Data strategy AI Takeover: Four application possibilities for intelligent machines in manufacturing

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Undoubtedly, we are currently being enriched by numerous AI moments—from modern chatbots to gadgets like Apple's Vision Pro. Especially in the industrial sector, AI innovations are picking up speed. Four promising uses stand out in particular.

Tim Long is Global Head of Manufacturing at Snowflake.

The days when data had to be carefully checked and processed by real humans are numbered. Because technologies like generative AI and large language models (LLMs) make it easier to generate and automate data. This gives the manufacturing world new tools that it can add to its tech stack. Although this movement opens up numerous possibilities, industries themselves are responsible for figuring out how they can use these new solutions to address their current challenges.

The focus is therefore clearly on the "how", and to answer this question, one has to start with the data. Because despite its great potential, AI is only as good as the data it is based on. For manufacturers who are more advanced in their data practices and follow a robust data strategy, jumping right in should not be a problem. However, manufacturers who are only now becoming aware of their data have a lot of catching up to do. To implement AI, they need to understand their data and learn how to organize it. Once this step is taken, four use cases for using AI open up for manufacturers.

Error reduction and cost optimization: Generative AI changes the rules of the game

What makes generative AI and large language models (LLMs) particularly valuable is that they allow data that was previously difficult to oversaw or analyze to be easily discussed in dialogue with AI tools. This fundamentally changes the handling of data. Furthermore, generative AI also simplifies analytical workflows, allowing manufacturers to detect errors in the production chain at an early stage and even optimize the entire production process. This makes it easier than ever to identify and improve manufacturing problems. Using iterations, engineers can examine data, test hypotheses, and use machine learning and simulation functions that are fully managed with generative AI.

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

Real-time problem-solving: AI support for disruption-free production lines

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

With the help of AI, which can identify the relationships of the data streams, manufacturers have the ability to quickly fix unexpected problems in the production line. For example, by using AI, they can detect anomalies like high temperature or a motor defect in machines at an early stage. AI can instantly identify possible causes by examining real-time data and even automatically offers recommendations based on this on how to fix the problem. In this way, downtime can be minimized and new benchmarks for production efficiency can be set.

Breaking through data silos: AI-supported insights for more resilient supply chains

The view on the supply chains shows: there are often unplanned disruptions. The challenge here is often to design secure supply chains and to be able to respond as quickly as possible to interruptions. The reality for most is that due to industry consolidation, they often have many ERP systems. These make it difficult to get a clear overview of the entire supply chain network.

In the manufacturing industry, employees still predominantly work in silos, thus isolating data. For example, information about inventory and transport capacities could reside in separate departments, which can slow down processes and even lead to financial losses. If these data silos are broken down, supply chain managers can gain insights from the consolidated data with the help of LLMs. Thus, the combination of AI and consolidated data raises companies to an advanced level: a level at which they obtain the necessary transparency they need to improve their planning process as well as predictions and route optimizations. In the long term, the implementation of AI within the supply chain can lead to higher profitability and improved customer satisfaction.

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

With the assistance of generative AI, manufacturers have the opportunity to simulate defects in order to identify similar errors early on in the future. Suppose an automobile manufacturer creates a 360-degree image of a vehicle after the painting process. A generative AI can then be used, for example, to layer various paint defects over the vehicle. With these images, the manufacturer can train a separate deep-learning model that is educated to recognize errors like these. This is one variation of how manufacturers can more straightforwardly develop and employ modern error detection and classification algorithms.

Outlook on the future of working with AI

There's hardly any doubt that AI will be a crucial component for all types of businesses and their ways of operation, especially for manufacturers. After the digitalization and networking of industrial processes and systems were already initiated in the mid-2010s, the discoveries and experiments with AI in 2023 added a new level to the fourth industrial revolution. Because AI offers the manufacturing industry numerous possibilities to organize the data emerging from networking—and above all, to use it for smarter and more efficient processes.