Bryan Merckling, CEO at Thinaer.
getty
According to the World Economic Forum, the global manufacturing AI market was worth over $3 billion in 2023 and is expected to increase to over $20 billion by 2028. The use cases grow each day with new technological advancements, including automated supply chains, enhanced product quality checks, improved personnel safety and predictive machine maintenance, among many other benefits.
Although generative AI use cases are largely known for simulating human intelligence, the advanced technology is beginning to revolutionize the industrial sector. Generative AI-based technology can be embedded into machine learning technologies to complete tasks in a more efficient manner. This can be used to quickly change production cycles, handle multiple complex products simultaneously or even alert employees of machine health and potential safety hazards in real time.
AI-powered machine learning technology appears to be the leader of the Fourth Industrial Revolution in manufacturing. However, the technology is only as useful as the data it’s fed. Manufacturers must first collect terabytes of data throughout the shop floor via machine sensors, cameras and interconnected systems. Once collected, the data must be contextualized to give it valuable meaning for manufacturers, which will set the foundation for how useful AI initiatives will be.
Roadblocks In AI Adoption
Investments in technology, infrastructure and training can range from tens of thousands to hundreds of thousands of dollars, depending on the size of the company and the type of initiative. A business looking to implement AI must invest in software, MES systems, data collectors (such as sensors) and regular maintenance in addition to training employees on how to properly use the new technology. After initial deployment, it could take months before seeing valuable ROI. This is a major bottleneck for small to midsize companies that don’t have adequate budgets or time to invest.
However, the commoditization of AI is making it more affordable for businesses. AI systems can now be purchased out of the box or incrementally implemented to help with accumulating costs. Gradual adoption can also allow manufacturers to see potential ROI before fully investing in the technology.
Additionally, many manufacturing firms lack the expertise needed for effective AI development, implementation and maintenance. According to the U.S. National Association of Manufacturers, 71% of CEOs cite the inability to attract and retain workers in the manufacturing sector as the major challenge affecting their business. This phenomenon is commonly referred to as the “manufacturing skills gap.” Older generations of workers are retiring, making it difficult to transfer tribal knowledge to younger workers.
This institutional knowledge retained by employees over the years is essential to training, reskilling and upskilling new employees. To add to this, there is a high demand for AI-skilled talent across all industries that manufacturers must compete with to attract and retain those workers.
Data quality and availability present another challenge, as AI algorithms require large amounts of high-quality data. A 2023 survey by the Manufacturing Leadership Council found that 65% of survey respondents made it clear that data was the biggest culprit to adoption. A company’s data foundation determines whether its Al project will work. Yet traditional industries lacking robust data foundations must first enhance their digitalization levels through business process improvement before they can effectively apply AI.
Many manufacturers also struggle to collect adequate data to feed their AI initiatives. This leads to a skewed dataset that is not representative of their entire smart factory. Others might have plenty of data but struggle to contextualize it. Integrating AI with existing systems can be complex due to compatibility issues, data silos and legacy systems.
Contextualizing data is key, as it gives the data meaning and transforms it into actionable information that can be used to help make decisions. With the help of MES and interconnected systems, manufacturers can gain an understanding of their data and the blind spots on their shop floor.
The Road To Industry 4.0
The combination of digital technology and AI has the potential to usher in a new era of continuous improvement in manufacturing, surpassing traditional methods of boosting productivity and unlocking additional value. AI-powered machine learning technology presents exciting opportunities for the future of Industry 4.0. While many top manufacturers have already reaped significant benefits from AI applications, others are just beginning their journey.
However, this advanced technology does not come without its challenges. Business leaders should be well-educated on the potential roadblocks before jumping on the AI train. Successful implementation relies heavily on collecting and contextualizing mass amounts of data across the manufacturing shop floor. This will lay the foundation for the technology’s potential and be a major competitive differentiator for those who properly use it.
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