Project Management - Part 7


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Project Management - Part 7

  1. 1. Project Management Part 5 Project Risk Management
  2. 2. Topic Outline: Risk Management <ul><li>Project risks and risk management </li></ul><ul><li>Identification of risks </li></ul><ul><li>Risk assessment and risk analysis </li></ul><ul><li>Contingency planning </li></ul><ul><li>Time and cost padding </li></ul><ul><li>Expected values </li></ul><ul><li>Risk management exercise </li></ul><ul><li>PERT analysis </li></ul><ul><li>Computer simulation analysis </li></ul>
  3. 3. Project Risks <ul><li>Uncertainty  a random chance that something will happen, with no way to control whether it happens </li></ul><ul><li>Risk  an uncertain event or condition that could negatively impact project performance </li></ul><ul><li>Each risk has a likelihood, or probability, of occurring and possible outcomes if it does occur </li></ul>
  4. 4. Managing Risks <ul><li>Since the project manager is responsible for project success, he or she can increase the likelihood of success by better managing risks </li></ul><ul><li>Risk management is a proactive approach to dealing with uncertainties rather than a reactive approach </li></ul><ul><li>Some risks can be disregarded and some can be avoided, but others should be planned for </li></ul>
  5. 5. Project Risk Management <ul><li>Risk management in projects involves: </li></ul><ul><li>Identifying risks </li></ul><ul><li>Assessing and analyzing the likelihood and impacts of risks </li></ul><ul><li>Trying to reduce the uncertainties (by gathering more information or making different decisions) </li></ul><ul><li>Trying to lessen the impacts of risks </li></ul><ul><li>Developing contingency plans for critical risks </li></ul><ul><li>Monitoring risks as the project progresses </li></ul>
  6. 6. PMI’s View of Risk Management <ul><li>Risk management consists of 6 subprocesses: </li></ul><ul><li>Risk Management Planning </li></ul><ul><ul><li>How to approach and conduct risk mgmt. activities </li></ul></ul><ul><li>Risk Identification </li></ul><ul><li>Qualitative Risk Analysis </li></ul><ul><ul><li>Assessing likelihoods and possible outcomes </li></ul></ul><ul><li>Quantitative Risk Analysis </li></ul><ul><ul><li>Computer simulations; decision tree analysis; etc. </li></ul></ul><ul><li>Risk Response Planning </li></ul><ul><li>Risk Monitoring and Control </li></ul>
  7. 7. Identification of Risks <ul><li>Identifying all of the possible events or conditions that might occur and may negatively impact project performance </li></ul><ul><li>A brainstorming session with the project team can be a helpful way to ensure that all important risks are identified </li></ul><ul><li>Determining symptoms or warning signs that indicate when the risk is about to occur </li></ul><ul><li>Determining root causes of the risk </li></ul>
  8. 8. Risk Assessment <ul><li>This info. should be developed for each risk: </li></ul><ul><li>Description of risk </li></ul><ul><li>All the possible outcomes of the risk </li></ul><ul><li>The magnitude or severity of the outcomes </li></ul><ul><li>Likelihood (probability) of the risk occurring, and likelihood of each possible outcome </li></ul><ul><li>When the risk might occur during the project </li></ul><ul><li>Interaction of the risk outcomes with other parts of this project or other projects </li></ul>
  9. 9. Risk Assessment Matrix Risk Likelihood Severity Detection Difficulty When System Crash Low High High Startup Software Glitches High Low Medium Post-Startup Users Dissatisfied Medium Medium Low Post-Startup Hardware Malfunction Low Medium Medium Startup
  10. 10. Risk Analysis Tools <ul><li>Probability analysis </li></ul><ul><li>Decision tree analysis </li></ul><ul><li>Monte Carlo simulation analysis </li></ul><ul><li>Life-cycle cost analysis </li></ul><ul><li>Delphi techniques for consensus </li></ul><ul><li>Technology forecasting </li></ul><ul><li>Game theory analysis </li></ul><ul><li>PERT analysis </li></ul><ul><li>Sensitivity analysis </li></ul><ul><li>Expected value analysis </li></ul>
  11. 11. Reducing Risks <ul><li>Try to reduce uncertainties (collect more information, use more reliable vendors, design for easy production, don’t use leading edge technologies, etc.) </li></ul><ul><li>Try to reduce the severity of potential outcomes (purchase insurance, convince customer to share the risk impacts, train employees how to respond quickly, etc.) </li></ul>
  12. 12. Contingency Planning <ul><li>A contingency plan is an alternative plan used if a risk event or condition occurs. </li></ul><ul><li>Examples: </li></ul><ul><li>Having a backup supplier for a key material </li></ul><ul><li>Carrying a safety stock for a key part </li></ul><ul><li>Having an alternate distribution channel to send products to China (air instead of boat) </li></ul><ul><li>Having hurricane evacuation plans </li></ul>
  13. 13. Time and Cost Padding <ul><li>Padding is a commonly used approach to address risks, since it is very easy to implement and since it protects against most minor risks </li></ul><ul><li>Padding refers to inflating the original time or cost estimates for activities or for the project </li></ul><ul><li>Unfortunately, this leads to longer project durations and higher costs </li></ul>
