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MachineMetrics raises $20M to meet industrial analytics demand

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Industrial analytics and machine monitoring platform MachineMetrics today announced that it closed a $20 million series B funding round led by Teradyne, an industrial automation and robotics company. Bill Bither, MachineMetrics cofounder and CEO, says that the new capital will be used to scale the company’s sales, marketing, and customer operations; expand its partner ecosystem; and enhance capabilities at the edge.

Manufacturing is undergoing a resurgence as business owners look to modernize their factories and speed up operations. According to ABI Research, more than 4 million commercial robots will be installed in over 50,000 warehouses around the world by 2025, up from under 4,000 warehouses as of 2018. Oxford Economics anticipates 12.5 million manufacturing jobs will be automated in China, while McKinsey projects machines will take upwards of 30% of these jobs in the U.S.

MachineMetrics’ platform aims to streamline machine data collection and production analytics to deliver insights. It offers plug-and-play connectivity to standardize, process, and analyze data at the source, providing the visibility ostensibly needed to avoid downtime and production losses.

“A trio of partners — myself, [Eric] Fogg, and Jacob Lauzier — came together on the concept roughly six years ago in an effort to capitalize on emerging technology that has essentially created a language that allows shop operators to read how their machines are functioning,” Bither told VentureBeat via email. “I met Fogg at a Valley Venture Mentors meeting, a local startup mentoring program, soon after he sold document imaging vendor Atalasoft [to Kofax for $5.5 million in 2011], and we began looking at challenges we could undertake together … Soon after founding MachineMetrics, we brought on a third partner, Jacob Lauzier, the company’s CTO, who brings to that post a background as a user interface designer and web application developer.”


Above: MachineMetrics’ cross-platform dashboard.

Image Credit: MachineMetrics

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With MachineMetrics, customers can connect sensors or older equipment with digital and analog interfaces (including Ethernet, Wi-Fi, and cellular) that can be configured and managed remotely through a web interface. The platform’s data engine transforms machine data into standard structures across different types of equipment, including data items such as custom sensor values, machine status, modes, alarms, overrides, load, speeds, feeds, and diagnostics.

Transformed data from MachineMetrics is aggregated and warehoused in a provisioned cloud environment, where customers can layer operational data and annotations on top to quantify modes like setup, production, and maintenance. The platform can also run analytics and AI and machine learning models to analyze data at the edge. For example, customers can deploy and manage algorithms to send alerts to factory workers or stop machines prior to equipment failure.

In addition to this, MachineMetrics offers an app building platform with capabilities like real-time dashboards, historical reporting, rules-based workflows, and text and email notifications. Users can create their own operator UI and apps, bring in a third-party elements like quality or work instructions, customize operator visuals for various roles, or publish data from the cloud directly into Microsoft Azure, Amazon Web Services, and other cloud service providers.

Improving analytics

Eighty-one percent of industrial internet of things implementations fail, according to McKinsey. And MachineMetrics’ own findings show that average machine utilization in manufacturing rate hovers around 24%, a low mark that has the potential to limit production. Bither says that the main culprits are process inefficiencies, unplanned maintenance, machine failures, and capital expenditure budgets.

“These machines, worth hundreds of thousands of dollars, produce hundreds of data points every millisecond, yet this data is not being captured or analyzed to improve efficiency despite all of the innovations in robotics and automation,” Bither said. “In order to get value from the data, specialized analytics need to be built, which is challenging without a common data structure. IoT platforms … are built to serve all industries, and therefore cannot solve actual manufacturing problems without custom system integration and application development … For example, in discrete manufacturing, there are hundreds of different OEM machine builders with no consistent standard of connectivity.”

Beyond its analytics platform, what MachineMetrics brings to the table is a dataset, collected over the last half-decade, of trillions of data points capturing millisecond-level changes on various machines, Bither says. This allows MachineMetrics to pinpoint problems on machines and prevent failures from happening using physics modeling and machine learning.


To date, 50-employee MachineMetrics has issued hundreds of remote stoppages of machines via its fleet of edge devices, Bither claims, resulting in the prevention of thousands of bad parts and tool failures. “MachineMetrics’ data science analyzes the why and how of the machine breakdown, using the extremely fine-grained data we collect from the machine’s motors. Over time, patterns emerge for each type of failure — allowing us to create a simple thresholding algorithm to stop the machine in its tracks whenever we see the earliest indicator,” he said. “Customers eliminate waste and can increase production without buying new industrial machines [and] harness the data we collect from the factory floor to make operational improvements across the board.”

MachineMetrics has indirect competition in production intelligence software provider Datanomix, predict maintenance platform Augury, and edge app development platforms Tulip and FogHorn. But business boomed during the pandemic as enterprises embraced digital transformation. MachineMetrics nearly doubled in revenue over the past 12 months and has hundreds of customers with thousands of users, according to Bither, spanning small manufacturing operations to large OEMs.

“Overall, the pandemic didn’t create a need for MachineMetrics — it accelerated it. Companies that relied on generic IoT solutions have learned lessons in the downfall. They are now eagerly investing in vertically focused solutions to drive value today,” Bither said.

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The latest funding round brings Boston, Massachusetts-based MachineMetrics’ total raised to $37.7 million. Ridgeline Ventures and existing investors Tola Capital and Hyperplane also participated in the company’s series B.


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