Ken: Good day, and welcome to episode 190 of our Momenta Digital Thread podcast series. Today I'm pleased to have Kishore Manghnani, co-founder and CEO of Shoreline IoT, delivering the industry's first zero-friction, one-stop asset performance management solution. Shoreline is Momenta's most recent venture investment. Kishore has over 25 years of management experience working at various semiconductor systems and software companies in Silicon Valley. Before starting Shoreline, Kishore was Vice President and General Manager at Marvell, responsible for all the wireless IoT platforms, smart home, and Wi-Fi business units. Prior Marvell, he worked in various executive roles at Motorola, TeraLogic, LSI Logic, and Maxtor. Kishore holds an MBA from Santa Clara University and an MS EE from the University of Hawaii. Kishore, welcome to our Digital Thread podcast.
Kishore: Thank you, Ken. Glad to be here. I'm excited to be on this.
Ken: Glad to have you as well, especially given that we just invested in you guys. I think it's great because you and I have probably been talking about this for several years in terms of watching how you guys have been growing in the space. Hence, we're excited to have invested and to interview you and share how you developed this company with the audience. We call this the Digital Thread podcast, and the idea is to talk about one's digital thread that leads to where they're at now. What would you consider to be your digital thread? In other words, the one or more thematic threads that define your digital industry journey.
Kishore: Sure. Ken, my digital journey started way back in the 90s with digital video, semiconductors, networking, wireless, and IoT and is still ongoing with the IML and Cloud technologies. I have worked with many B2B customers in consumer electronics and commercial, and now we're focused on the industrial sector. But the one common thread I would say that applies to all these technologies all these different sectors is that there are three key criteria that all these technologies and the applications and solutions must meet for the digital transformation to be rapid and beneficial to end users. The three things we noticed - it does not matter which industry you're in and which technology- number one, the solution must be straightforward to use by the end user. Number two, whatever solution is offered must deliver fast time to value. Number three, it should evolve to add more value, not by itself but also to enable other ecosystem technologies or partners to add to that value. We have noticed that these three ingredients are essential if you want rapid and massive digital transformation for different technology. They're a must and are common across all industries.
If you look at the smartphone industry, for example, they've done this very successfully. The leading consumer electronics companies bring digital transformation to smartphone applications. But when we look at the industrial segment, they still struggle to adopt digital transformation rapidly. We realize that the task is much harder for the industrial sector because you have a challenging environment and regulatory requirements. But the main question is how can we bring the consumer ease of use, which end users of smartphones and smart home solutions already expect from any solution to the ultimate end users in industrial machines? Frankly, this digital thread has been driving not only me but also the Shoreline team, the founding team, for the last four or five years. How do we build a true zero-friction APM solution for large enterprise customers in the energy and manufacturing industries? How can we bring that technology into the industrial sector? Without that, we don't see how you could see the rapid adoption of digital transformation.
Ken: Well said, wow. Easy to use, delivering fast time to value and enabling greater ecosystem value. It sounds like a promising set of attributes for any kind of tool. I like that you've taken the industrial sector and compared them to the state-of-the-art in consumer sector; that's a great target. If you can work it with the iPhone 14, you should be able to do the same thing with your GE jet engine.
Ken: As I mentioned, Momenta just became- the first outside capital in Shoreline. One of our key investment criteria is our audience will know deep industrial DNA of the founders. Kishore, you, and the team brought this all-in spade. I love that you spent 25 years across semiconductor systems and software, all before starting Shoreline. That's quite an incredible act in and of itself. I know you've already offered key insights from that time, and I think those are great principles. Let me ask, what inspired you to co-create Shoreline in 2017?
