Industrial Technology: Bigger, Broader & Bolder Than Ever Before

That’s right, this traditionally subdued sector is poised for productivity in areas including advanced manufacturing, the industrial Internet of Things (IIoT) and cloud robotics.

The panel moderator was Robert Finlay, Senior Mosaic Product Specialist with KeyBanc Capital Markets.^® He helps investors identify and capitalize on emerging themes. Joining him were:

• Charles R. Jankowski Jr., Ph.D., Director, AI and Robotics, at CloudMinds Technologies, which connects robots and devices over secure Virtual Backbone Networks (VBN) to cloud AI.
• Siddharth Srivastava, Vice President of McRock Capital, a venture capital firm focused exclusively on the Industrial Internet of Things (IIoT).

More Than Just Manufacturing

Starting off, it’s helpful to understand exactly how extensive industrial technology can be.

Sid Srivastava illustrates the breadth of the sector: “While plants and warehouses certainly are a portion, innovations and investments are taking place in subsectors from Agriculture-Tech (Ag-Tech) and heavy asset industries, including oil & gas—and energy in general. The space also extends to cybersecurity and AI for the IIoT, plus all tech involved with smart cities.”

How Did Industrial Technology Evolve?

The first wave started over 10 years ago, with industrial companies placing basic sensors everywhere, gathering data. For example, Ag-Tech captured weather info, HVAC collected heating & cooling details, etc.

According to Mr. Srivastava, the overarching idea was, “Let's capture it all, just because we can.” However, that approach caused saturation and data overload. Companies really need to dig to do anything meaningful with it.

For specific, actionable data, as Mr. Srivastava explains, “Now there's a movement of going back to ‘the edge’. Based on where data is being leveraged, the edge will mean something different to different people." Robert Finlay, of KeyBanc Capital Markets,^® concurs, “It really depends on the use case, where processing occurs, and the latency.” That said, the edge generally can be defined as the usage point where actions are being performed. It’s the preciseness of this data that makes it so valuable.

Impact on Industrial Technology Business Models & Deal Flows

In short, there’s a marked shift to domain-oriented, outcome-based models.

Sales for most providers in the industrial technology space have turned from being pure technology to being oriented towards business challenges, opportunities, and outcomes.

As confirmed by Mr. Srivastava, “Over the past two years, deal flow has increased with vendors focused on specialized domains and specific use cases. They’ve become niche-oriented.” He continued, “More and more providers have a domain expert sitting next to a technologist. That's the only way to better achieve actionable business outcomes.”

Correspondingly, this approach is changing business models in a significant way. Many AI companies with scale also have large elements of consulting—and this layer of consulting is actually growing.

That said, because these are fundamentally product companies, that's a bit counterintuitive: “How does that impact scale in the long run?” According to Mr. Srivastava:

• The more complex the platform or product, the more consulting is required. However, the broader view of KBCM as a whole is that companies are increasingly adopting less complex technologies to complement their more sophisticated technologies. They need solutions that can be used by many, not few.
• How big of a hurdle might this be, having to bring in so much consulting, as opposed to what previously was off-the-shelf, plug-and-play technology?

As explained by Charles Jankowski of CloudMinds Technologies, “Specific to cloud robotics, that also initially was a sale of pure technology—which was innovative, though less profound. Now, with a layer of consulting built around it, humans are there to teach the robots.”

• Instead of the classical model of a robot learning by being programmed with every detailed step, they learn from a human demonstrating how to do a task.
• This enables more cost-efficient and effective deployment of robots anywhere. They learn from humans, rather than require re-coding any time something needs to change.

What Will Prompt Change in Industrial Technology?

Will the speed of change be influenced more by generational and demographic transitions or by conventional competition?

Although all signs point to a combination of the two, the demographics factor is strong. Legacy machinery will require interface upgrades to help the next generation of workers operate connected equipment.

A classic example where the demographic shift is significant is in food service:

• Employees need to learn how to use equipment very quickly.
• The average worker may not be able to if they’re unfamiliar with it.
• You need a Human Machine Interface (HMI), essentially a user-friendly control panel, that makes it easy for workers to quickly adapt and be functional.

Examples of HMIs in cloud robotics involve a virtual robot, companion, teacher, instructor, etc.

Mr. Jankowski described this scenario, “It may not be cost-effective to put a physical robot in numerous locations, because those range from $10,000–$30,000 each. However, a virtual robot on a screen can be relatively economical.

“Also, when there’s an avatar helping, people are more likely to connect with that virtual character. That bond is stronger than just looking up an FAQ or typing in a question. Also, in many work environments, those actions can’t even be performed—because the work is literally hands-on.

“Plus, in many instances, visual demonstrations are faster and more effective than audio queries and responses. Users can quickly see and hear the avatar perform and describe activities: ‘Take the item and place it in the inspection area, then push these buttons in this sequence.’”