  14. 14. Time and Cost Padding <ul><li>People will generally use up as much time and money as they are allowed (if you don’t use it you lose it!) </li></ul><ul><li>Student syndrome  if extra padding is built into activity time estimates, some people are likely to procrastinate getting started, and then the protection against risk is lost </li></ul><ul><li>Although padding can be useful in reducing the severity of risk, it can also lead to inefficiencies and waste </li></ul>
  15. 15. Expected Values <ul><li>A construction manager is trying to decide what size crew to schedule for tomorrow based on weather: </li></ul><ul><li> Weather </li></ul><ul><li>Probability: 10% 20% 30% 40% Expected </li></ul><ul><li>Alternative Nice Cold Rain Snow Value </li></ul><ul><li>Large crew $860 $710 $160 $-350 $136 </li></ul><ul><li>Med. crew 520 430 190 -120 $147 </li></ul><ul><li>Small crew 280 240 170 130 $179 </li></ul><ul><li>sample calculation : </li></ul><ul><li>Large  .10(860)+.20(710)+.30(160)+.40(-350) = 136 </li></ul>
  16. 16. Risk Management Exercise <ul><li>Nelson Mandela Bridge case (25 minutes) </li></ul><ul><li>Divide into small groups </li></ul><ul><li>Read case </li></ul><ul><li>Discuss the issues and answer these questions: </li></ul><ul><ul><li>How would you have identified the risks? </li></ul></ul><ul><ul><li>Using the table provided, discuss how the risks were addressed and/or how risks could have been addressed. Also, indicate any additional risks you can think of. </li></ul></ul><ul><ul><li>Indicate whether the risks listed are internal or external. </li></ul></ul><ul><ul><li>Describe how you would determine the expected values of the risks listed. </li></ul></ul><ul><ul><li>Do you think that risk was adequately managed in this project? Why? </li></ul></ul>
  17. 17. Uncertain Task Durations <ul><li>Probability distributions </li></ul><ul><li>Discrete, uniform, triangular, normal, beta, etc. </li></ul><ul><li>Most common way to consider task uncertainty is to estimate the most likely, pessimistic, and optimistic durations. </li></ul><ul><li>PERT analysis assumes a Beta distribution for each task </li></ul>
  18. 18. Estimating Task Times (with PERT) <ul><li>Activity duration estimates : </li></ul><ul><li>a=optimistic, m=most likely, b=pessimistic time </li></ul><ul><li>Expected task duration: </li></ul><ul><ul><li> T e = (a + 4m + b)/6 </li></ul></ul><ul><li>Variance of task duration: </li></ul><ul><ul><li> Var = [(b – a)/6] 2 </li></ul></ul>
  19. 19. PERT Example <ul><li>Task Pred. Opt. Most Likely Pess. T e Var </li></ul><ul><li>a -- 3 4 6 4.167 0.250 </li></ul><ul><li>b -- 2 3 4 3.000 0.111 </li></ul><ul><li>c a 3 3 5 3.333 0.111 </li></ul><ul><li>d a 2 2 2 2.000 0.000 </li></ul><ul><li>e b 4 6 11 6.500 1.361 </li></ul><ul><li>f b 3 4 4 3.833 0.028 </li></ul><ul><li>g c,d 1 1 2 1.167 0.028 </li></ul><ul><li>h e 4 4 4 4.000 0.000 </li></ul><ul><li>i f 3 5 8 5.167 0.694 </li></ul><ul><li>j e,g 3 6 10 6.167 1.361 </li></ul><ul><li>k h,i 1 1 2 1.167 0.028 </li></ul><ul><li> T e = (a + 4m + b)/6 Var = [(b – a)/6] 2 </li></ul>
  20. 20. PERT Example <ul><li>Use T e values for task durations on project network to compute slack values. </li></ul><ul><li>The results of the new computations still shows path b-e-j as the critical path, with an expected project duration of </li></ul><ul><ul><li>T cp = 3.000 + 6.500 + 6.167 = </li></ul></ul><ul><ul><li>Var cp = 0.111 + 1.361 + 1.361 = </li></ul></ul><ul><ul><li>StdDev cp = sqrt(2.833) = </li></ul></ul><ul><li>MS Project with 3 task durations </li></ul>
  21. 21. Goldratt’s Critical Chain <ul><li>Assuming that an activity duration is known leads to underestimating project durations </li></ul><ul><li>Because of this, people tend to pad their time estimates </li></ul><ul><li>This may result in the “student syndrome” </li></ul><ul><ul><li>What is that? </li></ul></ul><ul><li>This in turn leads to procrastination, which can then result in missing the finish date </li></ul>
  22. 22. Goldratt’s Critical Chain <ul><li>Add safety time buffers at strategic points in the project network </li></ul><ul><li>Safety time buffer at end of critical path is called a project buffer </li></ul><ul><li>Safety time buffer just before where noncritical paths feed into the critical path is called a feeding buffer. </li></ul>
  23. 23. Computer Simulation Analysis <ul><li>General purpose simulation software can model how many products flow through all the machines in a factory and on to the warehouse. This capability is much more than what is needed to simulate projects. </li></ul><ul><li>Monte Carlo simulation is much simpler type of simulation analysis that we can use to model the uncertainty of task durations and costs. </li></ul><ul><li>Crystal Ball and @RISK are two such packages. </li></ul>
  24. 24. Crystal Ball and Project Analysis <ul><li>Crystal Ball allows you to specify any type of probability distribution for each task. </li></ul><ul><li>You specify all precedence relationships. </li></ul><ul><li>It then “shoots” random numbers into your probability distributions to simulate thousands of completions of the project. </li></ul><ul><li>The result is a probability distribution of the total duration of the project, from which you can answer the what-if questions about how long the project might actually take. </li></ul>