Managing With Data (or How to Get Your Processes to Talk to You)

On a cold winter day, I was riding with Mike, construction manager for the Midwest division of a top national builder. Mike wasn’t happy. 

The plan was to stop by several new, occupied homes to check on a problem, and with a heavy sigh he muttered, “It’s that worthless flooring again.” I asked him to tell me about it, and he launched into a tirade on how his division president had mandated the installation of the cheapest sheet vinyl flooring available in the kitchens, baths, and laundry areas of the builder’s entry-level homes. 

“Let me guess,” I asked, “Anything not 100% smooth is telegraphing through the vinyl, like a fastener not flush or a splinter off the lauan underlayment?” Mike nodded his head and in a loud voice proclaimed, “Splinter hell! You can see a nose hair trying to work its way up through this crap a month after installation!”

We visited three homes that day and I couldn’t dispute Mike’s evaluation. The vinyl looked like, well ... crap. You could see staple crowns and underlayment seams; even the smallest piece of debris not completely removed from the subfloor before installation was showing. 

In each case, the decision was made to bring back the installer to remedy the problem. Consider that response for a moment: once cabinets are in, trim installed, doors hung, and appliances placed, replacing sheet vinyl isn’t a matter of just rolling out a new run. You need the flooring company’s best installer to do this job, and he needs a helper and plenty of time, which won’t come cheap. And don’t discount the impact and inconvenience for the customer and how many neighbors, friends, and relatives they’ll tell about it. 

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I asked Mike how long the problem had been going on and how many floors had been replaced. He said it was more than two years and a replacement rate of more than 15%. 

So, what would it take to prevent this scary and costly cycle? Mike told me that simply moving up one grade in the vinyl could solve most of the issues. When I asked why the builder didn’t simply make that change, Mike laughed sardonically, “Oh sure, bring this up with Carl [the company president] again and get my head bitten off? What’s the point?” Mike was obviously frustrated and resigned to the situation. I just smiled and said, “I think we can fix this.”

Show Me the Money: How Data Can Affect Your Bottom Line

And, in fact, we did resolve the problem. We brought together a strong superintendent, the warranty manager, an accounts payable person, the rep from the flooring company, and one of their best installers. We pored over the historical data on warranty costs, extra trips, time loss, paperwork, plus supervision on both sides. After about 90 minutes, we reached an inarguable conclusion: Going up two grades in vinyl quality would eliminate the service issues, improve the look, and ultimately cost less. 

The one thing Carl always responded to was dollars. Once the data was put in front of him, he saw the bottom line and approved the change. Sales loved it, customers were happy, and the builder’s warranty department was greatly relieved at the burden lifted from their backs. 

This episode occurred more than 30 years ago, and I’ve seen similar scenarios play out countless times since then. Before I got involved, there had been a two-year battle over the vinyl flooring spec in this operation, with construction, warranty, and sales all strongly advocating for the change to a better, more robust product, with no success. All it took was simple data analysis to quickly change the president’s mind—no tears, no magic, no artful persuasion, no genius of any kind. Just indisputable data.

Over the next decade, we trained several hundred company staff from all departments and all levels in what we called “FQA—Field Quality Advisor,” where they learned both basic and advanced measurement and data analysis methods. The training period was two weeks, a month apart, with homework. It was quite an undertaking, but having five to 10 people in each division who understood how to find and use data made a profound difference in daily operations.

Yet the cold, hard reality of using data to guide informed decision-making is not a strong suit of our industry. I once delivered a keynote presentation to a group of home builder CFOs titled, “You Guys Don’t Count So Good!” (which I also wrote as a column for this magazine). The title caught their attention: Who does this former VP of Quality from Detroit think he is ... telling the numbers guys they are lousy at math?

