Today’s consumers are pickier than ever. They want customized, personalized, and unique products over standardized ones and prefer local, smaller producers over large-scale global manufacturers. At the same time, they also expect locally-produced products to be as cheap and reliable as those industrially produced. Factories, power plants, and manufacturing centers around the world must rely on automation, machine learning, computer vision, and other fields of AI to meet these rising demands and transform the way we make, move, and market things.
Pain Can Drive Progress
Since the industrial revolution, factories have been optimized to mass produce a few products rapidly and cheaply to satisfy global demand. “The largest inefficiency that most manufacturers face is inflexibility,” says Jim Lawton, Chief Product & Marketing Officer of Rethink Robotics, maker of collaborative industrial robots. “Traditional industrial automation requires hundreds of hours to reprogram, making it very impractical to change how the task is performed.”
Catering to finicky consumers is not the only challenge confronting modern factories. Costs of production in traditionally affordable countries like China and Mexico are rising. Oil and gas industries have been hit incredibly hard by historically low oil prices, driving the need for further efficiencies and cost reduction.
In virtually all factories, poor demand forecasting and capacity planning, unexpected equipment failures and downtimes, supply chain bottlenecks, and inefficient or unsafe workplace processes can lead to resource wastage, longer production periods, low yields on production inputs, and lost revenue. Manufacturers are also strapped for qualified labor, both skilled and unskilled, as older employees retire, younger generations lose interest in manufacturing jobs, and immigration policies tighten.
Prabir Chetia, Head of Business Research and Advisory at global analytics firm Aranca, details the current dilemma faced by many manufacturers around the world: “Energy costs among makers of electronic products can reach as high as 45 percent of the overall production costs. A significant amount of energy usage is redundant and can be easily avoided. In the food processing industry, supply chain bottlenecks can even result in 40 to 50 percent of dry crop matter getting wasted due to spoilage.”
Artificial Intelligence Is Essential For Survival
Innovative manufacturers already use artificial intelligence to tackle these many challenges. Here are the key ways that “Industry 4.0”, the latest trends in smart factories, leverage automation, data exchange, and emerging technologies:
Rethink Robotics, founded by robotics pioneer Rodney Brooks, advocates the “cobot” model where humans and robots work side by side for maximum effectiveness. While industrial robots have long performed heavy lifting and tedious work on assembly lines, they’re typically designed for a single tasks and require hours to reprogram. Baxter and Sawyer, Rethink’s smart collaborative robots, are able to learn a multitude of tasks from demonstrations, just like their human counterparts can.
“Training a robot is nearly as simple as training a human,” claims Chief Product & Marketing Officer Jim Lawton. “Companies that don’t have programming expertise on staff and can’t afford to spend hundreds of thousands of dollars on a traditional industrial robot can instead leverage more affordable, flexible automation and adapt to market changes.”
Industrial equipment is typically serviced on a fixed schedule, irrespective of actual operating condition, resulting in wasted labor and risk of unexpected and undiagnosed equipment failures. Once instrumented with sensors and networked with each other, devices can be monitored, analyzed, and modeled for improved performance and service. An industry leader in the space, GE enables manufacturers to create “Digital Twins”, or physics-based virtual models of large-scale machinery, on their industrial cloud platform, Predix.
“Twinning” a piece of equipment allows human operators to constantly monitor performance data and generate predictive analytics. According to Marc-Thomas Schmidt, Chief Architect of Predix, nearly 650,000 twins are currently deployed and range widely in complexity. 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.
Even relatively simple twins yield clear business benefits. Schindler, an elevator manufacturer, makes the bulk of their revenue on servicing costs, not asset sales. Operating a crew of service engineers on a fixed schedule is an inefficient use of labor. Instrumenting elevators with simple sensors and twinning them with Digital Twins enables Schindler to send service on a need basis rather than a time basis.
Instrumentation and digitization is not entirely straightforward in manufacturing, as we’ve previously shown. Schmidt explains: “Instrumentation is hardest when equipment is very remote. On offshore drilling platforms, for example, the biggest challenge is getting the data back to a place where it can be analyzed.”
Automated Quality Control
Faster feedback loops enable factories to tackle unplanned downtimes, low yield (% of units that pass quality control), and low productivity (time it takes to make a product). “Issues with low yield are most acute around high complexity products – like a laptop where there are a ton of various systems that need to come together perfectly for the product to work,” explains Plethora founder Nick Pinkston.
Pinkston also points out that productivity often trades off against yield. The faster a manufacturer pushes a process, the more likely they’ll hit errors and low quality output. “Better monitoring and adaptive control can allow you to increase the productivity of a single machine, and likewise better overall system monitoring and planning can allow the overall system to produce more product on the same numbers of machines.”
Rather than rely on humans for in-process inspection and quality control, a task that’s increasingly more challenging due to exploding product variety, companies like Instrumental.ai leverage cameras powered by computer vision algorithms to triage defects immediately and identify root causes of failure. Performing anomaly detection on hundreds of units in seconds, rather than hours, enables manufacturers to identify and resolve production failures before expensive delays pile up.
Overestimating or underestimating consumer demand leads to lost revenues, either in the form of stagnant inventory or lost sales. Rather than running reactively, real-time demand visibility can be achieved by connecting consumer apps and IoT with industrial IoT. With the rise of smart home devices like the Amazon Echo and Google Home, consumer trends and behavioral data can inform downstream supply chain and manufacturing activities.
While no complete end-to-end network exists in operation yet, supply chain optimization companies such as Elemica already combine businesses processes such as order management, procurement, transportation sourcing, and inventory management in a single convenient platform. Similarly, companies like Amazon, which control consumer touchpoints like the Echo, fulfillment centers, and manufacturing warehouses, are actively investing to close the feedback loop for on-demand production.
Automation Results In Real ROI
In a study involving hundreds of medium and large US-based wholesale distributors and manufacturers, ERP provider Macola discovered that 77% of manufacturers already automate their core business processes with software. Investments in technology have led to 81% reporting reduced cost and increased revenue, 76% reporting error reductions and accuracy improvements, and 95% reporting better customer service.
According to Volkhard Bregulla, Vice President of Global Manufacturing Industries at Hewlett Packard Enterprise, “AI-enabled predictive maintenance allows manufacturers to achieve 60 percent or more reduction in unscheduled system downtime, which dramatically reduces costs that accumulate across production downtime, part replacements, and inventory.”
The Future Is Flexible
Artificial intelligence and robotics, in tandem with complementary technologies like 3D printing and IoT, will enable the modular manufacturing required to meet rising consumer needs. Bregulla of Hewlett Packard Enterprise paints a likely scenario:
“We will begin to see the rise of smaller production sites being established much closer to the consumers themselves, for example within densely populated cities. This means that manufacturers will be able to dramatically reduce the time from factory to consumer, putting newly developed products into the customer’s hands within minutes or hours.”
Order-driven production not only reduces manufacturing waste, but also enables dynamic supply chains. Manufacturers will leverage real-time digital platforms connecting consumer and industrial data to configure the assembly of products on an ad hoc basis. When production lines can be quickly reconfigured to cost-efficiently create new, unique products, both consumers and manufacturers benefit.
Plethora’s Pinkston believes this dream is not far off. “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.”