Create a fact-based filter for project selection and a data-driven mark-up policy that maximizes profitability.
This article is the fourth in a series on data-driven decision-making in construction. The first article laid out the rationale for data-driven decision-making in the construction industry, while the past two discussed data-driven operations and business development, respectively. This article addresses the role of data in estimating and pricing decisions. The final article in the series is a capstone article that will pull together all of the pieces to present a case study on running a data-driven construction firm.
Construction is a competitive business. Especially in hard-bid markets, contractors compete fiercely for every project. The price the market will bear is sometimes determined by a few hundredths of a percent.
Competition in the construction industry, however, should not be about winning projects; it’s about making a profit. In the construction industry, that profitable competition begins in estimating and pricing work.
The role of an estimator is not simply to bid work, prepare quantity takeoffs or manage subcontractor outreach and communication. No, the role of an estimator is to help his or her firm acquire profitable work.1 FMI’s 5-S Estimating Model (Exhibit 1) highlights the key elements of a best-in-class estimating department, which are:
- Strategy and Alignment: How well does our project selection process align with the overall corporate strategy?
- Structure and People: Does our estimating team function as a team with the right mix of business acumen, negotiating and selling skills, and technical knowledge?
- Standard Processes: Does our estimating team have standard processes that maximize time spent on high-value activities?
- Systems and Technology: Does our estimating team effectively and consistently use available technology?
- Subcontractor and Vendor Relationships: Have we developed strong subcontractor and vendor relationships that we can leverage to create an advantage during project pursuits?
Running through this model is a significant amount of data that, when analyzed properly, greatly enhances an estimating department’s ability to acquire profitable work for the firm. The focus of this article will address answers to two key questions:
- Should we bid this job?
- How much profit should we build into our bid?
Answering these questions results from analyzing two primary sources of data:
- Past project performance to inform project selection criteria
- Bid data to inform the correct mark-up policy, given a set of bid criteria
Data-Driven Decisions in Project Selection
Data-driven firms look to past performance to understand what an “ideal” project looks like. While no two projects are alike, analysis of past project performance, as outlined in the seminal article in this series, uncovers performance trends around where a firm performs well and where it struggles.2
Data-driven firms have robust and structured go/no-go processes that align the estimating team with the strategic direction of the business and mitigate risk exposures to projects outside the firm’s “wheelhouse.” The first step is crunching the numbers to define what projects fit. This analysis creates the first filter to use when screening new projects. While the go/no-go process is not necessarily an absolute ruler on pursuits, it is an effective screen to know whether to pursue the job, and if so, with how much effort. Many firms may still decide to “go” on a job they are not excited about but not make a significant investment of effort. They price the work high and essentially throw their hat in the ring while guarding against estimating busts.
Without an effective, data-driven go/no-go process, your results may look more like the company in Exhibit 2. By analyzing cumulative gross profit over the past few years — sorting jobs from most to least profitable — you can see that this company achieved 90% of its gross profit with only 15% of its projects. The next 75% of projects contributed only an additional 30% of gross profit, of which the final 10% of projects gave back 20% of gross profit. Can you imagine the impact better project selection and execution would have on this business?
To be fair, no company can operate without a few underperforming jobs — there are too many variables at play to achieve that level of perfection. That said, there is no reason not to try to eliminate underperformers. While estimating is only one aspect of project performance, it is the front line in the fight against underperforming projects.
Data-Driven Decisions in Optimizing Mark-up Policy
Project selection is the first filter. It answers the question, “Should we pursue this job?” If so, the next question is how much profit should we build into our bid to maximize profitability? Not all jobs are created equally when it comes to potential profitability. Just about every business has a “sweet spot” when it comes to targeting projects. Part of the answer for this question comes from the analysis outlined in the last article of this series.3
- What does an “ideal” project look like for us?
- How do we target clients who create those project opportunities?
The rest of the answer comes from bid data analysis, which highlights where a firm should be aggressive, where it should be conservative, and where it might be leaving money on the table. To analyze past bid data, a firm needs to collect and analyze the following data:
- Your total contract costs for each project that you bid
- The lowest bid for each of those projects
Let’s start with the analysis required to understand what the overall mark-up policy should be. To answer this question, you need to quantify how different mark-up percentages would have changed the result of each project that you bid. For example, if you won 100 projects with a 4% markup, how many more would you have won at 3% given your cost for each project and the lowest bid? Next, compare how profitable that decision would have been to the baseline of profitability at a 4% markup. When you aggregate the results of the analysis, they yield a graph similar to Exhibit 3, which reveals a profit-maximizing markup of roughly 4% for this sample company. The company ends up winning fewer projects but makes more money as a result.
Much like the earlier analysis of project selection, this analysis adds much greater value when determining the right mark-up policy based on bid criteria. These criteria create categories of projects for which there is a unique and optimal markup to maximize profitability within that category. Depending on the project specifics, different markups result. For example, you may find that you only want to win a project with 10 or more bidders if you have the lowest number after a significant markup of 8%. Alternatively, you might find that you consistently underbid the competition by a large margin on small projects.
In practice, using the three example bid criteria to conduct analysis may result in three separate mark-up percentages for an upcoming project. Making the final call will come down to old-fashioned, managerial decision-making based on these analyses.
Applying the two analyses above to your organization creates a fact-based filter for project selection and a data-driven mark-up policy that maximizes profitability for the firm in project pursuits. Obviously, a firm must also understand its true costs and execute in the field to ensure successful project outcomes. Nonetheless, sound project selection and pricing approaches are essential first steps toward superior performance.
Rick Tison is a consultant with FMI Corporation. He can be reached at 919.785.9237 or via email at firstname.lastname@example.org. David Madison is a consultant with FMI Corporation. He can be reached at 919.785.9213 or via email at email@example.com. Tyler Paré is a consultant with FMI Corporation. He can be reached at 813.636.1266 or via email at firstname.lastname@example.org.
1 Clancy, Mike. Estimating for Advantage: It’s a Hard, Hard, Hard (Bid) World. FMI Quarterly. July 2010.
2 Tyler Pare, David Madison, Rick Tison. “Deconstructing Data.” FMI Quarterly, 2014 Issue 3.
3 David Madison, Tyler Pare, Rick Tison. “Data Driven Business Development.” FMI Quarterly, 2015 Issue 1.