The Role Of AI In Preserving Knowledge In Manufacturing Industries

Dr. Satyandra K. Gupta: Co-Founder, Chief Scientist, GrayMatter Robotics; Smith Int’l Professor & Dir. CAM USC; Fellow ASME, IEEE, SME, SMA.

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​Most manufacturers consider AI an important technology that can be used to improve factory operations. However, many manufacturers are adopting a wait-and-see approach to implement AI.

​Implementation Delays

Here are the most common reasons given to delay the implementation of AI:

Workforce Resistance

Employees fear that AI will render their roles obsolete, causing resistance to implementation. It is challenging to implement AI without buy-in from the employees.

Unclear ROI

Proving the financial return on investment based on the reduction in operational cost is difficult. The upfront cost of implementing changes and mitigating risks is high. Many potential benefits of AI are difficult to quantify in terms of cost reductions. So, it is hard to accurately compute ROI on AI deployment.

Trust Concerns

Lack of explainability often makes it hard for operators to trust AI recommendations. This leads workers to ignore AI and execute tasks manually to mitigate risks. Lack of trust delays AI adoption.

Implementing AI has a major hidden benefit for manufacturing enterprises—it can become a custodian of organizational knowledge. Unfortunately, this benefit is not directly quantified in ROI calculations and is often overlooked.

The Knowledge Deficit​

The retirement of Baby Boomers and Gen-X is expected to create a major challenge for the manufacturing industry. With nearly one-third of the workforce over the age of 55, the industry is bracing itself for a large-scale exit of highly experienced personnel whose expertise has been built over decades. As experienced workers retire, they take with them knowledge that is often not written down and difficult to replace. This includes tacit expertise developed through years of hands-on work, such as understanding material and process interaction behaviors, tool responses and real-time process adaptation. This knowledge spans physical, cognitive and experiential domains; therefore, it does not transfer easily through manuals or training programs. New workers need to spend significant time with experienced workers to absorb manufacturing knowledge.

For Millennials and Gen-Z, the manufacturing sector conjures images of a dull, dirty and demanding workplace. On top of that, manufacturing work lacks the flexibility and remote options that younger workers expect and demand today. Because of this, the industry struggles to recruit the next generation to replace retiring workers in the manufacturing sector. With fewer young workers entering the field, we cannot rely on knowledge transfer to new workers as a means of preserving vital manufacturing knowledge in today’s climate.

Companies are forced to raise wages and increase benefits to compete for talent, often passing these costs on to customers. This makes it hard for companies to compete. Although rising wages and improved benefits are a natural outcome of labor shortages, the overall cost pressures facing manufacturers extend beyond worker compensation alone. Slower execution due to labor shortages increases labor cost per product, reduces overall efficiency and delays delivery. Additional efforts are required for quality assurance when relying on a new and inexperienced workforce.

Many organizations are retaining older workers longer and attempting to rebrand manufacturing careers. While valuable, these strategies may not be sufficient. Extending careers delays workforce loss but does not address long-term demographic trends or the inevitability of workforce transition. Without a scalable way to capture knowledge, these efforts will not fully address the underlying problem. Retirement of experienced workforce in manufacturing creates a significant risk of knowledge loss and is likely to impact consistency, efficiency and adaptability on the factory floor.

The AI Archive​

AI can play a critical role in capturing and archiving knowledge that has traditionally existed only in the minds of experienced workers. Instead of losing expertise when individuals retire, organizations can embed it into AI systems that will remain with the organization and can be used by new employees.

AI can play the following roles in archiving and transferring knowledge:

Knowledge Capture And Documentation

AI-driven tools, such as video analysis and smart notetakers, can record and summarize expert interviews, transcribe voice notes during repairs and automatically generate standard operating procedures. Videos of manufacturing procedures can be recorded, annotated and archived by AI. Generative AI tools can be used to generate instruction videos based on the archived information.

Pattern Identification And Prediction

AI can learn to mimic the expertise of seasoned operators by analyzing historical sensor data to identify patterns in machine behavior, such as vibration or temperature shifts, that indicate a likely failure before it actually occurs.

Information Retrieval And Delivery

AI can instantly retrieve relevant technical specifications, troubleshooting steps or maintenance logs and deliver them to new workers when they need to make a decision on the factory floor. Unlike static manuals, AI-driven knowledge management systems can create living repositories that continuously learn from new design, production and quality data, creating a repository that improves over time.

Faster Training

By using AI-powered simulations and augmented reality tools, manufacturers can compress onboarding times, allowing new hires to benefit from archived expert knowledge and reach proficiency faster.

Closing Thoughts​

Waiting for the AI ROI to become favorable in the traditional sense may not be the right strategy to make a decision to deploy AI because the organization may lack people and knowledge to train the AI by the time they are ready to implement it. AI offers a viable path forward by transforming knowledge of the retiring workforce into a continuously improving, persistent knowledge asset. This alone can be an adequate reason to stop waiting and implement AI in manufacturing.​

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