Want a peek at the factories of the future? Consider the semiconductor industry.

Semiconductor fabs (short for fabrication plants), where microprocessors are made, have some of the highest levels of automation in manufacturing. Thousands of machines—running lasers, ultra-precision optics, dicing blades, grinders, chemical mechanical processing, advanced robotics, and more—are synchronized in a kind of automated ballet. Some technicians, in full body suits with masks, work in these clean rooms—but most of the humans involved are likely to be in another room in front of a computer screen, running analytics, planning scenarios, monitoring cobot (collaborative robot) performance, bringing unstable processes back to baseline, analyzing data, or managing programs.

The precision required to produce microchips is famous; there are some 1,500 steps to follow, with 100 to 500 variables in each. This necessitates intelligent machinery and greater automation than other types of manufacturing, so manufacturers need people with specialized skills to oversee operations, maintain quality, and solve production problems.

More manufacturers, churning out everything from air conditioners to yogurt, are pursuing a similarly advanced method for production. The goal of this digital-forward method—which integrates innovative technologies such as AI and machine learning, digital twins, robotics and automation, improved networking, laser machining, nanotech (and more)—is to increase output while also increasing efficiency, quality, and flexibility. In other words, to remain competitive.

Manufacturers of various types have already begun moving in this direction. The World Economic Forum’s Global Lighthouse Network has awarded its “lighthouse status” for advanced manufacturing to 132 facilities producing consumer packaged goods, food, appliances, beverages, electronic devices, pharmaceuticals, computer hardware, and medical products.

The human skills and capabilities required to build, manage, and optimize these machines and processes will have to change as well. “We are on the cusp of one of the most substantial shifts in how things are made,” says Michael Gretczko, principal and chief business architect at Deloitte Consulting. “The workforce of the factory of the future will look markedly different than it has looked since the dawn of the Industrial Revolution.”

How different? There will be new roles and responsibilities for production planners, line leaders, and machine operations. New skills that emphasize problem-solving over repetitive tasks. An aptitude for creating new ways of working and new products and services. The ability to apply design-thinking concepts, write software code, and apply analytics.

For semiconductor makers, the future is now. For other manufacturers, it’s coming.

Factory floor roles: Today versus tomorrow

How will this affect work on the shop floor specifically? Here’s a look at how some common factory roles will change as more digital and intelligent operations take hold.

Production planners, who are responsible for the scheduling and smooth operation of factory processes, traditionally work with manual or offline spreadsheets, manage issues reactively, and perform repetitive scheduling activities. In the factory of the future, they’ll use predictive analytics to optimize scheduling; proactively manage issues, such as machine downtime or late-arriving materials; work out solutions, such as predictive machine maintenance or purchasing more materials in advance; and identify opportunities for continuous improvement, such as reducing waste.

Line leaders, who oversee specific production assembly lines, typically spend their time collecting data, generating reports, and coordinating information and assignments. In an advanced manufacturing environment, they will be scanning intelligent dashboards built on automated data collection to identify and prevent potential issues and will be coaching and problem-solving with their team members.

Machine operators today might specialize in their specific machine or product lines, largely relying on organizational knowledge, supervisor instruction, and personal experience and judgment to address issues that arise. On a more connected digital shop floor, they’ll more likely train as generalists who can be flexibly deployed and capable of overseeing an entire production line. They’ll lean on digital tools and AI decision support to prioritize their work and identify and resolve issues.

Wanted: A new mix of capabilities

As suggested in the above job descriptions, roles in advanced manufacturing facilities will require a new integrated set of skills, combining the strengths of more than one role. For example, John Liu, principal investigator in the MIT Learning Engineering and Practice Group, says that industry leaders tell him they wish their technicians had more analytical skills and their engineers had more shop floor skills.

The new technologies entering manufacturing mean that traditional roles are changing and overlapping, says Briana Martin, senior manager in Deloitte Consulting’s Future of Work group. “The constant influx of technology is mutating traditional jobs into hybrid jobs that require skill sets from multiple domains and functions that never used to be found in the same job description, dispelling a common misconception that technology is requiring a higher level of technical skills only,” Martin says.

