Every project has associated risks at the onset of project start up. These risks can be divided into three categories: business, technical, and integration. Most of the risks, which show up during the project’s life cycle, will not be known at the estimating and handoff stages of the project.
To assess the risk of projects prior to winning the job or at the start of it, contractors can rely on their experience of past projects or, better yet, have a process in place to rely on data based on multiple factors and degrees of freedom. Collectively, these risk factors are known as job porosity.
This article aims to explain and show the measure of job porosity as well as describe the data-based modeling to help contractors build their project’s porosity factors.
Construction Project Process
Given the number of challenges in construction, a common perception is that every job is unique, and because of this, it is difficult to control and predict project outcomes. However, by collecting and analyzing the data between three independent data sources — estimating, field, and accounting — a predictive model for project outcomes has been developed.
A construction project takes such inputs as workforce power, money, and material and transforms them into a built structure with some level of time, cost, quality, and safety. In between the inputs and outputs is the construction process, which transfers the inputs to the outputs.
What makes each job seem unique is the lack of data and study within that process. Each job has inherent characteristics that will influence how the construction process will unfold; by knowing these characteristics in your company and using them to quantify the outcome, the project’s financial outcomes can be predicted even before the project is awarded.
Measuring Job Porosity
The owners or executives of construction companies with more than 30 years of experience can share war stories, usually tinged with emotion, with plenty of examples of bad jobs and good jobs, explaining what happened before, during, and after those jobs. Through their experiences, their wisdom is the best input to the estimating process, padding jobs that have certain conditions and shaving others based on where they know the company can take the risk. These conditions are known as the wisdom factors.
The job porosity factors very often match these wisdom factors; however, by adding data, the job porosity indicator will make the risk analysis based on data factors. As such, the factors can be weighted (based on their true impact) and studied for relationships.
Whereas wisdom may say a certain factor matters, the data may provide more context as to where the factor matters in certain conditions or when it is confounded with other factors, as explained later.
To develop these data-based factors, MCA, Inc.’s Research and Development department set out to investigate if job porosity could be predicted and quantified. After studying data from several companies that have applied Agile Construction®, key factors that represent pores in a job were identified, resulting in leaky profits.
This starts by identifying projects that have profit fade, which is defined as not making the estimated gross profit on a project. So, if a project’s estimated gross profit margin was 15% and the project finished with a 10% margin, then there was a five-point fade in profits.
All companies have profit fade and gain on their projects, and as long as the net is positive, the contractor stays alive another year. However, job porosity research identifies common characteristics associated with the fader projects so they can be predicted even at bid time and mitigated on an awarded job.
Exhibit 1 shows a schematic of the concept of job porosity, where a company’s project delivery system has certain characteristics that allow projects to fade based on given layers/conditions. Depending on if, and how much of, a job contains a certain factor, the job porosity factor predicts likelihood of fade. Common characteristics of project fade (job porosity factors) are explained next.
It is critical to scrutinize the data source and quality prior to looking for job porosity in your company. Contractors cannot pinpoint their true cost drivers, such as which cost or labor codes drive project outcomes, unless they use a process for measuring job productivity, such as ASTM E2691-20.1
Without a reference point independent from estimating, and progress measurement independent from accounting (labor hours), the true variation in performance is not measurable. There may be data available that shows variation, but the underlying source of the data does not reflect performance.
Without those independent measures, what would stop the labor from moving hours from one labor code to another or from moving costs from one cost code to another when they run out of hours (or cost) on the planned codes? It happens all the time and leads to unreliable data, and therefore, explains why job porosity cannot be studied.
Common Job Porosity Factors
Estimating Misses
When a job goes south, how often are you in the crossfire between “it was a bad estimate” and “they’re messing up in the field?” How do you know the right answer? Without a third reference point between estimating and accounting, it is hard to stop the blame game. So much can change from the estimate to the jobsite conditions, but the middle ground should be based on a plan (a Work Breakdown Structure (WBS) is recommended) that the project team can reference, whether the gap is to the estimate with all of its assumptions or to the planned job conditions.
Most jobs are bid using some manual of labor units that have inherent assumptions about productivity and job conditions, but that reference point is often lost or never passed on to the project team running the job. A module of Short Interval Scheduling® has been developed to measure estimating misses, using the categories of gaps identified through job review meetings, project audits, and postmortems over the years. Exhibit 2 shows a sample of the categories that are being collected in the estimating misses module.
Capturing this data is the first starting point of identifying porosity; if any of these conditions are known at the time of project startup, then their impact on job outcome can be quantified.