The original assembly line made it cheap to make a million of the same thing, but adaptive manufacturing makes it cheap to make a million unique things.

-Nick Pinkston, Founder and CEO of Plethora

The Digital Age has not only dawned but is exploding with applications across every field and industry. Just as AI is changing the face of medicine, engineering, and even education, so too is it changing the way manufacturing is conceived and executed in three key ways, rendering factories and fabrication processes as “smart” as any iPhone and as connected to the Internet of Things (IoT) as Amazon’s Alexa.

Heading off problems at the pass

Traditionally, industrial equipment has been scheduled for maintenance at regular intervals, regardless of operating condition. In practice, this meant that it was not uncommon for a company to dispatch technicians to routinely “service” a machine in perfectly good working order or a machine with unanticipated and undiagnosed equipment failures, wasting costly time and labor in the process.

But with advances in AI, machines can be fitted with sensors and networked, allowing their performance to be monitored, analyzed, and even enhanced or tuned, eliminating unnecessary and costly “check ups” or the need for unexpected “major surgery.”

The emerging practice of digital “twinning” involves creating virtual models of industrial-scale machinery, making it possible for either AI programs or human operators to “mind the shop” and generate predictive analytics.

Over half a million twins of varying complexity are are currently in use. Explains Mariya Yao, an expert in applied AI and Chief Technology & Product Officer at Metamaven, “Complex twins like those of gas turbines interpret data from hundreds of sensors, understand failure conditions, track anomalies, and can be used to regulate production based on real-time demand.”

But even twinning of simple machines can result in significant business benefits. For example, many elevator manufacturers generate the lion’s share of profit on servicing costs rather than equipment sales. Using traditional servicing practices in inefficient and wasteful, and by fitting elevators with sensors and twinning them, service is rendered only as needed.

Higher Quality Quality-Control

As the saying in evolutionary biology goes, “Rocks don’t get cancer, dogs get cancer,” pointing to the principle that higher-order, more complex structures bring with them exponential possibilities for breakdowns and complications of all kinds.

The more highly complex a product (a laptop, for example), the more manufacturers must contend with unanticipated downtimes, low yields (the percentage of units that fail to meet quality control standards), and low productivity (the time required for production).

Productivity is often in competition with yield, because the more quickly a a production process is pushed, the greater the likelihood for errors or poor quality product.

But the faster feedback loops afforded through AI enable heightened monitoring and adaptive control that can accelerate productivity without compromising the quality of yield.

Specifically, quality inspections can be handed off to AI rather than to humans, the latter of which are poorly equipped to keep up with both the complexification and proliferation of products.

While human-lead anomaly-detection on hundreds of units can consume many hours, inspections conducted by cameras run on AI vision-algorithms can do so in mere seconds, enabling manufacturers to identify and resolve production issues before costly delays accrue.

Consumer-Informed, On-Demand Production

As long as there have been buyers and sellers, accurately estimating consumer demand has posed a major challenge. Overestimating (or underestimating) consumer demand can be a sink or swim proposition, and many manufacturers have been left “holding the bag.” (And in manufacturing, a bag that is too full is not necessarily better than a bag that is empty.)

AI-enabled real-time demand-visibility allows manufacturers to strategically respond (rather than blindly react) to consumer trends through apps connected to commercial IoT and industrial IoT. Thanks to the rising ubiquity of smart devices like Amazon Echo and Google Home, consumer browsing and buying habits are increasingly transparent and, with consumer consent, this data can be fruitfully fed downstream to keep manufacturers abreast of changes they need to make in their supply chain and production activities.

While end-to-end networks that completely close the feedback loop for on-demand production are still the stuff of the future, companies like Amazon are leading the way and smaller businesses are optimizing supply chains by combining multiple business processes on a single, user-friendly platform.

If you’re a company looking to make your manufacturing “smarter” or are looking to bring a smart product to market, we can help. At Pivot, we’re industry leaders in smart manufacturing and have helped hundreds of businesses take their next step to success. Contact us today and see what we can do for you.