Of course, I was trying to provoke them. Knowing that CFOs think they’re the only ones who know numbers, I challenged three of them to a dinner bet over who could do math problems in their head the fastest. Next, I provided a litany of examples explaining how most of the numbers they used fit the proverbial “driving while looking in the rearview mirror.” Then I beat them up for using only measurements of outcomes, generally ignoring the process measures critical to managing their business. 

I was somewhat surprised at the positive reception I received following this talk, presented just before “the big crash.” Still, I was never invited back ... though maybe because I won two of the three math challenge bets! 

Purchasing Data 

The story of the vinyl flooring spec change is the perfect setup for illustrating the importance of good data in terms of truly understanding the difference between bid price and total cost. 

I often refer to this difference as “True Total Cost” to emphasize the need to look deeper than what is typically done. The level-one vinyl was clearly the low bid. The upgrade to a level-two vinyl was absolutely the lowest total cost. Is there really any question about which to buy? Yet, until presented with good data, the right choice escaped the decision-makers. 

Of course, there are additional factors to consider for True Total Cost, such as delivery, quality, capacity, accuracy, variance, warranty costs, and schedule adherence, among others. And for each one we can develop an ongoing data stream—easy to do if you start tracking from the beginning, much harder after the fact. 

By listening to your processes and responding to their data outputs, you can truly begin to understand the difference between bid price and total cost.

Let’s be brutally honest, though. If I stopped by your office now, for how many of those factors could you quickly show me good, well-organized data? I’m asking not what you think or for an educated guess, but what you know factually, with the data to back it up. Only a small percentage of builders fully track this data, but those that do make consistently better decisions and find more profit. 

Trade Selection Data

The same principles apply to trade selection. During the height of the COVID-19 pandemic, it was hard enough for most builders to simply find sufficient labor, much less make a deeper examination of essential factors beyond bid prices. Although significant labor shortages persist, the current economy has improved the flow compared with the past few years. It is important to remember though, that rare is a company that builds more than 5% of all the homes in a given market; the vast majority build less than 2%, with a trade base to support it. 

Let’s say you are in a market with 10,000 annual permits and you build 200 of those homes—that’s 2% market share. De facto reasoning then says you use just 2% of the trade base. Thus, even if there is a trade shortage of 15% to 20%, you’re looking at what the late Stephen Covey called “a world of abundance” —that is, abundance if you qualify as a true “builder of choice.” 

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So, from that pool, how do you choose? If you use bid price as your sole criterion, you’re guaranteed to make errors on a regular basis. Some of those will be expensive, such as time and dollars spent on rework and variance before closings, or warranty and liability after a home is occupied; some will just eat away at you one bite at a time in the form of schedule delays. Then there’s the financial pain of warranty and liability, plus the agony of trashing your reputation in the marketplace. Is there any way to justify not maintaining a real-time database of all critical data points on your suppliers and trades?

Schedule and Cycle-Time Data

I have spent literally decades working on this issue, learning early on from the late Bill Pulte, founder of Pulte Homes, the essential role of “absorption of fixed cost.” Reducing variable cost is indeed important, but as described above, most of the focus is on bid price alone. Fixed cost is a different animal, where cycle time makes all the difference. 

Building and selling one more house without increasing fixed cost leverages capital investment and brings a higher net margin, but you can’t go very far with that, if anywhere at all, if your cycle time increases. 

The concept is generally understood conceptually, but not often seen in practice. Ask builders for cycle-time data and what you’ll typically get is the schedule as established on paper, not the actual schedule as delivered from the field. Even actual data doesn’t do much unless it’s broken down by segments, such as:

1. Contract to Permit
2. Permit to Dig
3. Dig to Start
4. Start to Mechanical Inspection
5. Mechanical Inspection to Final
6. Final to Close (or CO – Certificate of Occupancy)

You may break it up differently, but in the words of the late Brian Joiner, one of the original “Deming Disciples,” “If you get your measurements right, your processes will talk to you.” 

Starting with your next contract, track each element of your cycle time by house, using actual, no-tears data. Listen to your processes and respond to their data outputs. 

