The topic of preserving, leveraging and organising historical data is gaining a lot of traction in the construction industry.
To me, this historical data is a veritable treasure trove of project and organisational knowledge that can be transformed into actionable data, instead of companies starting each project from scratch.
The need to capture this data is gaining impetus due in large part to this undeniable fact: the exodus of experienced construction workers shows no signs of abating, and there aren’t enough skilled young people coming in to take their place.
Data from the Census Bureau confirms our industry is greying — the average age of a welder in construction today is 55.
This has companies realising the time to start digitally capturing and using this data is while their key, experienced workers are still in on the job. With that, I’d like to explore five key areas with respect to capturing, building and using historical data:
- Best practices to develop and maintain parameter-based questionnaires
- Leveraging an estimate storehouse
- Building and maintaining rate libraries
- Templatising estimates
- Leveraging the data captured
In order for any estimate to be created, at some point an input of specific variables associated with a project will be required.
A parameter-based questionnaire will guide users through the input of those key variables. The questionnaires are designed and configured so that there are some inputs on the front end that lead to calculations on the back end that drive very detailed estimates.
A best practice would include, for instance, variables that allow for all potentially needed resources, QA/QC steps and worst-case productivities.
The estimators I’ve worked with prefer managing their data as “worst-case” productivities because they’d rather be conservative in some cases and plan for work to take longer than it may actually take.
This method may preclude being the lowest bid on a project, but if a project is awarded with a bid that includes a very aggressive or overly optimistic productivity rate, the contractor may end up losing money.
Once the user has inputted those variables and the results are folded into the estimate, those results can be edited at any time.
Using advanced construction estimating software, a team member can utilise different data fields to help organise and create cost item assemblies, almost like a production factory, and build out plans.
After the creation of a detailed estimate, the data generated can be organised by user, discipline, owner, agency, or the type of project — whatever best enables a company to leverage it. It can then be moved to what I’m calling an estimate storehouse, to be mined later when creating a new estimate.
All the estimate details have been fleshed out — crews, materials, productivity rates, etc. — and organised in a manner that best fits a company’s estimating process.
In our estimate storehouse, our line items are metaphorically “sitting on a shelf” ready to go, other than perhaps updating some quantities, or understanding what’s specific or unique about a project that would require some change to the variables.
The stored knowledge can simply be lifted and pasted into an estimate. This means the user is spending 100 per cent of available time editing and reviewing those variables, not hard-coding or typing them in, or examining what has previously been examined.
This knowledge capture allows a company to employ a best-practice approach to estimating. The ability to reach into a storehouse, grab the historical knowledge and turn it into actionable data saves both money and time spent on planning.
The key to any good estimate is a set of applicable rates — the cost of labour, equipment, materials, and so forth. By creating and maintaining rate libraries, a company can immediately access specific project costs when estimating and planning.
I suggest the “build-as-you-go” approach using the 80-20 rule when constructing a rate library. Instead of waiting to start inputting until 100 per cent of the materials a company could potentially use have been identified, perhaps focus on the 20 per cent of the material that might be used 80 per cent of the time and build a library of rate tables out from there.
Within the libraries, the rate tables can be organised by year, geography, union or non-union projects and other variables. The libraries contain a master list of nearly every resource that a project would require.
The software allows users to take into account job-specific issues that will impact these rates that can be filtered and imported into a working estimate.
Once an estimate is complete, the software allows for the comparison between a project and a rate library. Estimators can see what resources match what’s in the library, but maybe have been modified to fit a specific project, resulting in a new unit cost.
Everything built into the parameter-based questionnaire, estimate storehouse and rate libraries can be preloaded into an estimate template — a standard cost model by project type.
If a company has a cost model used for specific types of projects, everybody on the team should know how every cost combines and rolls up to different levels within the cost breakdown structure.
An estimate template also allows for everybody to know where to look for specific items and stay on the same page. All project team members begin with the same data and the same structure, and everything moves ahead in a very standardised way.
It also allows for an easier estimate review because team members have become accustomed to looking for specific things in specific places.
Templates can also be used as the basis for new projects. The estimate software will load a template that includes a company’s best-practices approach for whatever type of project is being started.
Leveraging historical data
The endgame in all of this data building, I believe, is benchmarking — the process that allows companies to validate estimates and detect and analyse trends.
I’ve worked with contractors who lament over being unable to go somewhere and see what it took to do the work, despite constructing plants, buildings, bridges, etc., for over 50 years.
Benchmarking can synthesise historical data based on known conditions or parameters. Digitally capturing and sorting all of this historical data is the only way companies can use truly sophisticated benchmarking analysis and put together a defendable estimate – one that ultimately puts a project on the path to success.
As the skilled labour exodus continues in construction, companies must start digging into their treasure trove of historical data. It will ultimately stem the tide of this construction brain drain and save both time and money over the entire life cycle of a project.
To learn more about InEight’s project cost management solutions, click here.
Rick Deans is InEight executive vice president of industry engagement.