However, many times in the urgency to complete the project and prepare for the next, much of this valuable data remains unorganized and inaccessible. Ideally, companies would compile this cost data into a form that they could then update on an ongoing basis and use to better forecast future project costs.
I recently worked with a major food industry company to help organize its data into a comprehensible, useful platform. There were many obstacles in this exercise such as compiling a wide range of data from multiple sources and software platforms, including the client’s own proprietary software.
Our process for setting up the initial study began with defining parameters, including items such as cost per square foot, CFM, ton, and linear feet. Cost parameters were necessary to define in order to set a quantifiable standard unit of measure. Further definition was created by classifying the data by project type, location, complexity factors, and unit of operation (such as room type or system type).
Once categorized, the data was then extrapolated and analyzed to baseline cost by review of location factors, inflation, scope of work by inflation and location, to one location. This study provided the client the data in both table form as well as charts to visually depict costs and how the varying markets influence costs. In addition to the study, it was important to create a new process for benchmarking historical project data. Fifteen example projects were chosen by the client because they best reflected the scope and cost data necessary to derive the most accurate results.
The client had its own unique work breakdown structure that lacked consistency across disciplines and divisions. From this exercise, we were able to assist and compile a standard work breakdown structure to make this data extraction more linear across departments and easy to access in the future. Steps we took to be sure that the system was thoroughly constructed included:
First, we provided the client with a usable, easily accessible Excel tool to help predict and estimate future projects based on multiple parameters. Included in the tool were construction procurement methods and influences, pre-identified project costs, tax incentives by location, and predicted cost of change.
Construction procurement methods were noted and grouped.
Construction procurement influences listed included CM as agent, CM at Risk, design build.
Project costs associated with additional capability/flexibility pre-investment (such as anticipating the infrastructure needs of future expansions) were identified and calculated separately.
Tax incentives for process equipment and infrastructure varied due to state and local government efforts to attract more business to build and expand in their areas providing more jobs; these were calculated by region.
Finally, the cost of change on projects, including change orders and field change orders, was anticipated.
The ability to more accurately forecast project budgets allows clients to better allocate and forecast their own capital spending plans to best leverage the market and net higher gains. This information also helps them better determine a project internal rate of return (IRR), which is a driver for project/investment to result in future profits after the initial investment.
Other factors in this study that were incremental to achieving useful data were the ability to accurately apply inflation and location factors to this data in order to normalize it and apply it to other regions of the United States.