Project Management - Part 7

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