CHAPTER TWO

CHAPTER TWO

We live in the age of the technology hype cycle. It’s almost deafening as each high tech takes its turn cresting in hype until its successor takes its place. The storyline is always the same: First the tech is worshiped, then its powers are described as if they were science fiction and finally some of the tech applications prove themselves to be practical and capable of creating value and sometimes disruption.

With financial engineering hardly moving the needle and traditional operating improvements becoming commoditized, it’s worth it for private equity to consider how the best practices for digital transformation can produce value in the PE’s fund.

Just as private equity looks for long-term trends within the noise of market cycles to find tailwinds for its investments, there is an opportunity to do the same with tech: to look through the noise of the hype cycles for long-term trends to provide similar investment advantages. Venture capital does it by making bets before the tech has proved itself. That’s not the role of LBOs. Instead they have the luxury of time to wait for the tech to derisk before using it to create value and sometimes disruption.

This is the job of the PE digital operating partner: to use these proven high technologies like IoT and AI as tools by pattern-matching winning tech applications within the portfolio companies. The process of applying these high technologies to create value is called digital transformation, and now it is an important source of alpha that’s been either overlooked or deemed too complicated until now.

With financial engineering hardly moving the needle and traditional operating improvements becoming commoditized, it’s worth it for private equity to consider how the best practices for digital transformation can produce value in the PE’s fund.

As discussed, digital transformation is the process of transforming a traditional company into a digital-traditional company through a series of digital initiatives that create value by using high technology like the internet of things, data science (analytics and AI/ML), the digital twin, sensor fusion, additive manufacturing, AR and blockchain. Digital transformation transforms traditional companies into data-driven companies (Figure 2.1), providing a platform for product innovation/invention, business model innovation/invention and greater operating efficiency, all leading to the North Star of increased enterprise value through EBITDA and valuation multiple gains.

Figure 2.1 Transformation to Data Driven

PRODUCT INNOVATION

Collecting customer data with a smart product (or smart service or smart environment) is like having a 24-hour-a-day, 7-day-a-week window into the customer’s world. From this data, different data science models are built to yield different business insights.

Usability models quantify how customers use their smart product, and just like an ecommerce company uses its usability model to improve its website, a digital-traditional company uses its usability model to make its physical smart product better too.

Take, for example, the road roller, aka the steam roller. Road rollers use their weight of up to 44,000 pounds and their vibrating drum to remove air from the hot mixed asphalt (HMA) to compress it to a specific density. Getting it right takes a lot of experience, and even then, it can be hit and miss. Enter digital transformation. Every geography has a known ideal asphalt density to make the perfect road. By digitizing the variables associated with asphalt density (HMA temperature, roller weight, roller speed and drum vibration frequency), we can build a paving usability model and then solve it in several different ways. To minimize time (increase efficiency) while achieving the desired asphalt density, we solve for max speed and control the speed of the smart road roller accordingly. (See Figure 2.2.) To minimize material usage (reduce COGS) while achieving the desired asphalt density, we solve for HMA thickness.

Figure 2.2 Usability Model

Which product is more valuable? A regular road roller that’s operated by gut or a smart road roller that compresses asphalt to the perfect density under different circumstances in any environment. Usability models like this are a platform to catalyze countless innovations. Innovations that lead to a more competitive product that produces alpha by increasing market share, which leads to higher sales and higher EBITDA.

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PRODUCT INVENTION

A utility model tells us what the customer uses our product for, and when properly developed, empowers us to invent new products, both physical and virtual.

Consider a smart surgical instrument–cleaning autoclave. In this example a simple utility model is used to measure machine capacity, what is being cleaned and how well it cleans. If over time we discover the trend that our smart surgical autoclave is being disproportionately used to clean a specific type of long surgical instrument, we have potentially discovered a need for a new product—one that matches the long instrument’s geometry, cleaning capacity needs and best cleaning mechanism. Furthermore, we can sell this new physical product along with custom-cleaning consumables formulated to clean the instruments in the new product. And by knowing how the autoclave is being used, we could sell an information product that tracks sustainability, or we could underwrite a product warrantee that is always profitable. We can invent new products by quantifying how our original product was being used. (See Figure 2.3.)

Figure 2.3 Utility Model and Its New Products

Another class of model that produces valuable information products is prediction models. These models use past and present data to make predictions about the future. A use case for prediction that’s applicable to both the smart road roller and the smart surgical autoclave is maintenance. Prediction models can be taught to understand normal operating conditions and quantify multidimensional conditions that eventually lead to failure. These predictions can lead to two types of new information products: a predictive maintenance product that notifies the operator of impending doom and a prescriptive maintenance product that makes changes to the smart product’s configuration to avoid or delay the failure before it happens.