Kishore: One of the inspirations, I would say, was when we looked at the industrial market, and before looking at the industrial segment, we also looked at the commercial segment. Then, when we looked at the industrial market, we- from our experience, we could tell that the industry could greatly benefit from digital transformation. However, as we dug more into this existing digital solution, we know that everyone- including the legacy vendors or the new startups, had pretty much the same architecture and the solution that was trying to fit into existing channels and the existing expert ecosystem that has been in place for last 20 to 30 years. None of the existing solutions was designed or optimized for the ultimate end users, which to us is like machine operators and field technicians. Engineers designed everything that we saw for the engineers. Coming from that background, making things easy for the consumer segment, we saw a great opportunity to solve this problem with a different approach for such a large market can make a huge impact, which excited us about the industrial segment.
Ken: For a level set for the audience, say you've used the term 'asset performance management.' How do you define asset performance management?
Kishore: Good question. Most asset performance solutions only focus on preventing failures. To us, asset performance management means remotely monitoring machine health so that an automated platform can deliver insights and actionable knowledge to clients so they can achieve sustainable optimal asset or operational performance. This means that the APM platform should run machines and processes at optimal performance and optimize production; it should extend the useful life of these machines so you can reduce the CAPEX burden. It should prevent failures and reduce maintenance costs to eliminate unplanned downtime. Finally, it should also reduce energy consumption and enable ESG monitoring so you can have sustainable operations. To us, asset performance means all of this, not just predicting failures.
Ken: I'm glad you defined that, and I was quite pleased to hear the ESG angle to all this as well, which I think most of the companies in our portfolio have a strong indirect ESG story to tell, just by the fact that they're usually put in for optimization purposes. It's interesting because, for all the things you've listed, you would think this would be "a no-brainer" for anyone operating large assets. But by some industry measures, I think 85% of industrial assets are still dark, in other words, unconnected and non-monitored. Why has this been the case?
Kishore: Yeah, this is a true observation. When we have a customer we're working with, some of them have 90% or more of the assets, which we call 'dark.' They're still not connected; they're still manually inspected and monitored. When we talked to these customers, the primary reason we found was that there are existing digital APM solutions, but they are very expensive. They're not scalable and very complex to set up and manage. Then, we dug more into why that's the case and why they're expensive and complex. Including the legacy vendors- even the new vendors, all agree. It's still complex, and the primary reasons we found were two or three reasons. Number one, almost all the solutions are multi-vendor solutions. They're custom installed by third-party contractors, system integrators, or the vendor's engineers at each side for each customer. Every installation, each side, is a custom installation. You require experts; you require different kinds of experts. You need IT experts; you need experts who understand these machines. Then you need experts who understand AI ML. All the AI ML- most of them usually require a lot of historical data and data scientists to see any value. In summary, as I said earlier, all the solutions are designed by engineers for engineers. They are complex and expensive, so the customers don't see ROI on more than 85% or 90% of their assets to deploy the solution.
Ken: You mentioned AI ML, and I know you talked about using AI ML, IoT, etc., to make industrial asset management both affordable and intelligent for all assets. How does this work in practice? If I have a large rotating pump and want to honor it, how will I engage with you guys to do that?
Kishore: Good question. I would say AI ML is a key part of our solution. But that's not the only part of the solution. Our goal is to have a zero-friction solution that's easy to use, delivers value very fast, and continues to add value. To build a solution like that, three main pillars are important. The first is the end-to-end solution we have developed, including hardware and software. The first item in this solution is a smart wireless sensor that can monitor more than a thousand machine health parameters with onboard analytics and direct-to-cloud wireless connectivity. Why we did that- the main reason is so you don't need new IP infrastructure or IT expertise, and the customer can self-install within minutes. We can ship the hardware to the customer, who can self-install it. There's no other product in the industry that can do that today. That's the number one item, building block, or solution.
