Instrumental Sees 2X Growth After US$20 Million Series B Accelerates Development of Category-Defining Manufacturing Optimization Platform

Instrumental secured US$20 million in Series B financing in the middle of 2020 to accelerate development of its manufacturing optimization platform (see the ABI Insight “Manufacturing Optimization Systems for the Postmodern COVID-19 Era”). Since then, the company has built new software features and achieved a twofold increase in customers, revenue, and headcount.

The primary market focus is discrete manufacturing, and more specifically electronics, apparel, medical devices, and appliances—verticals defined by complex assembly and the need for more automated root cause analysis. Reference customers include Bose, Cisco, and Honeywell, in addition to contract manufacturers such as Pegatron, Goertek, Foxconn, Flex, AQS, Quanta Computer, and Transtek. Instrumental has also supported Motorola since the end of 2017 and been involved in every phone launched from that point forward.

Manufacturing is not as automated as many people are led to believe, and this lack of automation results in a lot of waste. Scrapping defective parts or products, product returns, and brand damage are just a few examples of costly mistakes. In fact, roughly 20 cents of every dollar spent in electronics manufacturing is wasted, and roughly 5% to 10% of all products need rework. Putting the right data in front of the right people at the right time and resolution can significantly reduce this waste, especially in bringing new products to market (a process detailed in the ABI Insight “AI and Machine Learning Bring CE-Grade Manufacturing and Supply Chain Management to Any Industry”). This is a sentiment shared by manufacturers for some time. What’s changing is the quality of data input and, more important, the ability to layer and automate the application of that data in a closed-loop fashion.

Instrumental’s focus is on product data. It offers commodity camera hardware but also integrates with third-party/resident customer test station equipment such as those supplied by Cognex, Keyence, and Omron. Electronics manufacturers use Instrumental to not only find and resolve their design and process issues faster, but also expedite remote work. The company finds it increasingly important for executives to understand and plan for how to build remotely. Electronics manufacturers are just one example of an industry highly dependent on in-person investigation and travel before 2020; most manufacturers are in the same camp.

While 80% of deployments in 2019 were in China, it has also seen a more recent shift to manufacturing in other countries as result of the pandemic, geopolitical concerns, and branding (e.g., some customers prefer to not have “made in China” on their products). Deployments in 2020 were more along the lines of 40% China, 40% Taiwan, and 20% everywhere else. This data reflects where manufacturing is actually taking place versus where companies are headquartered, which are predominantly in English-speaking regions.

Dark yield refers to defective units that make it past functional tests and into the hands of customers. The cost of dark yield is high. Instrumental helps minimize this cost using a new feature called Data Streams. Data Streams pull in data from customer test station equipment and will ultimately provide better support for high mix and multi-sku lines, like you might find at a contract manufacturer or firm that has achieved level 4 or 5 digital maturity/autonomy.

This is important because typically, if you are a product design engineer and you hear that a functional test is failing, there are a lot of steps to determine why. This data wrangling and analysis (identification of outliers) is a huge pain. It is also a big chunk of time for product engineers throughout the day; once they learn a piece of insight, such as what the error is, they often need to go deeper, such as to identify whether the machine or operator is responsible for the error.

Instrumental is working on a new capability it calls Relationship Explorer to surface these kinds of insights automatically, reducing a task that used to take days or hours to a matter of minutes. Relationship Explorer should be viewed as an important step toward automated root cause analysis. Eventually, the derivative insight can be used to automate process improvement. This is significant because product and process issues occur throughout the supply chain, but cross-process and cross-factory optimization is hard. At the same time, brands need to move faster and make fewer mistakes to stay competitive. Conventional machine vision systems do not work with complete data, enable real-time access, or automate issue discovery; manufacturing optimization platforms do. These are a new category of solution that enable the kind of continuous optimization these companies need, constituting a new market in the making.