MachineMetrics, an industrial Internet of Things startup in western Massachusetts, is out raising a Series A round to expand its manufacturing analytics software business.
CEO and co-founder Bill Bither, a two-time entrepreneur who learned his way around a machine shop working for a United Technologies subsidiary earlier in his career, told BostInno that his Northampton-based company is actively seeking to raise a new round for the first half of 2018. The company previously raised a $2 million seed round in 2016 from Hyperplane Venture Capital, Bolt, LRV Health and MassVentures.
Founded in 2014, MachineMetrics is among the hundreds of startups and companies entering the industrial Internet of Things space, which is all about using technology to improve asset-heavy industries like manufacturing, logistics and mining. According to CB Insights, industrial IoT startups raised more than $2.2 billion in investments in 2016. Other local companies in the industrial IoT space include PTC, LogMeIn, Sentenai and Tulip.
Since MachineMetrics started selling its real-time analytics software to small- to medium-sized manufacturers in 2016, the company has accrued more than 50 customers, including SnapOn, Fastenal and BenchMade, Bither said.
“We’re not just learning from the machine data, we’re learning from human contact.”
The company’s software connects to CNC machines and uses machine learning algorithms on the data it collects from customers to recognize when machines are under capacity and when they might need maintenance, Bither said. All of the relevant information is provided on a dashboard, which can be displayed on large overhead monitors or tablets to give machine shop managers more mobility. In most cases, the software can connect to machines without any extra hardware, which Bither said is part of what makes the business model scalable (the company has hardware for connecting to older machines).
In addition to data coming from manufacturing equipment, MachineMetrics also relies on human input to help the software understand why, for instance, a machine has been down for five minutes or why a tool change occurred.
“We have the opportunity to ask questions like, ‘what’s happening,’ and the operator can provide input and the system can learn from that,” Bither said. “We’re not just learning from the machine data, we’re learning from human contact.”
The result is that manufacturers can increase machine capacity by upwards of 20 percent, Bither said. According to one customer, Carolina Precision Manufacturing, that helped the company increase capacity by $1.5 million in 2016 with no extra machines.
Beyond that, Bither said he sees an opportunity for MachineMetrics’ software to become a platform for other companies to build on top of, like a Salesforce App Exchange for the manufacturing industry. He said the company already has a few partners working on integrations with MachineMetrics, but he wasn’t able to disclose their names.
“By collecting all of this machine data, it really puts us in an interesting position to feed that right back from our customers,” Bither said. “Nobody is able to connect these different pieces of equipment to apply machine learning in the same way that we are.”