You are here
The Measured Mile
Journyx has always known the importance of tracking time and money on a per-project basis. Using this data, it is possible to build up a backlog of projects that can be referenced for future endeavors. Whether they are successes or failures, knowledge of past projects can greatly enhance the odds of success with similar jobs in the future. However, comparing individual projects to other similar projects is only one way of using prior time and expense data. The other comes in the form of a project completed under normal working conditions; in other words, a “measured mile.”
For those unfamiliar with the concept, it works like this. Projects attempted under ideal conditions and those undertaken during adverse conditions may vary significantly in adherence to budget and schedule. To a certain degree this factor can be blamed on external conditions, however it is important to isolate those conditions from individual productivity. Therefore, by examining a project completed under “normal” conditions, it is somewhat possible to isolate external variables by comparing that project to a project taken on during a certain period.
So, as an example, let’s imagine that a stadium needs to be constructed in a city. Fortunately, a comparable stadium was built a few years back in a city with similar terrain and ecological conditions. No major catastrophes befell that stadium, manmade or natural, and so it could be described as having been completed under normal conditions. Thus, this project could be labeled a “measured mile.”
Conversely, the new stadium faces numerous problems. In addition to strikes by public workers in the early months, a swathe of terrible thunderstorms strike the area. Ultimately, the new stadium takes longer and costs more to complete than originally estimated. By comparing the time and cost to completion against the “measured mile” project, it clarifies more succinctly the effect that these adverse conditions had on the project.
The benefits for this type of data, particularly in productivity analysis, are fairly evident. It becomes possible, by measuring projects relative to a baseline, to understand what a “normal” completion schedule would look like assuming there are no complications. In this way, the more detailed the previous data associated with the project is, the more accurate a view businesses can get into future projects that may face issues. As always, greater insight yields greater results and a closer adherence to forecasted costs.