With this concept in mind, Failure Time can be an added value method of estimating the exact buffer time needed for an individual expert. To estimate Failure Time the project manager must have historical data to reference for each individual expert. This also works for a team of experts that continually operate together (e.g. Elite Tile). The project manager takes the difference between the estimated time and the actual time to delivery of each task and records this data. Then the difference is divided by the estimated time to develop a ratio as a percent of difference to estimated time. This ratio as a percent is then run through a regression analysis which then gives a 99% cortile range of a more accurate failure time estimation.
Figure 1.1 shows how estimating buffer time in an arbitrary way gives the inevitable project completion time +30 or -30 minutes in project 2 and 3 respectively. This is assuming that no one in the project fails to hit the estimated time. Estimating this Buffer Time on Project 2 gives a total of 12.5% overage. This example is only a ½ hour over the initial project estimated time which is small when considered to a big picture. However, if this trend continued for a project that was estimated at 1920 hours (1 work year) the inefficiencies would be 240 hours or 6 weeks. There are not many organizations that can afford to have 6 weeks of wasted time on the books per year. Great value can be added to an organizations production schedule if historical data can be analyzed and more accurate Buffer Time can be estimated.
Using simple statically analysis a project manager can make a more accurate estimation of failure time and thus build a more accurate production schedule. The following figures show actual data taken from real projects as recorded by project managers. The statistical analysis of this data follows the datasheets.
Statically Analysis of TechXpress:
What we can take from this analysis is that the project manager can make a 99% accurate estimate that any given task estimated will have an actual completion time between 42.623% below and 83.136% above that of the estimated time. Considering the data, if the project manager wants to make absolute certain that the tasks will be completed the project manager will need to schedule 133% above the estimated time to accommodate the highest overage of Task 19. Considering this, the project manager can estimate Buffer Time at a more accurate level and eliminate the wasted time inefficiencies.
Statistical Analysis of Elite Tile:
What we can take from this analysis is that the project manager can make a 99% accurate estimate that any given task estimated will have an actual completion time between 11.3886% below and 23.0380% above that of the estimated time. Considering the data, if the project manager wants to make absolute certain that the tasks will be completed the project manager will need to schedule 33% above the estimated time to accommodate the highest overage of Task 21 and Task 11. Considering this, the project manager can estimate Buffer Time at a more accurate level and eliminate the wasted time inefficiencies.
Conclusion:
Considering the statistical analysis done with real world data Failure Time is an element that can easily be implemented into the daily scheduling of a project managers job. With this information project managers can eliminate some time inefficiencies by estimating the needed buffer time better. While there will never be an exact and 100% accurate way of estimating Buffer Time there is an effective way. With the evidence provided the thesis statement is proven correct.