A new mix of capabilities will be in demand in advanced manufacturing, including new technical proficiencies and specialized process skills in addition to some very human attributes. The following are likely to become more important in the coming years.

Increased flexibility

In an environment of frequent and ongoing change, manufacturers will need fewer experts in a specific machine or process and more employees who are adaptable, curious, and eager to acquire new knowledge.

Manufacturing requires people to frequently learn new skills. “The quickening pace of technology development and implementation dramatically reduces the shelf life of training and increases the lag between industry workforce needs and workforce training,” says Liu. “To keep up, workers must constantly learn new skills, and advanced manufacturing training must adapt, grow, and anticipate them.”

Companies will play a lead role in helping their people adapt by fostering a culture of continuous learning, Liu says. One method he advises is to provide a “hub” of core capabilities that employees need (an understanding of manufacturing processes and systems) along with “spokes” of skills that allow workers to excel in specific technology domains that can be updated as technologies evolve, such as additive manufacturing and robotics.

Complex problem-solving

As production environments change, companies will need workers who can recognize or anticipate issues, analyze situations, and determine the most effective solutions. One of the most important skills that manufacturing operators will need to add to their tool sets is complex problem-solving.

The role of machinists is an important illustration of this trend. Perhaps no role has made more of a shift in recent decades as it has morphed from manually operating machines to working with computer numerical control machines that are based on preprogrammed software to working alongside cobots performing material handling.

“[Machinists] went from handling a mill or a lathe to running the program to a future when they may have a cell of multiple machines to manage,” says Ben Armstrong, executive director at MIT Industrial Performance Center. “They will be responsible for a whole system and have to think about how to optimize it.”

Machine operators who once specialized in their assigned machines or products will be more flexibly deployed, leveraging digital tools and AI decision support to proactively identify and solve issues, says Armstrong, who also co-leads MIT’s Work of the Future, an initiative that studies the history of new technology introductions.

Business acumen

Current training for many manufacturing personnel emphasizes the “how” of manufacturing operations. But for workers to thrive in an advanced manufacturing environment in which greater numbers of intelligent and networked systems will be introduced, they’ll need a good grip on the “why” as well.

MIT has developed a framework for engineering students that emphasizes the key components of production: manufacturing processes, manufacturing systems, supply chains, and people. These enduring principles are industry agnostic, says Liu, and are as relevant for traditional machine shops as state-of-the-art semiconductor foundries. This core knowledge set can help workers evaluate developing technologies and understand how to make their companies more flexible and resilient.

Appetite for innovation and experimentation

The adoption of no-code/low-code operations platforms could enable more production personnel to participate in the ongoing optimization and transformation of production. Think robotic process automation for the shop floor.

“Meaningful decision-making will not be reserved for the manufacturing engineer or plant manager,” Liu says. “With full access to data, frontline workers can create their own apps to find insights and optimize their own tasks.”

Evidence from surveys shows that workers want to participate in these ways today, but few do, says Armstrong. Democratizing innovation and unleashing self-service automation could be empowering to the production personnel and drive operational growth, productivity, and efficiency. “This bottom-up approach to deploying new technology can deliver the greatest automation opportunities because they are based on workers’ input,” Armstrong says.

Specializations central to advanced manufacturing

In addition to a mix of new capabilities, advanced manufacturing teams will need people who have specialized experience and skills to help companies innovate and seek ways to enhance performance.

Design thinking

Design thinking got its start as an approach to new product and service development based on customer- or user-focused practices and ongoing prototyping. At its core, design thinking chips away at the biases that can get in the way of innovation, so it can be a valuable approach for those seeking to transform production facilities. Design thinking can help production leaders, managers, and planners develop human-centric ways to reframe problems or needs and then design and test advanced manufacturing solutions.

Continuous improvement

The aim of introducing technologies such as AI and machine learning, digital twins, robotics and automation is to increase output, efficiency, quality, and flexibility. Thus, the ability to evaluate performance and design processes for continuous improvement will be key.

