July 3, 2024 Can Manufacturers Use GenAI to Leverage Growth?

Manufacturers are seeing growth for the first time in several years—and they’ll need AI to sustain it.

By Shipra Sharma, Head of Analytics and AI, Bristlecone

Recent data reveals a promising trend: U.S. manufacturing is on the rise for the first time since 2022. This resurgence is occurring alongside the rapid advancement of artificial intelligence, particularly generative AI (GenAI). To seize the opportunities presented by this technological growth, organizations must leverage these innovations to optimize their manufacturing supply chains. While there is a clear desire to embrace new technology, many organizations still lack a comprehensive understanding of its implementation, training, and other critical aspects.

As the manufacturing sector recovers, the successful integration of GenAI and AI analytics will be crucial moving forward. These tools can process and integrate powerful data points, providing a comprehensive view of supply chain operations. This capability simplifies navigating issues like material shortages, making the process faster and more cost-effective. With pressing issues still at large, organizations must utilize all available resources to sustain this new growth.

What’s the Holdup?

Despite the apparent enthusiasm for AI present in headlines everywhere, many organizations face significant barriers to implementation. A recent survey found that 91.3% of respondents plan to leverage GenAI, yet most could not specify the resources they would use for its implementation, with many simply stating they were “unsure.” Further, only 12% of respondents reported personally using GenAI, highlighting a common trend in new technology adoption where initial excitement fades when practical application challenges rear their ugly heads.

This gap between interest and implementation is a classic feature of every new technology hype cycle. When a groundbreaking technology emerges, it generates widespread excitement and ambitious plans. However, when it comes time to apply these technologies, organizations often face complexities and drop the issue. This leads to grand plans being shelved—unless organizations call the experts.

For the manufacturing industry, which is currently experiencing its first growth spurt in years, this kind of hesitation and implementation misfires can be particularly costly. Organizations must move beyond the excitement phase and develop concrete strategies for integrating GenAI into their operations. This involves investing in the right tools, training employees, and fostering a culture of innovation and adaptability.

What Stands to be Gained?

The potential benefits of integrating GenAI into manufacturing processes are substantial. However, manufacturers around the world continue to grapple with things like high input costs, supply uncertainties, and weakening demand. These challenges are further exacerbated by factors like inflation and ongoing labor shortages. Because of this, organizations must capture maximum value from their manufacturing processes to stay competitive.

Assessing, optimizing, and enhancing current manufacturing operations will be key. This means adopting new technologies, is this case AI, and refining existing processes to meet the demands of a rapidly evolving industry. Despite initial costs and the complexity of integration, these improvements can generate up to $3.7 trillion in value by 2025. The significant return on investment makes the endeavor worthwhile.

Furthermore, the implementation of GenAI can streamline and enhance various aspects of manufacturing. By leveraging advanced AI tools, organizations can achieve higher levels of efficiency, reduce waste, and improve overall productivity. These improvements can help manufacturers navigate the challenges posed by supply chain disruptions and fluctuating market conditions, ensuring sustained growth and competitiveness.

GenAI, AI Analytics, and Cognitive Manufacturing

What do practical applications of GenAI and AI analytics in manufacturing actually look like?

1. Quality Control:

AI can monitor production processes in real-time, detecting defects and anomalies that may affect product quality. By identifying these issues early, manufacturers can make necessary adjustments before defective units are produced, reducing waste and ensuring higher quality standards.

2. Maintenance:

AI systems can continuously monitor equipment and detect changes in key parameters such as temperature, pressure, and vibration. By identifying potential issues before they lead to equipment failure, AI-driven maintenance can prevent costly downtime and extend the lifespan of machinery. Predictive maintenance models can schedule repairs and maintenance tasks proactively, ensuring seamless operations.

3. Safety and Efficiency:

AI can enhance workplace safety by monitoring working environments and performing automated safety checks. This helps prevent hazards and ensures compliance with safety regulations. Additionally, AI can optimize worker movements and processes, leading to increased efficiency. Wearable IoT devices can provide data-driven insights, allowing for further optimization of workflows and enhanced productivity.

Advanced analytics will ensure that actionable insights are derived from sensor data, facilitating easier technology adoption. This will enhance the overall efficiency of the manufacturing sector, ensuring smoother operations and sustained growth. By embracing GenAI and the advantages it can lend, organizations can navigate the challenges of modern manufacturing, leveraging advanced technologies to stay ahead in an increasingly competitive landscape.

About the Author:
As Head of Analytics and AI, Shipra develops new data solutions for supply chain organizations undergoing digital transformation. She believes that “analytics are key to unlocking the insights business leaders need to quickly and confidently make well-informed, data-driven decisions.”

Subscribe to Industry Today