forecast risks for the project as a whole.

Course Outline:

1. Methodology framework for risk management: Identifying and assessing risk is central to any risk process, but effectiveness depends on methods and practices for responding to risks, and an understanding of stochastic phenomenon that range from natural variation to chaos.
2. 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
3. Central Tendency among stochastic events: We’ll show that central tendency gives rise to engineering approximations that simplify a myriad of complexity, thereby providing useful heuristics for everyday application
4. Risk mitigation in time and resources schedules: Various artifacts of schedule architecture are inherently risky. We’ll look at the issues of merge bias, and the hazards of resource leveling. And, we’ll examine critical chain methods to see how they mitigate risks.
5. Application of Monte Carlo Simulation: Worked examples will show how to integrate Monte Carlo simulation with the traditional linear equations of earned value. And also the risks register: we’ll demonstrate the integration of the risks register with the baseline plan using Monte Carlo simulations.
6. Sampling metrics for project estimates: Every engineer takes a risk from time to time on incomplete or ambiguous data and information. But how incomplete is not too incomplete? This question will be addressed with worked examples
7. Hypothesis testing: Forming hypotheses is common practice in system engineering, but does real data support the hypothesis or not? It’s the Type 1 error that is most hazardous. We’ll work some problems to see how these are handled.
8. Predicting with regression analysis: What’s the next outcome going to be? Example problems will show what’s predictable
9. Evaluating Bayesian effects: Thomas Bayes had an entirely different idea about probability than the traditional frequency definition. His ideas permeate much of risk management engineering 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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