One approach to meeting this need in advanced manufacturing facilities could be a new breed of technologist, says Liu. A heavier shop floor may not be a good fit for engineers, and manufacturing technicians may lack the skills to take on this kind of work. Liu says a resilient and empowered technologist possessing a technician’s know-how and an engineer’s understanding of processes and systems could play a key role in the future. It’s the type of role for which technicians could receive training to gain the right skills.

Cobot collaboration

The future of manufacturing will depend on an advancing relationship between humans and machines.

Today’s “coexistence” between workers and robots sharing the same space while performing separate, sequential tasks is likely to evolve. Humans and robots may begin with cooperation (working on the same piece at the same time) and eventually collaborate, with factory personnel working with cobots capable of real-time response on shared goals.

“Imagine if cobots could sense, predict, and respond in real time to human guidance, fatigue, and movement,” says Liu. For example, a cobot could sense a machinist tiring in the task of polishing a complex surface. Having learned from the human the applied load, parameters, and optimal trajectories of the sander, the cobot could step in and give the machinist some time to rest. “The human remains present to perform aspects of the collaborative task that the robot cannot fully take over and maintains the overall supervision,” says Liu.

This would drastically decrease the physical demands of nonrepetitive tasks in jobs like welding, assembly, and painting but also would increase the cognitive demands on the human worker. “This new paradigm of work has a heavier emphasis on supervision, responsibility, and decision-making,” Liu says. “This consequently raises the bar on worker competence.”

Software, analytics skills also in demand

Advanced manufacturing will require people who can code and know their way around data, although this is not a traditional staple of factory floors. Specifical skills will include:

Software skills

Today, very few manufacturing job postings—around one in five—mention software skills, which is astonishingly low compared with other industries, says Armstrong. Hardware competencies still reign supreme in manufacturing, compared with other fields such as retail, finance, and healthcare. As increasingly more machines come under software control, that will need to change.

“One thing that I suspect will transform over the next 20 years is that almost every manufacturing job will require some software skills,” Armstrong says.

Everyone in a semiconductor control room is working with software. Eventually, more shop floor personnel in advanced manufacturing facilities will be software fluent as well. Armstrong has seen manufacturers that require more software knowledge for everyone on the factory floor in order to best operate in a production system that has a common software thread tying it together—but thus far, that’s the exception, not the rule, he says.

Data analytics and visualization

Advances in data and analytics—more automated data collection from connected machines, advances in AI and machine learning, and predictive capabilities—will transform a variety of roles in the factory of the future. Planners and line leaders, specifically, will work directly with data and analytics to perform their roles, says Martin of Deloitte Consulting.

Today’s planners tend to manage issues reactively, often still using offline spreadsheets, and perform repetitive scheduling activities. In an advanced manufacturing setting, they will use predictive analytics to create optimized production schedules and prevent supply chain problems, for example. They’ll spend more time looking for continuous improvement opportunities in data than fighting fires. Line leaders likewise will evolve from manually coordinating (collecting data collection, generating reports, assigning tasks) to using intelligent automated dashboards to identify and prevent issues. This will free them up to spend more time on the shop floor coaching their teams and solving real-time problems.

Digital simulation

As the rest of the world is still working out the potential value of the metaverse, manufacturers are pulling far ahead in what some now call the industrial metaverse.

Manufacturing applications were some of the first use cases for digital twins—those virtual replicas of real-life systems that can model, simulate, monitor, analyze, and optimize the physical world—and the manufacturing sector is ready to take this to the next level.

“Many advanced factories will have a dual existence—one in the physical space and one in the virtual space,” says Liu. “This is a continued evolution of the digital representation of whole systems composed of environments, products, processes. Think digital twin, but now instead of the typical single machine or process, it could be a whole factory, transportation system, or energy grid.”

It will be a step change in power and complexity, creating increased demand for skills in digital twin and digital threat management. Today, digital twin engineers largely focus on developing ways to streamline and automate production processes to make them more efficient. In the digital future, says Martin, they’ll “create a virtual representation of both the physical elements and the dynamics of how an Internet of Things–connected product operates and interacts within its environment throughout its entire life cycle.” Liu points out that the development of the industrial metaverse will have effects beyond these newer roles. “Frontline operators would also need to work with data, visualization, and mixed reality,” he says.

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