Audience Opinion Piece

Why is it important for manufacturers to become up to speed on AI... and what roadblocks should they be aware of along the way?

AI is the hot topic in manufacturing today. This is not surprising, given that it can be applied practically everywhere, from the shop floor to the back office.

Why is AI so hot?

AI is not just a tool, it's a game-changer. It automates repetitive tasks, freeing up workers to focus on strategic activities.

· Faster Production & Predictive Maintenance

AI-driven robots operate 24/7, ensuring consistent output and reducing production times. By analyzing equipment data, AI predicts maintenance needs, preventing breakdowns and minimizing downtime, which extends equipment life and reduces maintenance costs.

· Product Quality & Inventory Management

AI accelerates R&D by analyzing data and identifying trends, which reduces the time and cost of product development and testing. AI systems monitor and inspect products in real time, detecting anomalies and ensuring high quality. Additionally, AI predicts demand, manages inventory, and ensures timely deliveries by analyzing trends and data, which reduces overproduction and stockouts. AI enables the production of customized products and quickly adapts to specification changes, allowing for greater flexibility.

· Competitive Edge

AI optimizes processes, reducing operational costs through efficient resource utilization, energy savings, and reduced waste. By adopting AI, manufacturers enhance operations, improve product quality, and streamline supply chains, providing a competitive edge. Furthermore, AI leads to sustainable production practices through its ability to optimize resource use, waste reduction, and lower energy consumption.  

Not just a trend…

Viewing AI as a universal solution may be misguided; instead, consider it as a catalyst that enhances and accelerates the absorption of valuable insights and efficiencies.

AI is not a fleeting trend but rather a significant advancement in the evolution of manufacturing. It represents a transformative advantage that will become increasingly prevalent in the future. We can draw a parallel to the transition from the Bronze Age to the Iron Age. Civilizations that adopted iron technology gained significant advantages over those that remained reliant on bronze, ultimately leading to widespread adoption of the new technology or the conquest of those who resisted change.

Similarly, in today's manufacturing landscape, those who embrace AI and the associated data-driven culture will outperform those who cling to traditional methods. I foresee that within the next three years, AI will be integrated into the operations of virtually every manufacturer. Those who fail to adapt will inevitably fall behind.

Watch out for the bumps in the road...

However, blindly following the trend and implementing AI indiscriminately can be counterproductive and may lead to early failures. Investing more in thorough planning upfront, including consulting with external trusted experts, can help you avoid "pilot purgatory" and yield significant long-term savings.

While deploying AI across a manufacturing site is complex, equally challenging is the need to adjust corporate culture, mindset, and provide adequate training. Embracing change and fostering the right mindset is crucial. Even the best technology will fail if the workforce is resistant to it.

Like what we emphasized about Industry 4.0: success hinges on People, Processes, and Technology—in that order!

Typical roadblocks that manufacturers face when working to evolve their organizations towards a data-driven culture are:

· First and foremost: Humans

As mentioned above, shop floor workers and office employees often fear that new technology will eventually replace their jobs. This fear leads to resistance rather than embracing the potential benefits of technology in simplifying their tasks.

· Second: Machines

Manufacturing equipment represents significant capital investment and is expected to remain operational for long periods to justify this investment. Many machines have life spans of 10, 20, 30 years or more and were not designed with Industry 4.0, AI, and data-driven decision-making in mind. Consequently, it can be challenging to extract the necessary data from these machines.

· Third: Humans again

New technology often comes with high expectations, but once implemented, it may not fit perfectly, and the results can fall short of expectations. The lesson here is that there is no one-size-fits-all solution. Proper preparation and understanding of your specific needs are crucial before adoption.

· Fourth: Communication

Effective communication is vital when implementing new technologies that impact broad processes. Ensuring and maintaining clear communication between individuals and departments is essential for successful adoption.

These are typical roadblocks. However, few consider what happens after successfully implementing an innovation project that introduces data-driven decisions. Initially, things may go well, but occasionally, unexpected decisions occur, leading to confusion and skepticism about the technology. If these decisions result in significant setbacks, acceptance of the new technology—and any future innovations—can decline sharply.

Why does this happen? Not every AI model is flawless. Even the best applications cannot guarantee that their models are error-free. Without a method to ensure that the model is unbiased and accurate, you risk making poor decisions based on flawed AI models. In manufacturing, particularly, a bad decision can lead to substantial losses.

However, in summary, AI is transforming the manufacturing industry by driving efficiency, improving quality, reducing costs, and enabling innovation. Manufacturers who embrace AI are undoubtedly better equipped to meet the demands of a rapidly changing market and gain a competitive advantage.

Marcel Schaefer, Senior Research Scientist & SCFA Senior Program Coordinator, Fraunhofer USA

Dr. Marcel Schäfer serves as Senior Research Scientist for the Fraunhofer USA Center Mid-Atlantic CMA in Maryland since January 2019. From 2009 to 2018 he was with Fraunhofer Institute for Secure Information Technologies SIT in Germany. He owns a master’s degree in mathematics from the University of Wuppertal, Germany, and a PhD in computer science from the Technical University of Darmstadt, Germany. As PI, Co-PI and researcher Dr. Schäfer has led and worked in various projects that discover new challenges and opportunities broadly spread over the fields of cybersecurity and software engineering in both the public and private sector.Since March 2021 he is leading the Fraunhofer USA office in South Carolina as Senior Program Coordinator for Fraunhofer USA’s activities within the South Carolina Fraunhofer USA Alliance. Projects so far cover a broad spectrum ranging from smart manufacturing, IoT, Industry 4.0, digitization to predictive maintenance, predictive analytics and other data driven and machine learning featured technologies. Typically, those projects have a strong manufacturing focus.