The ability to manage large scale project costs has a direct bearing on the risk profile presented to both the contractor and the end-user, especially where the Government is the customer. Managing and controlling costs effectively requires good decision-making at the earliest possible stage of any project, however gathering accurate cost and performance data to base these decisions upon is highly problematical. Nevertheless, by emphasizing cost control at the very earliest stages of a large-scale project, decision-makers can put themselves in an optimal position to control both the total costs and the business and operational risks associated with project deliverables.
Estimating what costs "should be" is essential to providing a firm baseline for project forecasts. There are software solutions for managing cost estimation and performing sensitivity analysis on differing project component alternatives, however many of them possess fundamental flaws in their approach. Parametric cost estimation mathematics is the same, however the raw cost data sources must be thoroughly considered before the results can be relied upon.
For instance, consider a cost estimation solution which relies on "live" cost data created by contacting vendors with their pricing for supplying production components. This exercise will produce a range of price data; however it is typical for the lowest pricing bid to be selected as the cost data to be input into the parametric cost estimation solution. This leads to underestimation within the cost estimation software and builds in heightened risk of overshooting on any proposed project budget.
A further example of how subjective, misleading cost data can be introduced into a parametric cost estimation model is where potential vendors are solicited for pricing; however the price selected is based upon a loss-leader. By loss-leading on price the vendor hopes to gain other, more profitable business, however the price is typically unsustainable in an isolated context. Again, this method of acquiring hard cost data for parametric cost estimation introduces under-estimation and builds in the potential for project failure due to cost overrun.
Identifying the "Should Be" cost is crucial when considering whether to make or buy project components. For instance, the "Make Buy" decision to retain component manufacture in-house or to contract the work out, will have serious repercussions on the cost of acquisition, however the cost of acquisition may only amount to 20% of the total cost of ownership of the project. A further example of how any cost estimation solution must be capable of handling decision-making within the holistic context of a project is found in the "Design Alternative" decision. Without accurate cost data, how can a project decision-maker decide on whether to proceed with the proposed component or system solution called for in the initial project design, or should they utilize a different component/system which will do the same job, for hopefully reduced cost but departs from the original design?
The answer is that without highly reliable cost data, none of these decisions can be taken with a high degree of confidence. Any cost estimation solution must be able to take into account the context within which the cost data is being applied as well as the quality of the source material from which estimation data is derived.
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Article written by Lawrence Reaves, writing on behalf of http://www.galorath.com - providing time-saving estimation tools for every business environment.
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