Improving Margins and the Exit Multiple with Smart Tech In PE

I'm Bruce Sinclair, and this is the Private Equity Digital Transformation Show, where we talk about the next big thing in value creation. This episode is brought to you by Digital Operating Partners, creating enterprise value by leveraging the smart mega trend. More details, go to digitaloperatingpartners.com.

Hello, this is Bruce Sinclair. Welcome to the Private Equity Digital Transformation Show. Well, I have another mini episode for you. This one is on how to improve margins and the exit multiple with smart digital technology.

Margin improvement is old school and effective because it's low risk, as compared to other value levers like revenue growth, for example. Low risk because it's under control. For many years, and probably still today, margin improvement has been popular not because it has the greatest impact on EBITDA, because it doesn't. In fact, it's the least impactful of all the value drivers.

But popular because it's financial engineering. Who doesn't love a good spreadsheet? Partners love spreadsheets. And as opposed to understanding, say, a manufacturing line, spreadsheets are relatively straightforward.

But the traditional tactic of cost cutting can only get you so far before it starts backfiring. And these days, there's not a lot of fat or cost to cut. This leads us to operational efficiency. And one manifestation of this, at least for industrial and manufacturing, is predictive maintenance.

That is, using AI to predict when things are going to fall apart before they fall apart. That's the first topic we'll cover here. The second topic is multiple expansion. Regarding EBITDA impact, multiple expansion is high, sitting on the other end of the spectrum for margin improvement.

And unlike margin improvement, which is pretty black and white, multiple expansion is a game of perception, usually. Here, I'll introduce the three ways that smart digital can move this perception needle. They are the digital re-rate, making structural changes to the company, and the third one, the one we'll cover here, is developing and deploying novel business models. Without smart digital, this third one is no easy task.

In fact, it's no easy task with digital either, but at least it's prescriptive. That is, we know how to make it happen. How? Step 1.

Model how the product's customer gets paid based on their KPIs. Step 2. Measure the variables of the KPIs with data from sensors. And step 3.

use the data in the monetization model to quantify how much the customer gets paid. This has two advantages. First, it's the definition of value-based pricing. So there is that.

And two, what we're talking about here, it's a platform upon which new business models are invented. And since it's KPI-based, it's recurring too. And who doesn't like steady recurring revenue, let alone increase customer loyalty and a lower barrier to sale. I'll tell you who likes it.

The next buyer of the company paying the higher multiple. These are numbers 8 and 9 of 10 in this series of smart value creation techniques corresponding to the 10 value levers and drivers These two parts join the episodes on revenue growth drivers namely customer retention market share expansion, product expansion, market expansion, and customer expansion, and joining the value levers of pricing optimization and buy and build boosting. And the last one coming up next is debt management.

All fun stuff. On a last note, if you prefer to read rather than listen to these solo episodes, I have a smart digital value creation LinkedIn newsletter where I put most of these solo episodes in the form of articles. To find the newsletter, get the link from the show notes on digitaloperatingpartners.com or just connect or follow me on LinkedIn and the newsletter should be visible.

And with that, on with the show. Margin Improvement with Predictive Maintenance Revenue growth has more impact on enterprise value than margin improvement. It's a fact. For a mid-sized company with 36% gross margin and 70% profitability, a 1% increase in revenue results in a 6% increase in EBITDA.

That is, compared to a 1% reduction in variable costs and fixed costs, result in a 3.8% and 1.1% increase in EBITDA, respectively. Having said that, increasing profit margins is still a go-to private equity move because 1.

it's old school, tried and true, and 2. it's more controllable than growing revenue. Why? Because reducing costs is in your control, so you know what you're going to get.

But with revenue growth, you can't entirely predict what the customer will do since they're out of your control. It's more like pushing rope than pulling a lever. Margin improvement comes in many different flavors. Some are an indirect consequence of revenue growth, and others are more direct in nature.

Indirect revenue drivers, all of which I've discussed here before, include customer retention and customer expansion, which both lower customer acquisition costs, product expansion into higher margin products, market expansion into higher margin markets, and pricing optimization, when it's raised, and it's always raised, naturally expands margins. However, when most pros talk margin improvement, they mean direct activities such as cost cutting and increasing operational efficiency.

The latter is where I'll go here. That is, how to use smart digital to increase operational output while using the same resources. Choosing which operational group to make more efficient depends on the company's KPIs and its strategic objectives. Generally, this points to the operations that make the company money.

If the company makes things, the cost of goods are lowered. If the company services things, the cost of service is lowered. If the company operates things, the cost of operations is lowered. If the company sells things, the cost of customer acquisition is lowered.

You get the point. As an example, consider predictive maintenance as used in the manufacturing and industrial sectors. Predictive maintenance reduces the cost of manufacturing, for which the cost of goods is a good measuring stick. But there are others.

