To do cost modeling properly, construction companies need to invest significant resources. In my career, I’ve spoken with almost every major general contractor in North America, and I’m confident in saying that no one has a truly efficient process for cost modeling today. While some companies have better processes than others—some even building custom in-house solutions—when you peel back the onion, you realize they are investing a ridiculous amount of resources just to get the data into their cost modeling solutions.
This inefficiency significantly impacts the preconstruction process. Most companies treat every cost modeling request from clients as a scramble to respond rather than establishing a repeatable process. That’s because the proper solution doesn’t exist today. In this blog, we will dive deeper into the problems with in-house solutions and other cost modeling tools. As we look at the effort to aggregate and normalize data it’s easy to understand why successful cost modeling solutions haven’t been possible before cloud-based estimating.
Cost modeling requires companies to aggregate estimates across their organization. Here’s the catch: you need to normalize this data, too. What does that mean? You need to properly attribute costs by asset class so that you can compare similar assets in the future. Additionally, you need to classify key data points in the estimate, such as area or number of beds, so that when these estimates are ingested into the historical database, this data can easily be leveraged to answer important questions. The current solutions are not designed to facilitate either of these processes easily.
There are essentially only two ways people estimate in construction: they use Excel, or they use a desktop application (like WinEst, Sage, Beck, or CostOs). That’s essentially it. (Okay, there are a couple of cloud providers like Esticom and ProEst, but neither is a fit for large commercial general contractors, so let’s ignore them.) It’s easy to understand why aggregating a bunch of Excel estimates is difficult. But why is it hard to aggregate estimates from desktop applications? Well, they are called desktop applications for a reason. A lot of companies just deploy the software on individual devices. That’s where the estimate lives until the file is moved to some company cloud folder. Basically, this is just like having a bunch of excel spreadsheets. This all needs to make its way to the cost modeling solution. Yikes!
Another way companies deploy desktop software is by having employees use the application through virtual desktops. This really sucks for the employee, especially when a VPN is involved. Good luck trying to work from home! When companies deploy software this way, they typically create a unique database for each region or office. This is great for estimating but not for aggregating cost modeling data. You’ll need data engineers to gather all this information and centralize it if you want every region to leverage historical data across your organization.
Even if a company is good at aggregating project data, normalizing that data is the next step to make cost modeling easy. Normalizing data for cost modeling purposes is insanely hard because most companies don’t have a single cost database with a standard WBS (Work Breakdown Structure) used on each project. If you have multiple regions with different databases, I can guarantee with 100% certainty that those databases have significant variances. Therefore, trying to use estimates from one cost database to another will be challenging. Even more importantly, project costs need to be normalized by asset class. Most companies create WBS using sort fields. I will write a whole blog about why sort fields need to die, but let’s just say that sort fields make estimating harder. Many projects get completed without a proper WBSs making separating costs by asset classes a manual exercise. Sort fields also can differ from project to project because it’s so easy to add new ones and ignore standards.
We are the most comprehensive cloud-based estimating solution that allows companies to have a single cost database while maintaining different regional opinions of what an item costs. Our predefined WBS tools make it easy for estimators to allocate costs by asset class. Also, all critical data points—such as area or number of beds—are included and useful in estimates. This means that when historical data is curated, it’s straightforward and reliable—no questions asked. Bottom line: if you estimate in Ediphi, there is no data aggregation issue, and your project estimates can be normalized quickly for apples-to-apples comparisons in your next cost modeling exercise.
