Do you feel confident?

1. Do you feel confident? Building an integrated Monte Carlo risk register into your project plan Project Challenge - September 2011 Presented by Ian Wallace

2. Typical project risk register

3. Palisade Corporation • Decision-making software • DecisionTools Suite (Excel add-ins) • @RISK • Top Rank • PrecisionTree • Optimizer, NeuralTools, Evolver, StatTools • Developer’s Kit • @RISK for Project • Established 1984, HO in Ithaca, New York state. • European HQ in London • Over 150,000 users worldwide • Taught at top 60 MBA programs globally • Used by a huge variety of companies • www.palisade.com

4. Introductions - Ian Wallace • Accountant in Industry -ACMA • KPMG Management Consulting • 15 years in Project Management • Palisade - specialise in Decision Analysis using the DecisionTools Suite: • @RISK for Excel • @RISK for Project • Top Rank • PrecisionTree • RISKOptimizer, NeuralTools, etc

5. Agenda • Introduction • What is Monte Carlo risk analysis? • Why bother? • Example Monte Carlo risk register model using @RISK • Conclusion

6. History

7. Probability distributions • Example - The Triangular distribution • spreads the probability across • a simple 3 point estimate • quickly captures the 90% • confidence range (or lack thereof!)

8. Monte Carlo sampling

9. Monte Carlo distribution Expected Value/Mean ML Min Max 10 0 20 30 40 50 60 70 90% chance ‘Stresses’ the model with probability

10. Probabilistic sensitivity analysis Key risk drivers Systems design acceptance Commissioning Data migration Test acceptance Installation Development Task 1 Task 2 Helps prioritise the mitigation effort

11. Agenda • Introduction • What is Monte Carlo risk analysis? • Why bother? • Example Monte Carlo risk register model using @RISK • Conclusion

12. Decisions, decisions…… • Is this a ‘quality’ plan? • What is the margin of error? • Have we got enough time? • Have we got enough budget? • What are the ‘odds’? • What can we do to reduce uncertainty? ? • What price/date shall we quote? • Is this the most risk efficient approach? • What is the residual risk – do we need to off-lay more risk?

13. Typical risk approaches Risk registers 3-point estimates – minimum, most likely, worse cases Scoring methods What-ifs Just plain “gut feel” Not focused enough for difficult decisions

14. They also lead to a single number • “The Number,” once written, becomes set in stone • “The Number” is disseminated • “The Number’s” underlying assumptions – and errors - are forgotten • “The Number” becomes the basis for big decisions • Most likely case in 3-point estimate = “The Number” • Scoring methods produce a final single “score” = “The Number” • Running lots of subjective What-ifs forces managers to pick “most likely” point assumptions to get “The Number” • Gut feel simply guesses “The Number” (well, we all do it!) The 90% confidence range is more useful

15. Monte Carlo measures the confidence level Expected Value/Mean ML Min Max 10 0 20 30 40 50 60 70 90% confidence range Is this acceptable?

16. Probabilistic sensitivity analysis Key risk drivers Systems design acceptance Commissioning Data migration Test acceptance Installation Development Task 1 Task 2 Prioritises mitigation based on probable effect on the target outturn

17. Compare confidence levels

18. So - why bother? • Monte Carlo analysis provides the ‘ammunition’ for making difficult decisions, e.g. • changing the plan • supplier selection • contingencies – time and money • go or no- go (Stopping!) • Other benefits: • corporate benefits • improved risk management = competitive edge • helps avoid over-commitments to customers • good corporate governance practice - improved transparency • personal benefits • looks professional • shows deep understanding of the problem

19. Agenda • Introduction • What is Monte Carlo risk analysis? • Why bother? • Example Monte Carlo risk register model using @RISK - Building an integrated Monte Carlo risk • register into your project plan • Conclusion

20. Cost risk analysis using @RISK for Excel Recommended cost contingency after probability

21. Schedule risk analysis - @RISK for Project

22. And soon, @RISK v6 • Imports a MS Project plan into Excel for Monte Carlo simulation • Provides a dynamic link between MS Project and MS Excel • means that all the Monte Carlo analysis is in the same place • provides access to Palisade’s DecisionTools Suite • includes new risk perspectives – e.g. probabilistic project cashflows • See demonstration

23. Agenda • Introduction • What is Monte Carlo risk analysis? (probabilistic sampling) • Why bother? (‘ammunition’ for difficult decisions) • Example Monte Carlo risk register model using @RISK • Conclusion

24. Conclusion • Confidence and trust are vital in project management: • nobody wants poor quality plans and estimates • adding a probabilistic risk register to a cost estimate or project plan: • measures the confidence level and the key risk drivers • provides the ‘ammunition’ to make difficult decisions and build consensus on the way forward • Not difficult: • just add probability distributions to existing spreadsheets and project plans • works in familiar technology

25. Remember - ask yourself this question “I know what you’re thinking: 'Did he fire six shots, or only five?' Well, to tell you the truth, in all this excitement, I’ve kinda lost track myself. But being this is a .44 Magnum, the most powerful handgun in the world, and would blow your head clean off, you’ve got to ask yourself one question: 'Do you feel lucky?'

26. Then, ask yourself again Well do ya, punk?”

27. Questions? Good ‘Luck’! iwallace@palisade.com