Data strategy Four applications of intelligent machines in manufacturing

Related Vendors

Snowflake Computing GmbH

FAULHABER Drive Systems

Taiwan Machine Tool & Accessory Builders' Association (TMBA)

Wuxi InfiMotion Technology Co., Ltd.

Undoubtedly, we are currently enriched by numerous AI moments—from modern chatbots to gadgets like Apple's Vision Pro. AI innovations are particularly gaining momentum in the industry. Four promising applications stand out.

The days are numbered when data still had to be carefully reviewed and processed by real humans. Technologies such as generative AI and large language models (LLMs for short) make it easier to generate and automate data. This provides the manufacturing world with new tools to add to their tech stack. Although this movement unleashes numerous possibilities, industries themselves are responsible for figuring out how to use these new solutions for their current challenges.

The focus is clearly on the "how," and to answer this question, one must start with the data. Despite its great potential, AI is only as good as the data it is based on. For manufacturers who are already advanced in their data practice and pursue a robust data strategy, it should not be a problem to dive right in. However, manufacturers who are only now becoming aware of their data have a lot of catching up to do. To implement AI, they must understand their data and learn how to organize it. Once this step is taken, four particular applications for using AI open up for manufacturers.

1. Error reduction and cost optimization: Generative AI changes the game

What makes generative AI and large language models (LLMs) particularly valuable is that they allow for easy discussion of data that was previously difficult to comprehend or analyze, using AI tools in dialogue. This fundamentally changes how data is handled. Moreover, generative AI simplifies analytical workflows, enabling manufacturers to detect errors in the production chain early and even optimize the entire production process. Thus, it becomes easier than ever to identify and improve issues in manufacturing. With the help of iterations, engineers can examine data, test hypotheses, and utilize machine learning and simulation functions, all managed entirely by generative AI.

It has never been easier to digitally identify opportunities and accelerate the continuous improvement process. This leads to fewer errors, shorter cycle times, and overall lower production costs.

2. Real-time troubleshooting: AI assistance for disruption-free production lines

Another area that can be optimized through the use of AI is equipment maintenance. Even today, data helps manufacturers to more accurately calculate equipment maintenance in their production facilities. At the same time, this data enables predictive maintenance with the aim of maximizing the availability of production equipment. Nevertheless, there will always be cases where unforeseen disruptions occur, which production staff must respond to promptly.

With AI that can identify the interconnections of data streams, manufacturers have the opportunity to quickly resolve unexpected issues on the production line. For example, they can use AI to detect early when anomalies like excessive temperature or motor defects occur in machines. AI can immediately identify possible causes by examining real-time data and even automatically provide recommendations for action based on this data to resolve the issue. In this way, downtimes can be minimized, and new standards for production efficiency can be set.

3. Break data silos: AI-powered insights for more resilient supply chains

Looking at supply chains reveals that unplanned disruptions occur repeatedly. The challenge often lies in designing secure supply chains and being able to respond to interruptions as quickly as possible. The reality for most, however, is that due to industry consolidation, they often operate with many ERP systems. This makes it difficult to gain a clear overview of the entire supply chain network.

In the manufacturing industry, employees still predominantly work in silos, causing data to be isolated. This means that information about inventory and transport capacities could be in separate departments, slowing down processes and even leading to financial losses. By breaking down these data silos, supply chain managers can use LLMs to gain insights from the consolidated data. This combination of AI and consolidated data elevates companies to an advanced level: a level where they gain the necessary transparency to improve their planning process, as well as forecasts and route optimizations. In the long term, the implementation of AI within the supply chain can lead to higher profitability and improved customer satisfaction.

4. The art of error simulation: Generative AI in the production process

With generative AI, manufacturers have the opportunity to simulate defects in order to detect similar errors early on in the future. Suppose an automotive manufacturer creates a 360-degree image of a vehicle after the painting process. Generative AI can then be used to overlay various paint defects on the vehicle, for example. With these images, the manufacturer can train a separate deep learning model that is taught to recognize such defects. This is one way manufacturers can more easily develop and implement modern defect detection and classification algorithms.

Outlook on the future of work with AI

There is little doubt that AI will be an important component for all types of companies and their ways of working, especially for manufacturers. After the digitization and networking of industrial processes and systems began in the mid-2010s, the discoveries and experiments with AI in 2023 have added a new level to the fourth industrial revolution. AI offers the manufacturing industry numerous opportunities to organize the data generated by networking—and, above all, to use it for smarter and more efficient processes.

*Tim Long is Global Head of Manufacturing at Snowflake.