Number two is we have a large asset library that we've developed over the last three or four years- which is physics models of various industrial machines. The reason we did that is the moment the sensors are mounted on these machines to connect to the Cloud; the physics model can auto-generate the baseline of the machine on day one and auto-configure all kinds of settings that a non-expert in machine operating may not know what to monitor- all those things are automatically configured. Again, it's the same team building a zero-friction solution. Finally, to deliver value to the customer, we have a self-learning AI ML-based diagnostic suite that does not require historical data, does not require data scientists, and could do self-learning. It drives all the alarms, dashboard reports, recommendations, and root causes- the analytics engine drives everything. It's a combination of all these three items packaged nicely as an end-to-end solution that delivers a very affordable and scalable solution to the customer. That's how we have approached the solution to bring a consumer-type smart home experience to the complex industrial machine.
Ken: I call you guys up; you ship me the sensor, and I install it. It's cellular, so everything is taken care of regarding the information transfer. Then I go to the website, register the equipment, and get data. Is it that simple?
Kishore: Yes, even simpler than that, including the cellular built into each sensor, including these pre-activated SIM cards, so our customers do not even have to call any cell phone company. They don't even have to bother their IT department, they can just mount the sensor, press a button, and smartphone and the software takes it all from there completely. It auto-registers their device, and they select their password and configuration. At that point, they start seeing all the dashboards and charts in the Cloud. Perfect proof of our solution working well- our very first customer signed a seven-figure multi-year SaaS contract with Shoreline. This customer had never met Shoreline. Shoreline employees had never visited this customer's site. No third-party expert was required to come and help set up or configure our platform. This was done by themselves, and within a few months of starting a pilot, they signed a commercial contract without ever meeting with us.
Ken: That's incredible. The key term you used was SAS, so you do all this as a service. OPEX pricing, if you will, is quite differentiating in and of itself. You started to mention some of your use cases. I'm curious, how are people utilizing this, and what are some of your largest wins?
Kishore: As I said earlier, we're focusing on both energy and a manufacturing segment within energy. We see a lot of interest in manufacturing from the oil and gas industry, both upstream, midstream, and downstream. In manufacturing, our solution is being deployed in large chemical plants, appliance manufacturing, pulp, and paper, manufacturing plant building PVC pipes, steel mills, and all these different applications. Traditional manufacturing and the energy industry and customers are using all different kinds of assets, from large reciprocating compressors, engines in the natural gas midstream segment, pumps, and motors in the NGL plants and crude oil pumping stations, in factories, to the gearboxes, motors, mixers, grinders, blowers, cutter fans, chillers, tanks. Every week or two, our customers are deploying this on different kinds of assets, some we didn't even plan initially.
Ken: Wow, you must have a large group working on these physics models to be able to match all these different use cases. In the digital transformation of the industry, one of the challenges has always been longtime devalue- even 18 months to two years long sales cycles are uncommon in this space. How do you address this with the Shoreline solution?
Kishore: I think you brought up a point that when we were looking at entering the digital- building digital solution for the individual segment, one of the major things we just noticed you just mentioned was long sales cycles, a long time for customers to validate the ROI, sometime between 18 months and two years. To us, this was- you could not build a scalable company and platform with that kind of approach. This was when we focused on- everything we do had to be zero friction, whether it's a product, whether it's our business model, whether it's our distribution channels, go-to-market channels, or everything must be zero friction. We have done that already successfully with a product that is zero friction. Our business model is completely SaaS based; customers don't pay anything upfront for hardware. They only pay a SaaS subscription and a software subscription. Everything is included, including cellular connectivity, including hardware in their subscription. There's zero CAPEX for the customer to get started. We shrink all those decision-making to a much shorter period. Our product could be shipped. No expert is needed, and there's no pre-planning with the IT department to start even a pilot. Customers do pilot within two months. Usually, the legal contract takes longer than the pilot itself, so that we can go from pilot to commercial contract in three to four months. We've shrunk the sales cycles from 18 months to four months for large enterprise customers. It could be even shorter for smaller, medium-sized customers. In terms of time to value, our customers see value on day one when the platform connects automatically. They don't need manual track runs and readings anymore from day one. Within the first week, because of the physics models, they start to get predictions and anomalies, dashboards and alarms already fully working, and daily emails of what's going on with their assets. They see value on day one; they see value in one week. Our AI ML fully kicks in within three to four weeks because it does not need historical data; it's trained using very limited data. Huge ROI customers see within one month, and that's why our pilots only last six to eight weeks, and customers can decide.
