Former Apple Manufacturing Experts Bring AI to the Shop Floor

Ex-Apple manufacturing pros package their lessons-learned into artificial intelligence for the factory.

Two former Apple design engineers – Anna Katrina Shedletsky and Samuel Weiss (photo) – have leveraged machine learning to help brand owners improve their manufacturing lines. The company, Instrumental , uses artificial intelligence (AI) to identify and fix problems with the goal of helping clients ship on time. The AI system consists of camera-equipped inspection stations that allow brand owners to remotely manage product lines at their contact manufacturing facilities with the purpose of maximizing up-time, quality and speed.

Instrumental, smart manufacturing, artificial intelligence, contract manufacturersShedletsky and Weiss took what they learned from years of working with Apple contract manufacturers and put it into AI software.

“The experience with Apple opened our eyes to what was possible. We wanted to build artificial intelligence for manufacturing. The technology had been proven in other industries and could be applied to the manufacturing industry,” Shedletsky, CEO at Instrumental, told Design News . “It’s part of the evolution of what is happening in manufacturing. The product we offer today solves a very specific need, but it also works toward overall intelligence in manufacturing.”

Capturing Manufacturing Know-How in the Software

Shedletsky spent six years working at Apple prior to founding Instrumental with fellow Apple alum, Weiss, who serves Instrumental’s CTO. The two took their experience in solving manufacturing problems and created the AI fix. “After spending hundreds of days at manufacturers responsible for millions of Apple products, we gained a deep understanding of the inefficiencies in the new-product development process,” said Shedletsky. “There’s no going back, robotics and automation have already changed manufacturing. Intelligence like the kind we are building will change it again. We can radically improve how companies make products.”

There are number examples of big and small companies with problems that prevent them from shipping products on time. Delays are expensive and can cause the loss of a sale. One day of delay at a start-up could cost $10,000 in sales. For a large company, the cost could be millions. “There are hundreds of issues that need to be found and solved. They are difficult and they have to be solved one at a time,” said Shedletsky. “You can get on a plane, go to a factory and look at failure analysis so you can see why you have problems. Or, you can reduce the amount of time needed to identify and fix the problems by analyzing them remotely, using a combo of hardware and software.”

Bringing Computer Eyes to the Factory Floor

Instrumental combines hardware and software that takes images of each unit at key states of assembly on the line. The system then makes those images remotely searchable and comparable in order for the brand owner to learn and react to assembly line data. Engineers can then take action on issues. “The station goes onto the assembly line in China,” said Shedletsky. “We get the data into the cloud to discover issues the contract manufacturer doesn’t know they have. With the data, you can do failure analysis and reduced the time it takes to find an issue and correct it.”

Instrumental includes a machine-learning Detect feature that highlights

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