Copyright © 2009 PMI RiskSIGNovember 5-6, 2009 RiskSIG - Advancing the State of the Art A collaboration of the PMI, Rome Italy Chapter and the RiskSIG.

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Presentation transcript:

Copyright © 2009 PMI RiskSIGNovember 5-6, 2009 RiskSIG - Advancing the State of the Art A collaboration of the PMI, Rome Italy Chapter and the RiskSIG “Project Risk Management – An International Perspective”

Slide 2November 5-6, 2009 Copyright © 2009 PMI RiskSIG Bayesian Networks: A Novel Approach For Modelling Uncertainty in Projects By: Vahid Khodakarami

Slide 3November 5-6, 2009 Copyright © 2009 PMI RiskSIG Outline: u What is missing in current PRM practice? u Bayesian Networks u Application of BNs in PRM u Models u Case study

Slide 4November 5-6, 2009 Copyright © 2009 PMI RiskSIG Conceptual steps in PRMP u Risk Identification âQualitative Analysis u Risk Analysis (Risk Measurement) âQuantitative Analysis u Risk Response (Mitigation)

Slide 5November 5-6, 2009 Copyright © 2009 PMI RiskSIG Project Scheduling Under uncertainty u (CPM) u PERT u Simulation u Critical chain

Slide 6November 5-6, 2009 Copyright © 2009 PMI RiskSIG What is missing? u Causality in project uncertainty u Estimation and Subjectivity u Unknown Risks (Common cause factors) u Trade-off between time, cost and performance u Dynamic Learning

Slide 7November 5-6, 2009 Copyright © 2009 PMI RiskSIG Bayesian Networks (BNs) u Graphical model âNodes (variables) âArcs (causality) u Probabilistic (Bayesian) inference

Slide 8November 5-6, 2009 Copyright © 2009 PMI RiskSIG Bayesian vs. Frequentist FrequentistBayesian VariablesRandomUncertain Probability Physical Property (Data) Degree of belief (Subjective) InferenceConfidence interval Bayes’ Theorem only feasible method for many practical problems

Slide 9November 5-6, 2009 Copyright © 2009 PMI RiskSIG Bayes’ Theorem u ‘A’ represents hypothesis and ‘B’ represents evidence. u P(A) is called ‘prior distribution’. u P(B/A) is called ’Likelihood function’. u P(A/B) is called ’Posterior distribution’.

Slide 10November 5-6, 2009 Copyright © 2009 PMI RiskSIG Constructing BN High 0.7 Low 0.3 On time 0.95 Late 0.05 Prior Probability Sub-contract On time Late Staff Experience High Low High Low No Delay Yes Conditional Probability

Slide 11November 5-6, 2009 Copyright © 2009 PMI RiskSIG Inference in BN (cause to effect) With no other information P(Delay)= Knowing the sub-contract is late P(Delay)=0.50.7

Slide 12November 5-6, 2009 Copyright © 2009 PMI RiskSIG Backward Propagation (effect to cause) Prior probability with no data (0.7,0.3) Posterior (learnt) probability (0.28,0.72)

Slide 13November 5-6, 2009 Copyright © 2009 PMI RiskSIG BNs Advantages u Rigorous method to make formal use of subjective data u Explicitly quantify uncertainty u Make predictions with incomplete data u Reason from effect to cause as well as from cause to effect u Update previous beliefs in the light of new data (learning) u Complex sensitivity analysis

Slide 14November 5-6, 2009 Copyright © 2009 PMI RiskSIG BNs Applications u Industrial âProcessor Fault Diagnosis - by Intel âAuxiliary Turbine Diagnosis - by GE âDiagnosis of space shuttle propulsion systems - by NASA/Rockwell âSituation assessment for nuclear power plant – NRC u Medical Diagnosis âInternal Medicine âPathology diagnosis - âBreast Cancer Manager u Commercial âSoftware troubleshooting and advice – MS-Office âFinancial Market Analysis âInformation Retrieval âSoftware Defect detection u Military âAutomatic Target Recognition – MITRE âAutonomous control of unmanned underwater vehicle - Lockheed Martin

Slide 15November 5-6, 2009 Copyright © 2009 PMI RiskSIG Bayesian CPM CPM Calculation

Slide 16November 5-6, 2009 Copyright © 2009 PMI RiskSIG BCPM Example

Slide 17November 5-6, 2009 Copyright © 2009 PMI RiskSIG Activity Duration

Slide 18November 5-6, 2009 Copyright © 2009 PMI RiskSIG Trade off

Slide 19November 5-6, 2009 Copyright © 2009 PMI RiskSIG Trade off (Prior vs. required resources )

Slide 20November 5-6, 2009 Copyright © 2009 PMI RiskSIG Known Risk

Slide 21November 5-6, 2009 Copyright © 2009 PMI RiskSIG Known Risk (Control)

Slide 22November 5-6, 2009 Copyright © 2009 PMI RiskSIG Known Risk (Impact)

Slide 23November 5-6, 2009 Copyright © 2009 PMI RiskSIG Known Risk (Response)

Slide 24November 5-6, 2009 Copyright © 2009 PMI RiskSIG Unknown Factors

Slide 25November 5-6, 2009 Copyright © 2009 PMI RiskSIG Unknown Factors (Learning)

Slide 26November 5-6, 2009 Copyright © 2009 PMI RiskSIG Learnt distribution

Slide 27November 5-6, 2009 Copyright © 2009 PMI RiskSIG Total Duration

Slide 28November 5-6, 2009 Copyright © 2009 PMI RiskSIG Case Study (construction Project)

Slide 29November 5-6, 2009 Copyright © 2009 PMI RiskSIG Case Study (Bayesian CPM)

Slide 30November 5-6, 2009 Copyright © 2009 PMI RiskSIG Case Study (predictive)

Slide 31November 5-6, 2009 Copyright © 2009 PMI RiskSIG Case Study (diagnostic)

Slide 32November 5-6, 2009 Copyright © 2009 PMI RiskSIG Case Study (learning)

Slide 33November 5-6, 2009 Copyright © 2009 PMI RiskSIG Summary u Current practice in modelling risk in project time management has serious limitations u BNs are particularly suitable for modelling uncertainty in project u The proposed models provide a new generation of project risk assessment tools that are better informed and hence, more valid

Slide 34November 5-6, 2009 Copyright © 2009 PMI RiskSIG Questions? Thank you for your attention