Smart digital is behind two recent trends that are reducing maintenance costs. The first is moving from on repair to remote repair And the second is the transition from proactive to preventative to predictive maintenance Predictive maintenance uses AI to predict when a piece of equipment, or a machine, or any type of asset will fail. To maximize asset utilization, a subset of operational efficiency, the goal is to only stop and fix assets during scheduled maintenance periods.

Anything else is cost disruptive. For example, if a manufacturing line is scheduled to stop for maintenance on the first day of each month, but we predict a piece on the line will go down in the middle of the month, well, we schedule its repair for the beginning of the month it's predicted to fail in. By fixing the problem in advance during the planned downtime, we reduced unplanned downtimes, the nemesis of manufacturing margins. In businesses where maintenance has a meaningful impact on margins, we can use artificial intelligence to predict when assets will fail in advance of any noticeable sign of a problem.

By using smart digital to prevent unplanned downtimes, we increase the company's operational efficiency to improve its margins, or a relatively low investment in tech. So how is this done? To make predictions about asset failures with AI, we start by training a prediction model on the sequence of events that led to the failure by collecting the data corresponding to that failure. This data originates from sensors on the asset and is transported by the Internet of Things to the model generally constructed in the cloud.

Once our failure prediction model is trained by past failures, we can pattern match the incoming data with the data represented by our AI model. If there is a close enough match between the new data and the failure signature, a failure prediction is made with a certainty proportional to the closeness of the match. Deploying Data-Driven Business Models for Multiple Expansion Generally considered the longest of the value levers, multiple expansion can be influenced both indirectly and directly.

Revenue drivers and pricing optimization indirectly elevate the beta multiple by producing growth. Similarly, margin expansion by improving profitability, buy and build by increasing company mass, and debt management by throwing off more free cash flow also indirectly raise the exit multiple. Since I've discussed these value levers and drivers before, let's look at how to use Smart Digital to improve the exit multiple directly. The first way to directly increase the multiple is from the digital re-rate.

a premium associated with being digital. If a non-digital company is transformed into a fully digital company, its multiple comp would be that of a tech or a software company. Accordingly, a smart digital transformation re-rates a company's multiple commensurate with how digital they have become. The further along the transformation, the greater the digital multiple bump.

Implementing the structural changes needed to produce digital products is the second way to directly grow the multiple. The development of intangible assets such as proprietary IP and the establishment of a digital team to continue developing these assets fundamentally change the nature of a non-digital company, and as such, this progress is rewarded with a higher multiple at exit. Here I focus on the third way to directly drive multiple expansion the deployment of novel business models The source of all smart digital value creation is proprietary data the same type of data that has made big tech companies into the most valuable companies.

But instead of capturing proprietary customer data, we capture proprietary monetization data upon which new business models can operate. Capturing monetization data shows us how customers make money with our product. This profound capability enables us to price our products properly, the true definition of value-based pricing, and to create data-driven business models that better interface with the business models of our customers. The better the fit, the lower the monetization friction.

A key to developing a new business model is to identify the right KPI to base it on. A simple but illustrative example is the power-by-the-hour business model developed by jet engine maker Bristol Sidley and deployed by GE and others. The insight is, airline carriers don't want the weight of jet engines on their balance sheets. Instead, they would rather just pay for power.

This service business model couples to the most important airline carrier KPI, miles flown per filled passenger seat. To enable this, the proprietary monetization data collected by jet engine makers is engine use time. This proprietary monetization data results in a win-win for both parties. The buyer only pays for when the engine is being used to make money, and the seller earns more money by deploying a recurring business model.

Collecting proprietary monetization data enables the development of novel business models that until recently were impossible to deploy. Moving from one-and-done product sales to sales that recur to continuously generate revenue are rewarded by the next buyer paying a higher EBITDA multiple. So how's this done? Once the right KPI is identified, the product is instrumented, that is, sensors are added, to collect the corresponding KPI data.

This data is transported by the Internet of Things over the OT network to the IT network to software running on a server hosted in a nearby data center, also called the cloud. There, the price the customer pays is computed by inputting the newly arrived data into the business model. The outputted price is then transported by the internet to the company's billing system, posted on a server in another data center. Well, I hope you enjoyed the show.

For notes and links, go to digitaloperatingpartners.com. Contact me if you'd like to discuss our consulting firm's services or joining us, whether they're becoming part of our technical ecosystem or one of our digital operating consultants. That is, if you're ICIP certified.

And contact me if you know of a traditional private company for sale. One with EBITDA greater than $30 million and a business or products that seem like a perfect fit for digital transformation. And also contact me if your firm may be interested in raising the standalone digital transformation fund. We'd crush it.

I'm your host, Bruce Sinclair. Thank you for listening. Until next time, may your path to digital transformation be a successful one.