Probability and Statistics for Sciences and Engineers (EMIS 7370) Summer 2011

1. “Application of Probability to Risk Management Analysis” Probability and Statistics for Sciences and Engineers (EMIS 7370) Summer 2011 Tarek Elgembri Faris Altamimi

2. Introduction. • Project Management. • Project Risk Management. • Objectives of Project Risk Management. • Risk and Probability. • Perform Qualitative Risk Analysis. • Perform Quantitative Risk Analysis. • Risk Response. • Case study # 1 • Case study # 2 • Conclusion

3. This project provides an idea about applications of probability involved in project risk analysis. It shows how these applications are very powerful tools to succeed projects. • A risk of project is an event has a probability of occurring that could have a positive or negative effect on its success. • Risk is everywhere. Therefore, we should consider all risk factors to enhance our ability to make the best decision.

4. PROJECT MANAGEMENT • Project management is the application of knowledge, skills, tools and techniques to project activities to meet the project requirements. • Processes of project Management the main five Processes (initiating, planning, executing, monitoring and controlling, and closing).

6. PROJECT RISK MANAGEMENT • Project Risk Management is the processes of conducting risk management planning, identification, analysis, response planning, and monitoring and control on a project. • There are six processes of risk management (planning, identifying, performing qualitative, performing quantitative, risk response, and monitoring and controlling).

7. The matrix relationship between risk management processes and project management processes.

9. Objectives of Project Risk Management • Increase the probability and impact of positive events. • Decrease the probability and impact of negative events. • To identify the risks associated with each work package. • To develop cost-effective risk management actions.

10. The relationship between risk and knowledge of a project.

11. Risk and Probability • probability is a power tool to analyze and score risk. • Risks are possible future events that have not yet occurred and their probability of occurrence cannot be exactly measured. However, we can estimate their probability by many functions.

12. Perform Qualitative Risk Analysis • “ It is prioritizing risks for further analysis by assessing and combining their probabilities of occurrence and their impacts (PMPOK 2008)". • Tools and techniques to perform qualitative risk analysis: (experts’ judgment, workshops, interviews, and probabilities and impact matrix )

13. Probability and Impact Matrix • The main goals of this matrix are to prioritize risks and decide which of them needs more detailed response plans. • This table shows a sample of probability and impact matrix to determine whether the combination of probability and impact at any risk is high, medium, or low.

14. Perform Quantitative Risk Analysis • “It is the process of numerically analyzing the effect of identified risks on overall project objectives (PMPOK 2008)". • Tools and techniques to perform quantitative risk analysis : (sensitivity analysis, interviewing, expected monetary value analysis, and modeling and simulation).

15. Modeling and Simulation • Modeling and simulation are often used for risk analysis, especially for cost and schedule. • Simulation techniques compute the project models using various input data to determine a probability distribution for the chosen variable. • Monte Carlo technique is the most common type of simulation.

16. Risk Response • There are several strategies that could be applied to respond to risks. Selecting the approach of response is depending on the risk analysis. • Some risk response strategies: Avoidance Transference Mitigation Acceptance

17. Case study # 1 • The construction of Al-Haya shopping center . • We are going to prioritize the risks of the construction stages of the project • The following table describes the scoring system for measuring the ‘likelihood’ of a risk.

18. Case study # 1 • The following table describes the scoring system for measuring the impact of a risk.

19. Case study # 1 • Perform Qualitative Risk Analysis:The Rating is based on the calculated Priority score. • This is calculated as Priority = (Likelihood + Impact) / 2

20. Case study # 2 • The project is a construction of new neighborhood. • The following case is covered the study of cost and schedule risk analysis of the housing unite using Monte Carlo Simulation. • The values of the items were randomly generated between the minimum and the maximum value by using Monte Carlo simulation modeling in this website (http://www.riskamp.com).

21. Case study # 2 • Risk analysis of schedule and cost • This table shows the estimate time and cost to finish each item.

22. Case study # 2 • Risk analysis of schedule • The results of the simulation.

23. Case study # 2 • Risk analysis of schedule • The results of the simulation.

24. Case study # 2 • Risk analysis of cost • The results of the simulation.

25. Case study # 2 • Risk analysis of cost • The results of the simulation.

26. Case study # 2 • Observation • The original estimate for the most likely was 20 weeks. On the other hand, from the results, the total time was 20 weeks or less in only 52% of cases. • There is 71% chance to complete the project in 21 weeks. • The original most likely estimate cost was 160800. However, from the simulation, the cost of 160800 or less represents 66% of the cases. • There is 57% chance to complete the project with 163000 $ cost. • This kind of information might help to make different choices when we plan the project. Therefore, better decision can be made in finance, insurance, and hiring. • When a conservative organization wants a 85% likelihood of success, budget of 171,500 $ is required.

27. Conclusion • Risk analysis gives us a big opportunity to take an action in influence the probability of success and failure of a project. • Applying probability applications in risk management such as Monte Carlo simulation at the beginning of a project helps us to make a better plan for the project. • The results show that taking simple methods of estimating cost or time would be a risky approach of doing investment. • Monte Carlo simulation is a very valuable tool to forecast uncertain future events. • In order to be more accurate in the evaluation of risk, both the likelihood and impact of risk should be included in the analysis.

28. BIBLIOGRAPHY • Chu, Margaret, Diane Altwies, and Janice Preston. PMP Exam Success. 4th ed. N.p.: Core Performance Concepts, Inc, 2009. • Heldman, Kim. Project Management Professional Exam Study Guide. 4th ed. Indianapolis: Wiley Publishing, Inc, 2009. • Project Management Institute. A Guide to the Project Management Body of Knowledge (PMBOK Guide). 4th ed. N.p.: Project Management Institute, 2008. • RiskAMP. N.p., n.d. Web. 18 July 2011. • Solomon, Michael G. PMP Exam Cram: Project Management Professional. 4th ed. N.p.: Que Publishing, 2010. • Stracener, Jerrell. Instructor notes for EMIS7370 at SMU. Dallas: n.p., 2011.