Disrupting the Previously Undisruptable

Although disruption in industrial technology happens at a much slower pace, this presents not only hurdles, but also opportunities.

Obstacles include companies with legacy pieces of technology that came in different waves. There’s equipment and tech on the shop floor that's more than 30 years old. Plus, systems traditionally are very closed. Progression to the cloud definitely will be slower.

That said, opportunities can be seen via three stages of change: Adaptation, Augmentation & Advancement

Adaptation

As stated by Mr. Srivastava, “Companies that have been the most reluctant to adopt actually can represent best use cases. To start, innovations in other industries can be cross-applied. If there's an immediate gain, it's easily demonstrable—and thus likely a good place to apply entrepreneurial skills or place capital.”

A classic example is wearables or AR/VR for remote workforce management. This is not only because workers need their hands to do things, but companies increasingly need an intuitive way to communicate and monitor activities. Even for basic tasks, like planning and inventory management, these adaptations accelerate productivity gains.

Augmentation

Mr. Srivastava continued, “Smaller-stage innovators tend to be more focused on providing an augmentation layer to what's already happening. They're not there to change the way companies fundamentally do business. They're looking for specific solutions to specific problems, and those tend to be incremental. It's a scale approach, because the clients are massive.”

An example is around predictive or prescriptive maintenance. It's not part of a company’s core business, so vendors are providing a layer that reduces maintenance costs, decreases downtime, etc.

Unlike in other industries, less of an issue/incentive in the industrial technology space is legacy debt— where current companies have to keep existing business models and operational procedures going. The space almost always has a platform that already can manage legacy data, in place, without disruption.

So again, it’s an augmentation layer more than a changer of the entire process.

Advancement

Mr. Srivastava finds that, “Providers with more ambitious goals that want to displace things need to be particularly strategic. Some vendors have strong platform capabilities, but still invest heavily in building front-end applications, so they can ‘land and expand’.”

Still, lots of players are trying to change things. It's relatively easier for those with a technology that has far-reaching implications. Robotics, especially cloud robotics, definitely falls into that bracket.

Industrial-Strength Cybersecurity

As with any new technologies, security is a concern. Since IoT devices are notorious for being vulnerable, that’s one of the top reasons why most industrial companies remain wary. KBCM experts feel there are a number of IoT security businesses both of scale and early-stage that are helping companies solve the vulnerability problem. Devices are the main attack points. However, they are increasingly being secured in the same manner as IT systems, and major IT security providers like Forescout have rolled out massive IoT security offerings.

Compliance will be a major driver, particularly in Europe. This is because companies are seeing continual upgrades of standards and they're not really compliant with those. In the United States, in manufacturing—and industrial in general—there are fewer standards. It’s more of a mix of platforms, systems, and methodologies. As described below, efforts are ongoing to develop standards to make it easier for industrial enterprises, especially in terms of technological consistency and feeling comfortable putting data in the cloud.

For data storage security, it’s common to leverage big players like Amazon Web Services, Google Cloud, and Microsoft Azure.

Global Industrial Technology Robotics Competition

According to Mr. Jankowski, “In the United States, Advanced Robots for Manufacturing (ARM) is a public/private membership-based consortium. Their goal is to build an ecosystem of open components, where solutions can be built and ‘talk’ with each other on the floor.” Part of their mantra is, “Ensuring the future is built by robots, but directed by humans.”

Internationally, there are demographic forces in certain places that are pushing for the adoption of robotics. In large part, Mr. Jankowski explains, “This is because there aren't enough younger people balancing out older segments of the population.”

• In Japan, there are very few younger people compared to older people. Thus, they are adopting robots at a rapid pace for companions, elder care, etc.
• China has a similar situation, partly with respect to its previous one child policy. Additionally, they’re reached saturation levels with traditional workflows, even given their massive manufacturing capacity. Now, there's capital looking to invest in other things in their industrial technology space.

Germany also is leading the way, not due to demographics, but rather the multiple, large industrial conglomerates headquartered there.

Who Will Own the Industrial Technology Space?

Certain specialty providers, particularly if they’re in a large industrial technology sector—where there's a lot of spend—will quickly scale their businesses. Once they reach a certain size, they’ll most likely go more horizontal and become large platform players owning most of the coming monetization.

Of course, the other stakeholders, the cloud vendors, etc., will have an opportunity to react. Regardless, as Mr. Jankowski and Mr. Srivastava agree, domain specialization most likely will determine the winners.

To learn more, connect with Robert Finlay, Senior Mosaic Product Specialist at rfinlay@key.com or (503) 821-3917; David Kalez, Managing Director, Leader, Energy & Industrial Technologies at dkalez@key.com or (503) 821-3896; and Chris O’Brien, Director, Leader, Industrial Software at cobrien@key.com or (617) 316-6653.