Inventing new physical products and tailor-made consumables and information products all expand market size and find alpha by shifting the boundaries of competition or by entering adjacent markets. This too increases sales and EBITDA in a meaningful way.

Digital transformation transforms traditional companies into data driven companies, providing a platform for product innovation/invention, business model innovation/invention and greater operating efficiency, all leading to the North Star of increased enterprise value through EBITDA and valuation multiple gains.

BUSINESS MODEL INNOVATION AND INVENTION

Another class of data science model to consider is monetizability models. These models understand how the customer makes money with your product. Modeling the variables of your customer’s revenue model leads to two profound and related operating improvements: product pricing and product monetization.

Pricing improvement/rationalization is a go-to value creation lever used in private equity today; however, it is performed with experience, gut instinct and data. A pricing digital initiative driven by a monetizability model is all about the data. If we can measure the amount of revenue or profit generated by our product, we can value price it with extreme precision.

Pricing on its own increases sales, but as part of a custom-built business model it can also disrupt industries. Once we quantify the business model of our customer in relation to our product, we can transpose it to create a new business model that interfaces with the customer’s business model to reduce sales friction.

Digital transformation supports a number of classes of new business and revenue models, one of which is XaaS (anything as a service). A simple example comes from the aviation sector where jet engines are sold as a service. The most important KPI of the airline industry is miles flown per occupied seat—a metric that influences, among other things, airline share price and management compensation. A term originally coined by Bristol Siddeley and then popularized by Rolls Royce and GE, the “power by the hour” business model charges airlines based on the time the engine is used. This business model disrupted the airline industry by relating costs to revenue and shifting the capital expenditure off the airlines’ balance sheet.

Not only can new business models increase sales to improve EBITDA, they can also affect the valuation multiple. Changing the business model or introducing other novel digital strategies is a thick chapter in the playbooks of the most valuable and, not surprisingly, most innovative tech companies. Automotive, music, video, housing . . . time and time again tech companies have disrupted legacy industries by structurally changing the business or customer experience based on customer data. Through digital transformation, this is now available to PE’s traditional portfolio companies that sell physical products.

OPERATIONAL EFFICIENCY IMPROVEMENTS

Until now, we’ve been looking at the external benefits of digital transformation: benefits that have improved the customer experience and company competitiveness. Next, we turn our view internally to see how digital can reduce our operating costs and expenses. Cost cutting is a common application of digital transformation. Not because it’s more valuable, but because until now cutting costs is what naturally comes to mind when most corporates and GPs think about information technology. Nonetheless, digital transformation can improve operational efficiency by improving asset utilization (people and equipment), production yield, availability, capacity, performance and quality. Let’s look at a few examples to see how.

Logistics can improve supply chain visibility by using track & trace technology to feed real-time data into models that, once developed, can be optimized for time (speeding up delivery) or for any other KPI, such as reducing fuel consumption. Biotech, after digitizing the “pots and pans” of by-hand experimentation, can use data science to identify the experiments to do that have the highest probability of success.

Digital transformation transforms traditional companies into data driven companies, providing a platform for product innovation/invention, business model innovation/invention and greater operating efficiency, all leading to the North Star of increased enterprise value through EBITDA and valuation multiple gains.

In these examples, alpha is organically created, but digital transformation can also support inorganic growth. Buy and build and TAM expansion acquisition strategies can be supported by extending the tech into the add-ons to integrate them into the platform company. Not integration in the sense of plumbing, but instead integrating the data being carried by the IT pipes. One such example is to use data science to predict overall platform and add-on inventory needs for working capital improvement, supply chain rationalization and group buying power.

Operational efficiency lowers COGS, and by using the customer data collected by the smart product, it can improve SG&A effectiveness too. This reduction of COGS and OPEX widens margins, increasing enterprise value by improving EBITDA.

THE NEWEST VALUE CREATION TOOL

Digitally transforming a traditional company into a digital-traditional company produces a data-driven company. Once the physical has been digitized, portfolio companies can take advantage of the same value creation strategies used by the most valuable tech companies. Data-driven companies build models such as usability models, utility models, prediction models, monetizability models and optimization models that in turn galvanize product innovation, product invention, business model innovation/invention and operating efficiency.

Digital transformation and its associated high technologies have been derisked so they can now deliver an important source of alpha additive to the other operational improvements typically applied by private equity operation teams. As a value generator, not only does digital improve margins, but it enables a variety of ways to increase the top line and valuation multiple, making digital one of the most powerful operational value creation tools available to GPs today.

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