Terry Johns: Uncertainty - understanding the impact and the importance of recording assumptions
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This document discusses uncertainty analysis and the importance of recording assumptions. It explains that single point estimates are often inaccurate, and it is better to estimate variables as ranges or distributions. Tools like sensitivity analysis can help identify the key drivers of uncertainty. Monte Carlo analysis incorporates the uncertainty ranges into simulations to generate outputs. All estimates and assumptions must be thoroughly documented in files like the Master Data Assumption List, as models may be audited. Recording assumptions provides evidence for results and allows others to understand and validate the analysis.
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Terry Johns: Uncertainty - understanding the impact and the importance of recording assumptions
- 1. Uncertainty Understanding the impact and the importance of recording assumptions riskHive CAAS training – block3 - 2009 1 riskHive APM Uncertainty presentation Oct 2013
- 2. Terry JOHNS Senior Risk Consultant with riskHive ltd riskHive CAAS training – block3 - 2009 2 riskHive APM Uncertainty presentation Oct 2013
- 4. Presentation Content • What will be covered – What is Uncertainty and Risk – How to establish uncertainty drivers. – Estimating Uncertainty values – Monte Carlo – Why we record data/estimate assumptions. riskHive CAAS training – block3 - 2009 4 riskHive APM Uncertainty presentation Oct 2013
- 5. What is Uncertainty? riskHive CAAS training – block3 - 2009 5 riskHive APM Uncertainty presentation Oct 2013
- 6. Discussion points • Single point estimates. • Better to use a range of possible values. • What shape is the range. • Is there historical data? – Has been cleansed of risks that occurred • Where to focus effort. riskHive CAAS training – block3 - 2009 6 riskHive APM Uncertainty presentation Oct 2013
- 7. Estimating Maturity riskHive APM Uncertainty presentation Oct 2013
- 8. Sensitivity Analysis • How to establish what elements have the most impact. • The riskHive solution is the statistical analysis the whole MS Excel cost or MS Project model • Find the Drivers riskHive CAAS training – block3 - 2009 8 riskHive APM Uncertainty presentation Oct 2013
- 9. Flexing the Model The Arrisca tool riskHive CAAS training – block3 - 2009 9 riskHive APM Uncertainty presentation Oct 2013
- 10. riskHive APM Uncertainty presentation Oct 2013 • The model is ‘flexed’ by Arrisca to stress- test the sensitivity of the outcome to each individual input element. • An input is: • Excel: Any cell that is not text or a formula (constant) • Project: Any task duration that is not a milestone or a ‘roll-up’ task. • The sensitivity ranges are set in the Arrisca application as Upper and Lower Driver Analysis Multipliers (+/- percentages) 10
- 11. riskHive APM Uncertainty Presentation Oct 2013 Sensitivity Driver Analysis process flowchart 11 Identify all Inputs Goto 1st Input Get Cell Value (V) Set Value to (V - x% ) Record effect on outcome Set Value to (V + x% ) Record effect on outcome Reset Value to (V) Goto next Input More Inputs ? Y N Calculate & rank effects Display Tornado Chart Start End Apply ‘minimum’ multiplier Apply ‘maximum’ multiplier
- 12. Start ARRISCA • riskHive ARRISCA
- 13. riskHive APM Uncertainty presentation Oct 2013 Project Driver Analysis and quantification by stress-test 13 • Demonstration of Project Sensitivity Driver Analysis.
- 14. riskHive APM Unceratinty presentation Oct 2013 Excel Driver Analysis and quantification by stress-test 14 Simple example contract for risk assessment with Arrisca PFS Materials Subcontract Design Rate Production Rate Subtotals Total £ £ Hrs £/hr Hrs £/hr £ a £5,000 £250,000 25,000 Hrs £55 1630000 b £1,000 £15,000 150 Hrs £45 22750 c £400 £20,000 1,000 Hrs £55 75400 d £1,500,000 60 Hrs £77 1504620 e £500,000 £2,000,000 45,000 Hrs £34.00 4030000 f £500 2,500 Hrs £40.00 100500 g £1,500,000 400 Hrs £40.00 1516000 Work Cost Total 8879270 Overhead 220% £19,534,394 £28,413,664 Escalation 5% £443,964 £28,857,628 Profit 7.10% £2,048,892 £30,906,519 Selling price £30,906,519 Delta 0 Delta% 0.00% • Demonstration of Excel Sensitivity Driver Analysis.
- 15. riskHive APM Uncertainty presentation Oct 2013 Sensitivity Driver Analysis and ‘the inquisition’ Identify, investigate and describe critical driver data Identify Driver Fixed? How fixed? Determine value Identify Owner How long for? Yes Uncertain? Define Range MinExpMax (Distribution Shape) Risk? Define Risk MinExpMax (Distribution Shape) Define Probability Opp’? Define Opp MinExpMax (Distribution Shape) Define Probability Quantify Risk Quantify Opp End Assessment Review Unc? Yes Yes Yes No No No No 15
- 16. What to do with all these estimates? riskHive CAAS training – block3 - 2009 16 riskHive APM Uncertainty presentation Oct 2013
- 17. riskHive APM Uncertainty presentation Oct 2013 Monte Carlo Analysis process flowchart 17 Identify affected Inputs Goto 1st affected Input Get Cell Value (V) Calculate Uncertainty Value (O) Set Cell Value to U+(R-O) Record effects on Cells & Outcomes Reset all Cell Values to (V) Goto next affected Cell More Variance ? Y N Calculate Statistics Generate Graphs & Reports Start End Generate Monte Carlo 3PE parameters Generate Monte Carlo Risk parameters Calculate Risk Opp Values (R,O) Apply Monte Carlo Simulation parameters More Sims? Y N
- 18. Recording Estimates • The tool will help you determine where estimating effort should made. • In the MoD every business case will go thro an assurance process, no doubt most Company’s have or should have a similar process • To satisfy this Cost & Schedule models for high value projects will be investigated by an independent assurance team. riskHive CAAS training – block3 - 2009 18 riskHive APM Uncertainty presentation Oct 2013
- 19. Evidence • All models should be Validated & Verified. • All estimates will need to be documented either in Master Data Assumption List (MDAL) or Cost Data Assumption List (CDAL). • Top 10 drivers to be listed in an report. riskHive CAAS training – block3 - 2009 19 riskHive APM Uncertainty presentation Oct 2013
- 20. Summary • Single point estimates are usually wrong. • Ranges for uncertainty are better. • There are tools to help establish drivers. • Estimates must be recorded • Models must be V&V’d • Monte Carlo is used to create outputs • You need to be ready to explain outputs to management. riskHive CAAS training – block3 - 2009 20 riskHive APM Uncertainty presentation Oct 2013
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