Spm unit iii-risk-pert
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The document discusses the PERT technique for risk management. PERT requires three estimates for each activity - optimistic, most likely, and pessimistic time estimates. It uses these to calculate an expected duration for each activity. Standard deviation can then be calculated for each activity based on the range between optimistic and pessimistic estimates. This allows calculation of probability of meeting or missing target dates by determining z-values and converting them to probabilities using a provided graph. Monte Carlo simulation is presented as an alternative technique that involves repeated random sampling to compute project completion times under uncertainty.
Spm unit iii-risk-pert
- 1. SPM-UNIT III RISK MANAGEMENT Prof. Kanchana Devi
- 2. PERT Technique Used to evaluate the effects of uncertainty CPM & PERT are similar CPM requires Single Estimate PERT requires Three Estimates 2 Prof. Kanchana Devi
- 3. PERT Three Estimates are Prof. Kanchana Devi 3 Most Likely Time The time we would expect the task to take under normal circumstances.“ m” Optimistic Time Shortest time in which we could expect to complete the activity, barring the miracles.“a” Pessimistic Time Worst Possible time. “b”
- 4. Expected Duration Prof. Kanchana Devi 4 PERT Combines the three estimates to form a single expected duration, te Formula for te is te= a+4m+b 6
- 5. Calculate the expected duration Prof. Kanchana Devi 5 Activity Optimistic(a) Most Likely(m) Activity Duration Pessimistic(b) A 5 6 8 B 3 4 5 C 2 3 3 D 3.5 4 5 E 1 3 4 F 8 10 15 G 2 3 4 H 2 2 2.5
- 6. After Calculating Expected Duration Prof. Kanchana Devi 6
- 7. PERT Network after the Prof. Kanchana Devi 7
- 8. Activity Standard Deviation Prof. Kanchana Devi 8 S = b-a which gives the degree of uncertainty 6 The activity standard deviation is proportional to the difference between the optimistic and pessimistic estimates. Can be used as a ranking measure of the degree of uncertainty or risk for each activity
- 9. Standard Deviation Prof. Kanchana Devi 9
- 10. PERT With ‘SD’ Prof. Kanchana Devi 10
- 11. Probability of Meeting or Missing Target Date Prof. Kanchana Devi 11 The PERT Technique uses the following three step method for calculating the probability of meeting or missing a target date: Calculate SD of each project event Calculate the Z value for each event that has a target value Convert Z values to a probability
- 12. Prof. Kanchana Devi 12 Note: To add two Standard Deviations we must add their squares and then find the square root of the sum. The SD for event 3 depends on the activity B. The SD for event 3 is therefore 0.33 For event 5 there are two possible paths, B+E or F. The total SD for path B+E is √(0.332+0.502) = 0.6 and For path F is 1.17 SD for event 5 is therefore the greater of two 1.17
- 13. Z- Value Formula Prof. Kanchana Devi 13 Te is the Expected Date T Target Date S SD Z = T - te S
- 14. Calculate Z value –Event ‘4’ Prof. Kanchana Devi 14 (10-9.00)/0.53 = 1.8867 A Z-Value may be converted to the probability of not meeting the target date by using the graph given below
- 15. Converting Z values to Probabilities Prof. Kanchana Devi 15 A Z-value may be converted to the probability of not meeting the target date by using the graph. Eg: The Z-Value for the project completion (event 6) is 1.23. Using graph this equates to a probability of approximately 11%, that is, there is an 11% risk of not meeting the target date of the end of week 15.
- 16. Monte Carlo Simulation Prof. Kanchana Devi 16 An alternative to PERT Technique MC Simulation are a class of general analysis techniques that are valuable to solve any problem that is complex, nonlinear or for some uncertain parameters. It involves repeated random sampling to compute the results. Advantage: Repeated computation of random numbers - easier to use this technique when available as a computer program
- 17. Steps – MC Simulation Prof. Kanchana Devi 17 Step 1: Express the project completion time in terms of the duration of the n-activities xi,i=1,n and their dependencies as a precedence graph, d= f(x1,x2,….xn). Step 2: Generate a set of random inputs, Xi1,Xi2, …Xin using the specified probability distributions. Step 3: Evaluate the project completion time expression and store the result in di. Step 4: Repeat steps 2 and 3 for the specified number of times. Step 5: Analyze the results di, i=1,n; summarize and display using a histogram.