Manufacturing Blog: Workforce Skill Gaps Makes 4IR Inevitable
To meet ever-changing market demands, manufacturing processes need to be increasingly efficient, flexible, and intelligent. Industry 4.0 (4IR), almost nonexistent in 2014, may finally be fully embraced by manufacturers that face increased professional workforce skills gaps and other significant challenges amid growing production constraints.
4IR is the digitalization of the manufacturing sector and includes disruptive technology and tools that embrace such systems as data analytics, internet of things (IoT), artificial intelligence and machine learning (AI/ML), and advanced robotics and automation. These interconnected schemes use business operational data and real-time information from networked smart sensors to apply analytics and instruct automated systems to reach a given outcome.
A clear space occupied by AI and similar technologies is the ability to better streamline product development. CAD coupled with 3D printing systems, barely 40 years old, can now generate thousands of “potential” designs. Each iteration works toward optimizing such considerations as weight, strength, and cost. Despite a skills labor shortage of those who are experts who can drill, cut, and mill, these systems produce workable part prototypes.
Supply chain optimization is also a clear technology solution that allows manufacturers to get more done as they streamline how they manage inventory and forecast for the future. Cobots are now found in warehouses and on many production floors often performing repetitive, boring tasks or more labor-intensive jobs. Part of a warehouse automation strategy, they use efficient layouts that minimize bottlenecks and maximize productivity. The bottom line is that manufacturers are able to sustain production with a smaller labor force.
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These systems also offer manufacturers greater insight into their processes. Predictive maintenance, for example, allow organizations to predict when machinery is likely to fail. These systems reduce downtime and allow organizations to schedule labor and maintenance in an optimal way. Moreover, a digital twin, for example, could generate scheduling with the ability to plan and optimize production. These digital models mirror real-world conditions. With less workers, manufacturers can test adjustments in an environment. So engineers can now understand how a component or product, or a process or system will impact them using a dynamic model.
In 2024 experts expect accelerated digital transformations of organizations. Emergen Research for example, reported the global 4IR market will reach $279.75 billion by 2028. When it comes to AI, 2024 State of Manufacturing Report’s survey of engineering, supply chain, manufacturing, and product development leaders found 88 percent of respondents report implementation in their operations. This need will be great since, according to the U.S. Department of Commerce, the U.S. manufacturing sector accounts for 10.3 percent of the nation’s GDP and contributes $2.65 trillion to the U.S. economy, and now employs nearly 13 million American workers and growing.
More for You: In 2024 Manufacturing Means Embracing AI
Yet, some researchers are warning that AI/ML is not infinite. The proper application of machine learning to robotic control systems starts with understanding what they can’t do, experts are telling those manufacturers that will listen. In fact, recently, some of that AI "scaling law" optimism has been replaced by fears that we may already be hitting a plateau in the capabilities of large language models trained with standard methods.
And if technologies don’t have innate challenges, each organization must find solutions to such daunting situations as significant expenditures, difficult training, and a massive cultural shift. Despite all this, most manufacturers have come to see that they must embrace digital transformation. Experts also offer other challenges beyond the need for tons of data that need to be clean and curated. Other considerations are integration with existing systems and the cost of that incorporation.
But while such technologies are expensive in themselves, the cost of reskilling and upskilling are also a big consideration. Some manufacturers are bridging the gap by using AI to accelerate training and make educating workers customized to each individual’s need. For example, organizations use this technology to generate more effective training sprints in manufacturing environments. And safety training often uses personal protective equipment to monitor worker compliance and direct training in real-time.
Cathy Cecere is membership content program manager.
4IR is the digitalization of the manufacturing sector and includes disruptive technology and tools that embrace such systems as data analytics, internet of things (IoT), artificial intelligence and machine learning (AI/ML), and advanced robotics and automation. These interconnected schemes use business operational data and real-time information from networked smart sensors to apply analytics and instruct automated systems to reach a given outcome.
Supply chain optimization is also a clear technology solution that allows manufacturers to get more done as they streamline how they manage inventory and forecast for the future. Cobots are now found in warehouses and on many production floors often performing repetitive, boring tasks or more labor-intensive jobs. Part of a warehouse automation strategy, they use efficient layouts that minimize bottlenecks and maximize productivity. The bottom line is that manufacturers are able to sustain production with a smaller labor force.
Discover the benefits of ASME membership
These systems also offer manufacturers greater insight into their processes. Predictive maintenance, for example, allow organizations to predict when machinery is likely to fail. These systems reduce downtime and allow organizations to schedule labor and maintenance in an optimal way. Moreover, a digital twin, for example, could generate scheduling with the ability to plan and optimize production. These digital models mirror real-world conditions. With less workers, manufacturers can test adjustments in an environment. So engineers can now understand how a component or product, or a process or system will impact them using a dynamic model.
In 2024 experts expect accelerated digital transformations of organizations. Emergen Research for example, reported the global 4IR market will reach $279.75 billion by 2028. When it comes to AI, 2024 State of Manufacturing Report’s survey of engineering, supply chain, manufacturing, and product development leaders found 88 percent of respondents report implementation in their operations. This need will be great since, according to the U.S. Department of Commerce, the U.S. manufacturing sector accounts for 10.3 percent of the nation’s GDP and contributes $2.65 trillion to the U.S. economy, and now employs nearly 13 million American workers and growing.
More for You: In 2024 Manufacturing Means Embracing AI
Yet, some researchers are warning that AI/ML is not infinite. The proper application of machine learning to robotic control systems starts with understanding what they can’t do, experts are telling those manufacturers that will listen. In fact, recently, some of that AI "scaling law" optimism has been replaced by fears that we may already be hitting a plateau in the capabilities of large language models trained with standard methods.
And if technologies don’t have innate challenges, each organization must find solutions to such daunting situations as significant expenditures, difficult training, and a massive cultural shift. Despite all this, most manufacturers have come to see that they must embrace digital transformation. Experts also offer other challenges beyond the need for tons of data that need to be clean and curated. Other considerations are integration with existing systems and the cost of that incorporation.
But while such technologies are expensive in themselves, the cost of reskilling and upskilling are also a big consideration. Some manufacturers are bridging the gap by using AI to accelerate training and make educating workers customized to each individual’s need. For example, organizations use this technology to generate more effective training sprints in manufacturing environments. And safety training often uses personal protective equipment to monitor worker compliance and direct training in real-time.
Cathy Cecere is membership content program manager.
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