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# Spm unit iii-risk-pert

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### Spm unit iii-risk-pert

1. 1. SPM-UNIT III RISK MANAGEMENT Prof. Kanchana Devi
2. 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. 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. 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. 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. 6. After Calculating Expected Duration Prof. Kanchana Devi 6
7. 7. PERT Network after the Prof. Kanchana Devi 7
8. 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. 9. Standard Deviation Prof. Kanchana Devi 9
10. 10. PERT With ‘SD’ Prof. Kanchana Devi 10
11. 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. 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. 13. Z- Value Formula Prof. Kanchana Devi 13  Te  is the Expected Date  T  Target Date  S  SD Z = T - te S
14. 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. 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. 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. 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.
18. 18. Histogram Prof. Kanchana Devi 18