Many of today’s factories are a far cry from the soot-covered, smoke-belching machine shops of old: The Internet-connected machines talk to one another, compile output and maintenance data, and some accept software updates much like personal computers or smartphones.
But managing the tides of incoming data to fine-tune factory productivity has required an industry of its own, with startups and major industrial players angling to solve the big data problem for manufacturers.
One of those startups is MachineMetrics, which recently raised an $11.3 million Series A funding round led by Tola Capital and plans to double its 25-employee headcount within the next few months, according to co-founder and CEO Bill Bither.
MachineMetrics, with offices in Northampton, MA, and Cambridge, MA, says one of its selling points is that it has trained its artificial intelligence engine with so much data that now, out of the box, the company’s software product can flag machinery for potential maintenance or repair needs, or detect anomalies that need attention.
“We collect data from so many different machines, thousands of machines,” Bither says. “That allows us to learn from machines and people on the shop floor and make quick decisions.”
It’s hard to tell how MachineMetrics’ product stacks up against related tools from the likes of GE (NYSE: [[ticker:GE]]), Accenture (NYSE: [[ticker:ACN]]), and Oracle (NYSE: [[ticker:ORCL]]), but the Massachusetts startup has amassed a respectable list of customers. MachineMetrics says its system is running in factories owned by industrial supplies manufacturer Fastenal, tool-maker Snap-on, oil and gas equipment manufacturer National Oilwell Varco, compressor and pump maker Gardner Denver, automotive parts manufacturer Continental, and construction material maker Saint-Gobain.
Fastenal manufacturing vice president Tim Borkowski says once the company adopted MachineMetrics’ product, production time increased by more than 100 hours for each of the first three months.
“There’s no more educated guessing or finger-pointing; there’s a solid reason behind everything and every decision we make,” Borkowski says in a prepared statement.
Bither started his career as a mechanical engineer at United Technologies in Connecticut and founded MachineMetrics in 2014. He says manufacturers often have difficulty connecting their Internet-capable equipment to cloud-based software systems and even more trouble finding people with the time or experience to create a machine-analysis platform for the factory.
MachineMetrics’ system lets manufacturers know when machines are down, what are the reasons for good or bad performance, and an accurate time estimate for making parts. The system also lets companies know when the machines may need maintenance.
Seattle-based venture capital firm Tola Capital led the round and was joined by previous MachineMetrics investors Hyperplane Venture Capital, Long River Ventures, MassVentures, Hub Angels, and Firebolt Ventures. MachineMetrics says it will use the capital to expand its data science and product development teams, while accelerating global sales.
