MachineMetrics – IoT for Industrial Manufacturing
Table of contents
Often called Europe’s last dictatorship, Belarus is a country where you can do things you wouldn’t be able to do elsewhere – like ride in a tank. Actually, they have about half a dozen different tanks you can ride in, each with varying price points. Then there are the factory tours. Want to see how the world’s biggest truck gets built? They’ll gladly show you. After that, head over to Minsk Tractor Works and they’ll even let you help put together a tractor by hand. It’s probably some of the most OG manufacturing happening outside of North Korea, and it’s well worth a look before things go the way of modernization.
Today’s modern factory isn’t just physical, it’s also replicated digitally. An entire factory – the workers, the machines, the parts – all have a digital twin that produces data which can then be optimized by machine learning algorithms. Today, we’re going to talk about a company that’s helping make that happen in the world of manufacturing – MachineMetrics.
About MachineMetrics
Founded in 2015, San Francisco startup MachineMetrics has raised $37.7 million in disclosed funding with the lion’s share of that coming in the form of a Series B that closed just weeks ago. Leading that round was a company that’s found in our own tech stock portfolio, Teradyne (TER), who said that MachineMetrics’ rapid growth aligns strongly with their larger industrial automation strategy. The MachineMetrics Industrial IoT platform can be installed in minutes, automating the collection of data from manufacturing equipment, while delivering a rapid return on investment. To understand the decision making process behind why a company would adopt such a solution, we can listen to the CEO of one that did – Carlson Products.
The Carlson Products Case Study
Based out of Wichita, Kansas, Carlson Products is a major supplier of formed and fabricated component parts for commercial bakeware, industrial, doors, and restaurant leaders globally. The CEO of Carlson, Austin Peterson, saw machine tracking in action and wanted to implement it at his own company. He reached out to machine manufacturers who provided rudimentary capabilities that were almost as bad as what they had in place at the time – manual tracking using a Microsoft Access database (rolls eyes) that was neither responsive nor accurate.
The MachineMetrics solution started with tablets mounted to 15 machines that tracked downtime, setups, part rejections, etc. Large monitors were put in place across the shop floor providing visibility into performance against production goals. Production managers were notified of issues by text in real-time. There was now a ground truth that could be used to monitor productivity and incentivize the company’s 100 employees accordingly. The end result was quite remarkable with a return on investment (ROI) of just 30 days.
Mr. Peterson isn’t the only CEO willing to share his MachineMetrics success story with the world.
The Wiscon Products Case Study
Torben Christensen is leading the charge at Wiscon Products of Racine, Wisconsin where they’ve been producing precision parts for various industries since 1945. The primary goal of adopting an IoT solution was data accuracy, and MachineMetrics bubbled to the top with their flexible pricing structure and ease of data accessibility. Without any historical data, planning and scheduling was difficult. Wiscon Products spent $16 million on equipment and felt like it was being run at capacity. It wasn’t. The production department was always “busy,” but the output wasn’t there. When MachineMetrics measured Wiscon Products against an industry benchmark, the results were eye-opening. “It was staggering to realize how bad we were,” said Wiscon’s CEO. There was nowhere to go but upwards, and that’s where they went.
In reading through these success stories, you may be tempted to think that successful IoT implementations in manufacturing are the norm, but that’s hardly the case.
The Problem with IoT in Manufacturing
Historically, IoT implementations in manufacturing have had a high failure rate. When you consider there are now 450 IoT platforms to choose from, it becomes critically important to understand why these failures occur. A manufacturing company is an ecosystem of distinct types of equipment, coupled with a disparate collection of software applications – the so-called application Frankenstack. Trying to force-feed your archaic operations onto some pre-configured IoT platform just results in lots of project work where an ROI is years away with a project stuck in “pilot purgatory.”
As usual, it all comes down to big data. That’s why data collection infrastructure is the foundation of the MachineMetrics Industrial IoT Platform. Transforming data into a standard format and storing it in the cloud means you then have an accurate digital twin that can be fed to machine learning algorithms which do cool things like predictive analytics.
The average MachineMetrics customer starts with a machine utilization of 28% and realizes an improvement of 15 to 20% in a matter of months. That’s the power of reliable big data. Today, hundreds of manufacturers have connected thousands of machines to MachineMetrics across global factories, from mom and pop job shops, to some of the largest manufacturing companies in the world. Of course, when there’s a big blue ocean total addressable market (TAM), other sharks come around to feed.
MachineMetrics’ Competitors
There’s no shortage of competitors in this space, something we wrote about in our piece on 7 Industrial IoT Startups Using AI to Monitor Machines. In that list was Sight Machine, a company that’s also based out of San Fran doing much the same sort of work MachineMetrics does, now having raised over $80 million. Perhaps most interesting of the lot is a company called Augury that listens to the sounds machines make and uses them to perform predictive analytics which reduces downtime and increases machine lifespan. Then there’s industrial IoT startup Samsara which has now raised close to a billion dollars for their “connected operations cloud.” In our own portfolio can be found C3, a company that’s deployed trillions of IoT sensors across a broad set of industries.
What we liked about MachineMetrics was the extent to which they provide detailed supporting evidence that their platform creates value. It’s easy to get bogged down in a mire of buzzwords and not talk about what value your fancy technology provides clients. Adding value breeds success stories, and there’s nothing like a good reference client to help close a sale. The effort MachineMetrics puts into examining why most IoT manufacturing implementations fail helps assure would-be clients they’re in safe hands. With every implementation that MachineMetrics does, the more experience they get with handling disparate data sets, the more capable a provider they become.
Conclusion
In the U.S. alone, there are over 625,000 manufacturing businesses, 98% of which are categorized as small businesses. A solution that can be rapidly deployed along with no-code capabilities means that clients can be onboarded quickly and the platform can use all that delicious big data to improve itself, further increasing the value it adds over time. In times of economic turmoil, a platform that lets small businesses “do more with less” should become increasingly popular.
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