5 Powerful Use Cases of AI in Manufacturing

The proliferation of digital technologies, IoT devices, and advanced tracking systems have compounded the problem. This wealth of data has given rise to greater silos of data within the organization which in turn has led to disconnected data sets. Critically, the fragmentation of data impedes the creation of a holistic view of the organization’s supply chain.

Despite all of the recent hype around generative AI, Micropsi needs to do more to promote its unique use of vision AI for manufacturing, he said. Micropsi also plans to share more of the data captured by cameras, said Jackson. It is exploring how to feed that data to command-and-control systems and the dashboards that factory personnel look at daily for key performance indicators (KPIs). Jackson has more than 30 years of executive experience and was previously CEO of video analytics provider Drishti, which was recently acquired. He has led software companies such as Vantive, Ounce Labs, Shunra, and Zeekit, said Micropsi. We’ll look at a few of the use cases of AI development services for manufacturers in our blog today.

Inventory management

For example, machine learning can automate spreadsheet processes, visualizing the data on an analytics screen where it’s refreshed daily, and you can look at it any time. A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended. A. The market for artificial intelligence in manufacturing was pegged at $2.3 billion in 2022 and is anticipated to reach $16.3 billion by 2027, expanding at a CAGR of 47.9% over this period. This data depicts the promising future of AI in manufacturing and how it is the right time for businesses to invest in the technology to gain significant business results. The development of new products in the manufacturing industry has witnessed a significant transformation with the advent of AI.

Now, it is executed seamlessly in a single step, thanks to robotic assistance. GE Appliances helps consumers create personalized recipes from the food in their kitchen with gen AI to enhance and personalize consumer experiences. GE Appliances’ SmartHQ consumer app will use Google Cloud’s gen AI platform, Vertex AI, to offer users the ability to generate custom recipes based on the food in their kitchen with its new feature called Flavorly™ AI.

Top 10 Use Cases of AI in Manufacturing

Generative design is a way to explore ideas that could not be explored in any different way – just think about how much time it would take a real person to come up with a hundred different ways to design a chair. Artificial intelligence can do it in no time, letting the human expert choose from a wide range of options. Digital transformation like that can change the way a company delivers value to the customers and improve efficiency of processes.

This not only reduces the time taken for customers to find the right products but also improves the overall customer experience by making it more personalized and convenient. AI significantly contributes to enhancing product visibility and searchability by generating high-quality product data. This data is derived from various sources such as customer feedback, online reviews, market trends, and real-time sales data. AI algorithms analyze this data to produce structured and accurate product information, facilitating efficient product searches. By embedding AI capabilities into factory machines and equipment, manufacturers can benefit from automation, which allows them to optimize the overall production process.

In terms of deployment, leaders were doing 18 different use cases where the emerging companies were six on average. The last one is data, specifically the democratization of data, where leaders normally put much more effort into making sure that data was accurate. Ninety-two percent had processes to make sure that the data was available and accurate.

The involvement of robots in high-risk jobs can help manufacturers reduce unwanted accidents. Digital twins allow manufacturers to gain a clear view of the materials used and provide the opportunity to automate the replenishment process. Implementing AI in manufacturing facilities is getting popular among manufacturers. According to Capgemini’s research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third. For example, an automotive manufacturer can use RPA bots to process supplier invoices. The bots can extract relevant details, validate them against predefined rules, and enter the data into the accounting system, eliminating the need for manual data entry.

AI Adoption Trends in Manufacturing

Most supply chain tasks can be fully or partly automated through low-code platforms, which use a wide range of Application Programming Interfaces (APIs) and pre-packaged integrations to link previously separate systems. These cut the development time, enabling companies to swiftly react and adapt their applications to new market conditions, disruptive events, or changing strategies. It enables business users with little technical knowledge to quickly build, test and implement new capabilities. These structural trends will shape new operating models and improve broad processes. To avoid being left behind, it is important for organizations to understand these trends and apply specific actions to begin their transformation sooner rather than later.

To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance. Let’s look at how AI is assisting organizations in the manufacturing industry to achieve their business goals.

Improving Quality and Throughput

They prioritize AI use cases in manufacturing that offer clear business benefits, practical feasibility, and swift value realization. By harnessing the power of AI and ML in manufacturing, companies are transforming their supply chain strategies, for enhanced efficiency, precision, and cost-effectiveness. Watch this video to see how gen AI improves customer service for an automotive manufacturer, delivering real-time support to the vehicle owner who sees an unexpected warning light.

• Due to its additive processes, the automotive industry is currently the biggest benefactor of generative AI for sustainability.
• These experts rely on their knowledge and experience to manually adjust the equipment or material and troubleshoot unexpected issues.
• Organizations will need to intensely focus on mining relevant, clean and well-governed data if they want to make the most of their new technology investments.
• He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years.
• With the use of sensors, cameras, and other data collection techniques, the representation replicates the physical properties of its real-world counterpart.
• Generative AI is used to improve product design, engineering, production, and operations in various industries.

One thing to observe is the focus on generative AI and how it will affect various industries. An important question to ask here is whether it already has a huge impact on manufacturing or if actual use cases are yet to be discovered. Similar to retail, AI plays a major role in product personalization for manufacturing. Customers want customized products, and manufacturers have to keep up if they’re going to survive. Factory operators play a major role in the smooth running of the factory – no matter how advanced the system is. These experts rely on their knowledge and experience to manually adjust the equipment or material and troubleshoot unexpected issues.

Read more about Cases of AI in the Manufacturing Industry here.