Data Strategy
AI Takeover: Four Application Possibilities for Intelligent Machines in Manufacturing
A guest post by Tim Long* | Translated by AI
4 min read
Associated Supplier
There’s no doubt that we’re enriched by many AI innovations right now, from modern chatbots to gadgets like Apple’s Vision Pro. In the industrial sector in particular, AI innovations are gaining momentum. Four promising areas of application particularly stand out.
Tim Long is Global Head of Manufacturing at Snowflake.
The days when data had to be carefully checked and processed by real humans are over. Because technologies like generative technologies AI Large language models (LLMs) make it easier to generate and automate data. This gives the manufacturing world new tools to add to its technology stack. While this movement opens up many possibilities, it is up to the industries themselves to determine how they can use these new solutions to address their current challenges.
So the focus is clearly on the “how,” and to answer that question, we need to start with data. That’s because, despite its great potential, AI is only as effective as the data it relies on. Manufacturers who are more advanced in their data practices and have a solid data strategy should have no problem getting started. However, manufacturers who are just starting to become data-aware have a lot of catching up to do. To implement AI, they need to understand their data and learn how to organize it. Once that’s done, there are four use cases for AI available to manufacturers.
Reducing errors and optimizing costs: generative AI is changing the rules of the game
What makes generative AI and large language models (LLM) particularly valuable is that they make it easy to discuss data that was previously difficult to monitor or analyze in a dialogue with AI tools. This fundamentally changes data processing. In addition, 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. Through 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 easier to digitally identify opportunities and accelerate the continuous improvement process. This results in fewer errors, shorter cycle times and overall lower production costs.
Real-time problem solving: AI support for seamless production lines
The use of AI can also optimize device maintenance. Even today, data helps manufacturers to calculate device maintenance in their production facilities more accurately. At the same time, this data enables predictive maintenance with the aim of maximizing the availability of production facilities. However, there will always be cases where unforeseen disruptions occur, to which production personnel will have to react quickly.
With the help of AI, which can identify relationships between data streams, manufacturers have the ability to quickly resolve unexpected issues on the production line. For example, using AI, they can detect anomalies such as high temperature or motor fault in machines at an early stage. AI can instantly identify possible causes by examining real-time data and even automatically offers recommendations based on it on how to resolve the problem. In this way, downtime can be minimized and new benchmarks in production efficiency can be established.
Breaking Down Data Silos: AI-Driven Insights for More Resilient Supply Chains
The supply chain perspective shows that there are often unforeseen disruptions. The challenge is often to design secure supply chains and to be able to react as quickly as possible to disruptions. The reality for most is that due to the consolidation of the industry, they often have many ERP systems. This makes it difficult to have a clear overview of the entire supply chain network.
In the manufacturing industry, employees still work largely in silos, isolating data. For example, information on inventory and transportation capacities can reside in separate departments, which can slow down processes and even lead to financial losses. If these data silos are broken, supply chain managers can gain insights from consolidated data using LLMs. Thus, the combination of AI and consolidated data takes companies to an advanced level: a level at which they gain the transparency needed to improve their planning process as well as route forecasting and optimization. In the long run, implementing AI within the supply chain can lead to higher profitability and better customer satisfaction.
The Art of Error Simulation: Generative AI in the Production Process
Using generative AI, manufacturers have the ability to simulate defects to identify similar errors earlier in the future. Suppose a car manufacturer creates a 360-degree image of a vehicle after the painting process. Generative AI can then be used, for example, to superimpose different paint defects on the vehicle. From these images, the builder can train a separate deep learning model, which is trained to recognize these types of errors. This is a twist on how manufacturers can more easily develop and use modern error detection and classification algorithms.
Future prospects for working with AI
There is no doubt that AI will become a crucial element for all types of companies and their operating methods, especially for manufacturers. After the digitalization and networking of industrial processes and systems that began in the mid-2010s, AI-related discoveries and experiments will mark a new milestone in the fourth industrial revolution in 2023. Indeed, AI offers the manufacturing industry many opportunities to organize data resulting from networking and, above all, to use it for smarter and more efficient processes.
