With stock market indexes hitting new highs in the New Year and trillion-dollar infrastructure deals starting to work through the economy, 2022 is off to a roaring start. Yet pandemic-induced labor and supply-chain disruptions along with ever-tightening deadlines and quality requirements have manufacturers scrambling for how to respond most effectively.

The good news is prioritizing your digital manufacturing journey will pay dividends across a number of production and business areas that will remain prominent in 2022. Trends will continue to evolve, and sometimes explode, across the business landscape. We’ll discuss how being better connected helps identify and respond to them with success.

Trend #1: Seize the opportunities to build the case

First, last, and always, there must be return on investment. Every project for our Clients begins with building the business case and identifying the ROI for improved digitalization in production operations. One company in the high-pressure die casting business identified a scrap rate of 9 to 10% as the industry norm. By establishing equipment connectivity, gathering data and improving process and part quality across numerous manufacturing metrics, we found that reducing that rejection rate by a single percentage point would result in saving a quarter million dollars annually. Such results are not only valuable in themselves, but they also have a way of inspiring ongoing process improvement.

Trend #2: Products are better with digitalization

Improved digitalization and connectivity are not only changing manufacturing processes, but manufactured products as well. This trend is being realized in machine tools, robotics and a growing number of components.

One major manufacturer of pumps, motors and bearings not only uses predictive analytics in their manufacture, but due to improved uptime and quality, sells and ships their products at a reduced cost. In addition to an improved price point, they also offer a subscription-based remote monitoring service as part of their warranty. In the case of pumps, for example, wear components such as seals are monitored by the pump supplier and shipped to the customer for replacement prior to failure. This pays benefits across a number of areas for both the pump supplier and end user: improved product uptime, reduced spare parts inventory and a closer value-added customer relationship. Expect to see more such business model changes as connectivity improves.

Trend #3: Costs pushing downward

Fears of budget-devouring incomprehensible digitalization projects in manufacturing are being dispelled. Hardware is cheaper along with computing power and memory, and components such as sensors are accessible and available. In fact, an overall system-wide improvement mission can be a goal but does not have to be your entry point. “Think big, start small, fail fast” is a powerful mantra.

Brand new equipment with built-in connectivity is not necessarily an immediate requirement, either. Process standards from organizations such as OPC (OPC-UA) and the Association for Manufacturing Technology (MTConnect) are helping connect legacy equipment to improved production networks. A few standards to keep an eye on include:

• Enterprise-control system integration ISA95
• Platform independent service-orientated architecture OPC-UA
• Manufacturing equipment semantic vocabulary – MTConnect ANSI/MTC
• Quality information framework
• Digital twin framework for manufacturing – ISO 23247

Trend #4: A.I. for augmented intelligence

More manufacturing companies are discussing and adopting artificial intelligence (AI) applications in ever-growing numbers, and the pace is only going to increase. According to a recent Gartner CIO Survey of more than 3,000 executives in 89 countries, AI implementation grew 270% in the past four years, 37 % in the past year alone, and will reach $6.14 billion in value this year.

FIGURE 1: In many ways the trends associated with Industry 4.0 are an augmentation or further development of possibilities inherent in the introduction of the programmable controller and distributed control system in the 1980s and being introduced over time in an evolutionary manner. Courtesy: IoTco

“We still remain far from general AI that can wholly take over complex tasks, but we have now entered the realm of AI-augmented work and decision science — what we call ‘augmented intelligence,’” Chris Howard, distinguished research vice president at Gartner, said. “If you are a CIO and your organization doesn’t use AI, chances are high that your competitors do and this should be a concern.”

Among the CIOs surveyed, whose employers represent $15 trillion in revenue and public-sector budgets and $284 billion in IT spending, deployment of AI tripled in the past year, rising from 25% in 2018. Gartner credits the climb with the maturation of AI capabilities and the rapidity with which it’s become an integral part of digital strategies. AI is reaching a historical moment because of six converging factors:

Bigger data: Many devices have given us access to vast amounts of data to process, both structured (in databases and spreadsheets) and unstructured (such as text, audio, video and images). Big data will only get bigger. AI-assisted processing of this information allows us to use this data to discover historical patterns, predict more efficiently, make more effective recommendations and more.

Processing power: Accelerating technologies such as cloud computing and graphics processing units have made it cheaper and faster to handle large volumes of data with AI-empowered systems through parallel processing. In the future, “deep learning” chips – a key focus of research today – will push parallel computation further.

A connected globe: Global manufacturing supply chains together with social media platforms have fundamentally changed how individuals interact and what information they can expect and when. Increased connectivity is accelerating the spread of information and encouraging the sharing of knowledge, presaging the emergence of a “collective intelligence,” including open-source communities developing AI tools and sharing applications.

Open-source software and data: Open-source software and data are accelerating the democratization and use of AI, as can be seen in the popularity of open-source machine learning standards and platforms. An open-source approach can mean less time spent on routine coding, industry standardization and wider application of emerging AI tools.

Improved performance: Researchers have made advances in several aspects of AI, particularly in “deep learning” and “deep reinforcement” that are rethinking how processes perform. Take resistance welding, for example. Resistance welding used to be air- or hydraulic-driven, with sensors that told the robot to squeeze to a given pressure and fire the current. The result was over-welding on a scale of 30% on average, according to FANUC North America, Rochester Hills, MI.

With the luxury of a developmental R&D lab that brings to bear emerging research in robotics, linear motors, CNC controls, sensors and industrial applications experience, one of the latest results is what FANUC calls Learning Vibration Control.

Combining cameras and software, “gakushu” (studying/learning) robots equipped with the LVC package automatically adjust to fine variables in fixturing or other conditions in real time and adjust their motion for up to 15% cycle-time improvements in spot-welding processes, all with quality checks that result in tolerances not reachable manually.

As costs push downward, we will see basic industrial products such as pump controllers, conveyers and all manner of factory floor automation adopt AI techniques. With IIoT connectivity, given a choice between a motor controller that just controls, and a motor controller that controls and provides information about its own future performance, more and more industries will move toward AI-based controls.

Trend #5: The emerging “smartforce” with digitalization

A significant trend that has been front of mind in manufacturing for many years is where will the production workers of tomorrow come from? Digitalization and improved analytics will not only result in improved and more agile plants, but it will also help attract a digitally native, app-savvy workforce.

The next generation of manufacturing workers will be on a continuous life-learning spectrum with workers ready to take on new roles that become in demand more rapidly as technologies change manufacturing processes and operations.

For the education system to be able to keep up with industry’s demand for workers, schools will also have to be nimble and pivot educational programs and labs to efficiently fill the roles needed by their local industry sectors.

IoTco also prioritizes education through our IoT Academy. For those who are ready to work with us, we offer three types of workshops, including:

• Seminars and workshops.
• One-day executive course.
• Three-day executive course.

It has been said that data is the new oil and shops of all sizes are sitting on potent wells. Taking the analogy further, companies can both uncover a virtual lake of data and refine and enrich it. Seizing opportunities for more digitalization, better analytics and transformative performance in manufacturing is as much a mindset as it is a mission. Improving connectivity and access to data will continue to drive changes in products, business models and the workforce this year and beyond, not only in design and manufacturing, but in the consumer marketplace as well.

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