## Long Tasks

Another problem common to schedule network architecture is the so-called long task. What is "long" in this context? There is no exact answer, though there are many rules of thumb, heuristics. Many companies and some customers have built into their project methodology specific figures for task length that are not to be exceeded when planning a project unless a waiver is granted by the project manager. Some common heuristics of "short" tasks are 80 to 120 hours of FTE time or perhaps 10 to 14 days of calendar time. You can see by these figures that a long task is one measured in several weeks or many staff hours.

What is the problem with the long task from the quantitative methods point of view?

Intuitively, the longer the task, the more likely something untoward will go wrong. As a matter of confidence in the long task, allowing for more possibilities to go wrong has to reduce the confidence in the task. Can we show this intuitive idea simply with statistics? Yes; let us take a look at the project shown in Figure 7-13.

Path 1,0: 60 workdays

CRM Date 3/25

0,95

Path 1,0: 60 workdays

Probability Distribution i

B 1.076

B = Most Ukely Duration

Statistics of Iho Baseline Task

EvDüls

7%-over

Probability Distribution i

B 1.076

B = Most Ukely Duration

1.3B

Statistics of Iho Baseline Task

Slalislics of trie Baseline Task

■ Variance = 2Ù days-squared * Standard deviation s.i days

See Itie chapter that explains probability distributions for the loiimulas for variance and standard deviation for the Triangular distribution. Figure 7-13: Long Task Baseline.

In Figure 7-13 we see a project consisting of only one task, and it is a long task. We have assigned a degree of risk to this long task by saying that the duration estimate is represented by a Triangular distribution with parameters as shown in the figure. We apply the equations we learned about expected value and variance and compute not only the variance and expected value but the standard deviation as well. It is handy to have the standard deviation because it is dimensioned the same as the expected value. Thus if the expected value is measured in days, then so will the standard deviation be. Unfortunately, the variance will be measured in days-squared with no physical meaning. Therefore the variance becomes a figure of merit wherein smaller is better.

The question at hand is whether we can achieve improvement in the schedule confidence by breaking up the long task into a waterfall of tandem but shorter tasks. Our intuition guided by the Law of Large Numbers tells us we are on the right track. If it is possible for the project manager and the WBS work package managers to redesign the work so that the WBS work package can be subdivided into smaller but tandem tasks, then even though each task has a dependency on its immediate predecessor, our feeling is that with the additional planning knowledge that leads to breaking up the long task should come higher confidence that we have it estimated more correctly.

For simplicity, let's apply the Triangular distribution to each of the shorter tasks and also apply the same overall assumptions of pessimism and optimism. You can work some examples to show that there is no loss of generality in the final conclusion. We now recompute the expected value of the overall schedule and the variance and standard deviation of the output milestone. We see a result predicted by the Law of Large Numbers as illustrated in Figure 7-14. The expected value of the population is the expected value of the outcome (summation) milestone. Our additional planning knowledge does not change the expected value. However, note the improvement in the variance and the standard deviation; both have improved. Not coincidently, the (sample) variance has improved, compared to the population variance, by 1/N, where N is the number of tasks into which we subdivided the longer task, and the standard deviation has improved by 1/v N. We need only look back to our discussion of the sample variance to see exactly from where these results come.

Biiiitlnt v\

Palh t.O: 6D work days 3/23

C PM Data

\til

2/12

2/12

6= Most Li Kulv Duration

6= Most Li Kulv Duration

Probability Distribution of cach i-horl task i EKpoctod Value

avis 3/29 EV Data

Blnlistics Of the shorter lasks Task 1.1, ML - 1$, EV - 16. Vat - 1.63

las* i.i, ml =; is, ev - it. var ■ Task 1ML n BO. ev ■ SI .33. Vw - 2 Task 1 4t ML = 10. EV = tO.S7r Var = 072

Overall

• Sum □( all EV - 6.1 days (&ima Jt$ fifliftJjwe)

■ Sum of fill variances - fi.iS <Jey3-5Oueri0<J i74°i mtprWQ'2 (WiSJuipO

• Sianciard fleviaiion ■ s.gs ea^s (49% im^rov&i mmc

See Nik clip.viei dial eipiami probability dsflribUtions lor kirnni us l<v nsriAnca mm itairiarrJ cfavialicnioMhe Tnaiigular dislribulior..

Figure 7-14: Shorter Tasks Project.

[5]Brooks, Fredrick P., The Mythical Man-Month, Addison Wesley Longman, Inc., Reading, MA, 1995, p. 16.

[6]Brooks, Fredrick P., The Mythical Man-Month, Addison Wesley Longman, Inc., Reading, MA, 1995, p. 25.

Team LiB

## Project Management Made Easy

What you need to know about… Project Management Made Easy! Project management consists of more than just a large building project and can encompass small projects as well. No matter what the size of your project, you need to have some sort of project management. How you manage your project has everything to do with its outcome.

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