The industrial sector is wrestling with a critical issue as seasoned professionals retire, taking with them decades of valuable operational technology (OT) expertise. This phenomenon, known as industrial “brain drain,” is causing significant disruptions, especially in the manufacturing industry. The rapid departure of experienced workers is creating a daunting challenge to maintain operational efficiency and safety standards. The manufacturing industry, particularly, finds itself on the brink of a potential operational crisis as it struggles to adapt to a dwindling pool of expertise.
Understanding the Industrial Brain Drain
The Extent of the Problem
Data from LNS Research Associates highlight a stark decrease in workforce retention and experience levels in the manufacturing industry. In 2019, the average tenure of manufacturing workers was 20 years, which dramatically dropped to an average tenure of three years by 2023. The retention rate of new manufacturing personnel also suffered a decline from 90% to 50% within just four years, underscoring the urgency for solutions. This alarming trend signals a critical loss of institutional knowledge, the consequence of which is a significant challenge for today’s and future workforce to step up and meet operational demands.
Impact on Operational Efficiency
As the experienced workforce retires, the loss of specialized knowledge and expertise adversely affects the operational efficiency of manufacturing plants. This trend is resulting in increased downtime, reduced productivity, and compromised safety standards. Without the presence of veterans who have accumulated insights on the nuances of plant operations, troubleshooting processes can become protracted, increasing operational costs and impacting timelines. The dwindling pool of senior professionals poses a threat to the continuity and smooth functioning of industrial operations. In the long run, failure to address brain drain could threaten the sustainability and competitive edge of manufacturing firms.
The Role of AI in Capturing Expertise
Introduction to AI Solutions
According to Bryan DeBois, director of industrial AI at RoviSys, AI can play a pivotal role in mitigating the effects of brain drain by retaining and scaling critical knowledge. By leveraging machine teaching and learning algorithms, AI systems can capture the expertise of retiring professionals and utilize it to maintain operational standards. These AI solutions enable the documentation and replication of best practices, ensuring that valuable knowledge is preserved for the next generation of workers. Furthermore, AI technologies offer a scalable solution to capture the procedural and experiential knowledge honed by years of hands-on experience.
Traditional AI
Traditional AI algorithms, which have been in use for two decades, are powerful tools for capturing industrial expertise. This category includes unsupervised and supervised learning models. Unsupervised learning can detect anomalies by identifying deviations from normal operations, offering insights into potential system flaws without needing predefined labels for the data. In contrast, supervised learning uses historical data to train models that predict specific outcomes, making it highly effective in applications such as predictive quality control. By predicting the final quality of a batch before its completion, these models allow operators to intervene proactively to ensure consistent output quality.
Autonomous AI
Autonomous AI takes these capabilities a step further by autonomously addressing recognized problems. Through deep reinforcement learning, autonomous AI can make decisions and learn continuously from data inputs and past experiences. This AI variant automates complex tasks, ensuring consistent application of expertise without human limitations like fatigue or availability. Its ability to adaptively learn and make decisions enhances its applicability in highly variable environments where traditional control systems may falter. Autonomous AI agents, often referred to as “brains,” leverage past data to guide current operations efficiently, incorporating human-like reasoning to optimize processes such as production scheduling and raw material utilization based on real-time market demands.
Types of Industrial AI
Generative AI
Generative AI, such as ChatGPT, excels in natural language processing, though its application in critical industrial settings remains limited. Experts like DeBois caution against using generative AI in plant operations due to the risks of inaccuracies and fabricated information, which could lead to equipment failure or safety hazards. Although generative AI holds promise in tasks such as drafting reports or generating maintenance workflows, its inability to consistently produce accurate and reliable outputs makes it unsuitable for critical applications where precision is paramount. Current generative AI models must overcome significant reliability concerns before being trusted with high-stakes industrial processes.
Expert Opinions on AI Technologies
ISA Fellow Jonas Berge of Emerson further categorizes AI technologies into causal AI, machine learning, and deep learning. Causal AI embeds principles of physics and chemistry into operational models, providing a robust framework for predicting equipment behavior and process efficiency. Machine learning, on the other hand, focuses on identifying correlations within historical plant data to anticipate anomalies and forecast operational trends. Deep learning, which forms the basis of many advanced AI systems, excels at extracting patterns from vast amounts of data. While generative AI has faced criticism for its limitations in critical operations, it showcases potential for future development in areas where less stringent accuracy requirements exist.
Implementing AI in the Industrial Sector
Building a Case for AI Implementation
Effective AI implementation in the industrial sector requires an incremental approach, starting with small projects that demonstrate quick wins. According to DeBois, a “snowball” effect of return on investment (ROI) can be achieved by iteratively solving problems and expanding successful implementations. This strategy helps industries integrate AI without overwhelming their systems or personnel. By focusing on specific, high-impact use cases, companies can build a portfolio of successful AI projects that bolster confidence in broader deployment efforts. Such an approach not only reduces the initial risk but also provides valuable experience and insights that can be applied to more complex AI integrations.
Proven Use Cases
Specific AI algorithms that address well-defined use cases can lead to significant improvements in operational efficiency and safety. Successful projects serve as proof points, encouraging broader AI adoption across various industrial operations. By focusing on practical applications and demonstrating measurable benefits, industries can overcome resistance to AI integration. Case studies in areas such as predictive maintenance, quality control, and supply chain optimization illustrate how AI can drive operational advancements. These success stories highlight not only the potential for improved efficiency and reduced downtime but also the importance of methodical implementation strategies that prioritize real-world impact and employee buy-in.
Leveraging AI to Preserve Expertise
Mitigating the Effects of Brain Drain
By capturing the expertise of retiring professionals through AI, the industrial sector can mitigate the impact of brain drain. Autonomous AI, in particular, offers a robust solution, enabling consistent decision-making processes and automating complex tasks while preserving critical knowledge. The ability to codify and deploy expert knowledge through AI ensures that valuable insights are not lost but instead enhanced and distributed across the workforce. This transition allows newer employees to benefit from the expertise of their predecessors, facilitating continual improvement and innovation within the industry.
Preparing for the Future
The industrial sector is grappling with a significant issue as experienced professionals retire, taking with them decades of invaluable operational technology (OT) knowledge. This occurrence, often referred to as the industrial “brain drain,” is resulting in serious disruptions, particularly within the manufacturing industry. The swift exit of these seasoned workers presents a formidable challenge in upholding operational efficiency and safety standards. Manufacturing, in particular, is facing a potential crisis as it contends with a shrinking pool of expertise.
With these retirements, companies lose not just seasoned hands but vast amounts of tacit knowledge. This shift makes it harder to train new employees quickly enough to fill the gap. Moreover, this brain drain jeopardizes the industry’s ability to innovate and maintain high productivity levels. Many companies are now scrambling to implement knowledge transfer programs, document processes meticulously, and rely more on automation and advanced technology. However, while technology can help, it cannot entirely replace the nuanced understanding that veteran workers possess.
This issue forces the industry to rethink workforce strategies, invest in employee training, and create retention programs to maintain their older talent. Addressing this situation is critical for sustaining not only the current operational standards but also for ensuring future growth and adaptability in an ever-evolving technical landscape.
