An estimating technique that uses a statistical relationship between historical data and other variables (e.g., square footage in construction, lines of code in software development) to calculate an estimate for activity parameters, such as scope, cost, budget, and duration.
This technique can produce higher levels of accuracy depending upon the sophistication and the underlying data built into the model. An example for the cost parameter is multiplying the planned quantity of work to be performed by the historical cost per unit to obtain the estimated cost.
Examples of Parameters
The parameters used to build an estimate will vary between industry and project. For example, the cost
of rebuilding a house might be based on:
- Number of bedrooms
- Garage/No garage
- Number of floors
of filming a promotional video might be based on:
- Minutes of footage needed
- Number of cameras
- Sound equipment required
- Audio visual equipment rental cost per day
- Day rates for camera operators, sound technician, actors etc.
Advantages of Parametric Estimating
- It is quick - because it uses a set of ready-made parameters it is a faster method of estimating then bottom-up approaches that estimate each individual item required for a project.
- Detailed information is not needed - Analytical estimating required great detail. For example, quantity surveyors estimate building costs from the bottom up using detailed plans to cost out number of electrical sockets, light switches, door handles, lengths of pipe etc.
Disadvantages of Parametric Estimating
The accuracy is dependent upon the quality of the model used, and whether there are historically costed parameters available. For example, it may be difficult to estimate a software integration with any accuracy if the integration is to a system and associated API that are not previously known to the software development team.