AI Analytics for Manufacturing: Best Use Cases & Business Solutions

How Much AI Is Used in Manufacturing?

More and more manufacturers are adopting AI into their processes. Here’s what the numbers say:

• 41% of manufacturers already use AI-powered apps for supply chain analytics.

• 35% of manufacturing companies used AI in 2023, mainly for predictive maintenance and quality control.

• 27% of businesses say that AI has added value to their operations, and 56% expect major results within the next 2 to 5 years.

As you can see, the role of AI analytics in manufacturing is enormous. 

As Roman Hutnyk, СЕО of Bits Orchestra, puts it, “Our experience integrating AI into manufacturing operations has shown us that data-driven insights don't just optimize processes, they redefine what's possible. AI enables manufacturers to innovate faster, adapt more efficiently, and ultimately deliver more value to their customers. Collaboration between AI and human expertise drives the best results, combining intelligence with experience to achieve true operational excellence.”

What are the uses of AI analytics for manufacturing? The technology can be helpful throughout the entire production cycle — and beyond. Just look at the following steps:

Top 11 AI-Powered Use Cases in Manufacturing

Now, let’s see the specific applications of AI analytics in manufacturing. The most common — and powerful — ones are as follows:

1. Supply Chain Management

AI can help you optimize everything from inventory levels to logistics by analyzing market conditions, supplier performance, and material demand. Imagine your system anticipating material shortages before they actually impact your production line.

2. Cobots

Collaborative robots, or cobots, are being increasingly used in assembly lines. AI powers these robots and enables them to work safely alongside humans. With this kind of robotic process automation, you reduce repetitive or labor-intensive tasks and let your employees focus on more important jobs.

3. Demand Prediction

Forecasting future demand is something you wouldn’t want to skip in your manufacturing workflow. Using AI for this purpose lets you easily analyze past sales data, customer trends, market patterns, and even social media sentiment to get accurate demand predictions. The result? You avoid overproduction or shortages.

4. Automated Paperwork

Let’s be honest: no one likes paperwork. Hopefully, AI can take care of that. It automates invoice processing, order entry, compliance monitoring, and other administrative tasks.

Remember the project we mentioned earlier? The platform for a leading procurement company does just that — streamlines Accounts Payable, invoicing, and compliance with PEPPOL standards by using machine learning models.

“Our experience highlights that by automating paperwork with AI, a company has been able to cut down processing time by 40% and reduce compliance errors by 30%. To achieve this goal, companies must have good data quality and provide proper training to the employees.” remarked CTO of Bits Orchestra, Serhiy Sydorchuk.

5. Recruitment and Employee Retention

AI analytics significantly helps with talent acquisition. How? It screens candidates faster, matching the right talent to the right roles. It also studies worker behavior to predict turnover and recommends strategies for increasing employee productivity and retention.

6. Workplace Safety and Compliance

This use case is particularly important and usually involves the industrial Internet of Things — AI-powered cameras and sensors. These devices monitor work environments, identify potential hazards, and alert if something gets out of control.

7. Order Management

AI can be used to simplify the order fulfillment process. It makes it easier to track shipments, predict delivery times, and provide real-time updates to customers. All in all, it improves coordination between production and logistics teams.

8. Predictive Maintenance

We’ve talked about this one already, but it’s so important it’s worth mentioning again. Predictive analytics in manufacturing can be used to detect early signs of equipment failure — instead of responding to them when it’s too late.

9. Assembly Line Optimization

AI can help optimize operations on the shop floor. It analyzes data from various stages of the assembly line, identifying weak, inefficient spots. Then, it automatically adjusts workflows—or suggests adjustments to your employees—improving production speed and reducing defects.

10. New Product Development

Launching a brand-new product is challenging — yet AI makes the process faster and smarter. It helps analyze market trends, customer preferences, and competitor strategies, giving you a clearer picture of what to build. It can also generate early design concepts, predict material costs, and simulate performance before prototyping.

11. Quality Control

AI has its impact on quality control, turning it from a reactive process into a proactive one. Now, instead of random batch inspections, computer vision systems and sensors analyze every single product in real time. These solutions detect even the tiniest defects that human eyes might miss, minimizing deviations from quality standards.

In case the above use cases aren’t enough, you can also explore generative AI in manufacturing examples we’ve carefully gathered for you.

Have questions about the uses of AI in your manufacturing company?

Leading manufacturers are already using AI to optimize production, reduce downtime, and improve quality control. Get in touch with our specialists to discover how AI can transform your manufacturing processes.