^{"A Good Science and Engineering Decision is a Good Business Decision"}

The days of training required depends greatly upon the experience and capabilities of the participants. Here are four proficiency levels and approximate days of training:

AWARENESS 1 to 4 hours

• Ten-step decision analysis process
• Understanding that cash flow drives business value
• Using present value discounting for time value of money
• Expected value concept and solving simple decision trees
• Expected Monetary Value decision rule

Note: Executive Workshops range from 1 hour to 1 day in length. Depending upon the interests of the participants, the discussion ranges from how to interpret and use the results of a decision analysis and the calculation fundamentals -to- risk policy and measuring shareholder value creation.

WORKING KNOWLEDGE 0.5-3 days

• Decision policy; why risk aversion usually can be neglected
• Traditional decision criteria and strengths/weaknesses
• Complement, addition and multiplication rules
• Probability distributions; mean and standard deviation
• How Monte Carlo simulation works
• Simple spreadsheet modeling and using a decision analysis tool (Crystal Ball, @RISK, DATA, PrecisionTree or other decision analysis software)
• Designing decision models for Monte Carlo simulation and decision tree analysis
• Multi-discipline team analyses

COMPETENCE 2 to 7 days

• Modeling inflation and escalation
• Bayes’ Theorem
• Value of information problems
• Proficient in spreadsheet modeling: layout, names, functions, debugging, documenting
• Competence in a decision analysis computer tool (see above)
• Modeling dependencies; representing correlation
• Diagramming techniques (e.g., influence, tornado, sensitivity and spider diagrams; thought maps)
• Judgments and biases; feedback; post-audits
• Cost of capital; capital constraints; simple optimization methods
• Project schedule models and project risk management

PROFICIENCY experience with substantial real-world problems

• Experience in constructing medium-sized decision models (over 50 variables)
• Portfolio modeling and optimization
• Optimizer's curse and winner's curse phenomena
• Competitive bidding
• Portfolio theory; CAPM alternative
• Risk policy calculations using a utility function
• Traditional optimization techniques: LP and mathematical programming
• Emerging technologies: simulated annealing, expert systems, fuzzy logic, chaos theory, analytic hierarchy process, multi-criteria decision-making
• Evaluation project control, review and documentation
• Analysis write-up and presentation