For most builders, recording the numbers looks easy on the surface, but is more difficult to do (culturally) than they expect. If your people, both internally and in the field, can’t be 100% brutally honest without repercussions, your data will be worse than bad—it will mislead you. That’s on senior management, so start there and assume it’s an obstacle until proven otherwise.

On-Site vs. Off-Site Data

I have been working since the early days of COVID on a comprehensive model for comparing true total cost of on-site versus off-site construction methods. Does that sound so hard? A good example is shifting from stick-framed roof systems that use rafters, ridge boards, purlins, posts, beams, and point loads descending throughout the structure to building with factory-built trusses. 

According to 2020 data from Home Innovation Research Labs, just 42.4% of new single-family homes have roofs framed using only trusses, and 29.4% represent hybrid roofs framed using both trusses and rafters built on site. 

When working through this calculation with several builders in recent years, I was stunned to discover the truss companies, while trying to sell their product, rarely provide more than a gross price for the required trusses delivered to the site. I saw almost no analysis of the material, labor, and especially time saved throughout the construction process, and not just for the roof itself. When we get all, or the great majority, of load transferred to the outside walls of a home, the savings on load bearing throughout the structure, including foundations, change the equation significantly. 

Only a small percentage of builders fully track this data, but those that do make consistently better
decisions and find more profit.

But in less than two hours using good measurements and available data, we converted the answer from a “No, trusses do not cost out,” to a “Yes, we’d be crazy not to move over to trusses.” Just this one element of off-site construction becomes quite involved. On one hand, we have fewer trips to the building site; on the other hand, we often incur the added cost of a crane to lift and place the trusses. We are just getting started, but with good data, we can determine the answer.

When It Comes to the Data You Use, It’s All About Value

The huge number of factors involved from start to finish make the on-site vs. off-site model far more complex than you may initially think. The time-honored engineering definition of Value = Benefit ÷ Cost is essential. As described above, measuring cost is difficult enough, requiring true total cost for both fixed and variable, and few do this well. 

Measuring—and projecting—benefit is even more difficult. Benefit can be to the consumer, the community, to multiple suppliers and trades, and to internal departments. With input from trusted advisers, I accomplished the goal of laying this model out as the basis for a calculation template. Yet, despite my efforts, at this stage it’s still too complex. Excess complexity makes it hard to understand. Lack of understanding results in mistrust. Mistrust kills even the best model. 

Now I am working on simplifying the model, asking the question of each element: Can we eliminate this element and still achieve accuracy? So far, the great majority of factors can’t be cut out. Tough choices, but as soon as it’s ready, you’ll hear it here first. (If you’d like to be a part of the beta test group, email me at scott@truen.com.)

We could cover additional examples of data needs and application in land development, sales and marketing, and finance and accounting, in addition to the four areas described above. But as big an advocate for using data as I am, it also comes with significant warnings. 

First, understand that in most cases, an estimate divided by an estimate is accurately described as mierda de toro. Be highly suspicious of ratios, especially the denominators. I love graphical presentation of data, but be wary of scale manipulation, both the chosen spacing and cut-offs, high and low. Even worse are graphs that have Y-axis scales for two different factors on both the left and right of a graph. Just forget these. By manipulating the scales, the providers can make the data look like anything they want. 

Finally, heed a lesson from my physics professor decades ago, who demonstrated, with data, how if the rate of power-plant expansion were to continue for the next 30 years as it had for the previous 30, every square inch of the U.S. would now be covered by power plants.

If you want to see improved margins, good data is essential. There’s no disputing the need for good data, nor the current reality that we’re not skilled, as an industry, at measuring and providing good data. Like the ills of cheap vinyl flooring, it’s high time we fixed that.  

For a free PDF of Scott’s “Preparing for the Downturn” and “Bridging the Margin Gap” column series, email your request with contact information to info@truen.com. You may reach Scott at scott@truen.com or 248.446.1275.