Ken: That's incredible when you think about the traditional- it's a CAPEX-intensive, engineering-intensive, IT-intensive process to put a sensor on a piece of rotating equipment and get it tied into the monitoring system there. You guys are focused on this zero friction and very fast time to value, which is great. Let me ask you to put your prognosticator hat on for a minute. Where do you see the greatest opportunities for asset performance management in the next five years? AKA, where are you going to be steering the company?
Kishore: We've already seen and will continue to see uptake- what I said, the traditional manufacturing. The chemicals, pulp and paper, steel, building materials, and primary metals, and we're in some of the largest chemical plants globally. Some companies have 50 plants and 47 plants deploying our solution in the chemical industry. As I mentioned, we've seen huge interest in the energy industry, which is oil and gas. Lately, we've seen a lot of interest from our existing oil and gas customers to use our platform to also help them monitor gas emissions and reduce energy consumption. These are existing customers we have started with- but then new applications they plan to use our platform for. Then a few years down the road, we see in the renewable segment that windmills are a great application for our solution and water infrastructure and utilities, especially with climate change. What happened in Mississippi recently? If they had a platform from Shoreline, they would have gotten an early warning three or six months ago to see the problems in their water infrastructure. Globally, there are more than 10 million machine assets for water infrastructure. Our platform and solution, including what sensors are already designed, are not carrying those water infrastructures today. Still, we believe that will be another big segment in the next few years.
Ken: Great. We certainly have a lot of deep relationships in that space, so we look forward to helping bring some of those as part of our investment. Kishore, how can somebody find out more about Shoreline?
Kishore: Our website, shorelineiot.com. They can also find we have great relationships with other ecosystem partners, like- AWS is one of our great partners, and we've been doing some solution launches together. AWS, we're building some channel- OEM partnerships; we'll be announcing some of those later this year. Customers would be able to find us through any one of those channels. But in the short term, it is our website shorelineiot.com.
Ken: Well, in closing- I always like to ask, where do you find your inspiration?
Kishore: I am inspired by those who challenge the status quo. Individuals or organizations that look at the world around them are not afraid to stand up and push for change. It is people like this who spark growth in humanity, business, technology, and every aspect of life. In business, I am inspired by leaders who have the courage to break away from the "tried and true" but rapidly fraying business models. They embrace the future first mindset, envision the possible, and then lean into the change by investing in the next, not the now.
Ken: Excellent. Well, Kishore, thank you for sharing this time and insights with us today.
Kishore: Thank you. Thank you, Ken. My pleasure to be on your podcast.
Ken: Great conversation. This has been Kishore Manghnani, co-founder, and CEO of Shoreline IoT, making industrial asset management affordable and intelligent for all. Thank you for listening, and please join us next week for the next episode of our Digital Thread podcast series.
Connect With Kishore Manghnani
What inspires me?
I am inspired by those who challenge the status quo. Individuals or organizations that look at the world around them and are not afraid to stand up and push for change. People like this spark growth in humanity, business, technology, and every aspect of life. In business, I am inspired by leaders who dare to break away from the "tried and true" but rapidly fraying business models. They embrace the future first mindset, envision the possible, and then lean into the change by investing in the next, not the now.
About Shoreline IoT
Shoreline is an industrial AI/ML asset performance management SaaS solution company. It enables asset-intensive industries to connect all their assets to manage performance, improve efficiency, reduce maintenance costs, extend equipment's useful life by decades, and unlock rich operational data. It was founded by AI, ML, Cloud, HW, and industrial machine modeling experts from leading companies. For more information, visit https://www.shorelineiot.com