Conceptual costing for a project involves estimating the cost of a project with very limited data. It is not required that a flowsheet be available. This method is best adapted to the use of basic cost data from a project using a similar process to the one being considered. For example, basic project cost data for chlorination of benzene might be used for conceptual costing of a project involving similar chlorination of methyl benzene (toluene). Other examples include, but certainly are not limited to, hydrogenation, sulfonation, oxidation, alkylation, and polymerization. In practice, several parameters might have to be included to achieve any degree of accuracy. For example, fixed capital costs for polymerization can vary widely; vinyl chloride polymerizes easily and ethylene polymerizes with difficulty. Here “polymerization” of itself cannot be classified as a definite capital cost. Conceptual costing may be used for the following:
· Ascertaining project profitability in its early development · Quickly checking bids from contractors and subcontractors · Estimating costs and profits for potential process licensing · Quickly assessing inquiries for increased product sales 1.11 FORECASTING
One of the primary tasks of a cost engineer is to make forecasts. Any project analysis requires forecasting at some level, whether it is only for a week or for many years into the future. The cost engineer is not merely asked to estimate the fabrication costs of new facilities. Responsibilities include assessing the benefits of upgrades and changes; making profitability analyses; making accurate schedule forecasts; and breaking down construction, startup, operating, and cumulative costs. The economic success of a project or the feasibility of a course of action all depend upon assumptions the cost engineer has made about coming events.
The forecast starts with an understanding of the present and is based upon experience. Both require sources of data. The best source of data, of course, is an engineer’s own company’s records and experience; the most common forecast models are based upon in-house history. The next best sources are the professionals and suppliers with other companies or organizations with experience on similar projects. If, however, a project is new, either as a technology or just to the company, published information is usually available if one knows how to go about collecting it. Some additional reading materials
and data sources are listed at the end of each chapter. Libraries, especially university and specialty libraries, are lucrative sources of information as is the Internet. For example, unlike many engineering societies, AACE International* maintains a large online technical library of cost engineering publications and data. Staff personnel at libraries and various government agencies can often suggest sources of information that are not immediately obvious. Although this fact in itself seems obvious, staff experts and information specialists are often overlooked by individuals conducting research outside of their familiar sources.
makes an incredible wealth of information available to project and cost engineers that is invaluable if used with care. It is vital to know that the source is legitimate. The Internet is open to anyone and as such, it has no quality control or veracity check. When using it, use it with caution and common sense.
One must also remember that published information varies widely in quality and applicability. It is risky to base a project decision on print-published or online information without firsthand knowledge of the limitations of the original data. And one must always remember that costs and conditions vary widely in different regions and over time. The only real safeguard that a cost engineer has is an understanding of the sources; it is one of the hallmarks of professionalism.
Forecasting has already appeared in the economic relationships that we have discussed. Rates of return and inflation rates have been assumed, and it has been further assumed that these rates remain constant for the length of the project under consideration. These assumptions may not be unreasonable with a short-lived project, but the longer a project stretches, the more tenuous these assumptions become. Often overly conservative estimates are as damaging to a project as is inordinate risk taking (project economics can be “over-engineered” as well as the project itself).
The most common techniques used in forecasting involve trend and regression analysis, which is a mathematical way of saying history matching.
Figure 1.3 shows four popular curves used in prediction techniques. Each has
its specific areas of applicability.
Computer modeling has become increasingly popular in the field of forecasting. Contributing factors to this increase include the increasing power and affordability of personal or desktop computers and the increasing skill with which business analysts are matching trends with mathematical models. The power extended to forecasters is enormous; parametric analyses that used to require months, and were thus unaffordable in quantity, can now be performed in seconds. All levels of risk can be assessed.
But there is a caveat. As discussed, all forecasts include assumptions. More than one mathematical treatment can be used to analyze the same data.
*AACE International, 209 Prairie Avenue, Morgantown, WV 26501, USA. Phone 1–304–296–8444 or 1– 800–858–COST. Internet: http://www.aacei.org.
Unpredictable changes in the economy, such as wars or oil shortages, create changes not addressed by models. In short, computer modeling is only a valuable tool when the modeler has a complete understanding of how the model works. Then, and only then, can the reliability of a model’s output be known. Once again, the company’s best safeguard is the professionalism of its cost engineer. Unfortunately, there is an increasing tendency for engineers and analysts to use computer software as a “black box” with little personal knowledge of the operational procedures and assumptions made within the “black box” and with unwarranted faith in the output.