6 Ways AI Changes Manufacturing Industry

1. Predictive maintenance

Before the emergence of AI, machine maintenance was strictly arranged to minimize the risk of unexpected failures. Now, companies can use predictive AI systems to customize the maintenance requirements of each device, create optimized schedules for individual machines, and improve efficiency without increasing costs.

For example, milling facilities often have the problem of spindle breakage, which slows down production speed and increases operating costs. However, by integrating AI programs into software, these factories can maintain up-to-date monitoring to detect potential failure points before they cause problems.

2. Quality assurance

Using AI to enhance quality assurance practices can not only produce better final results, but also help businesses determine the best operating conditions in the workshop and determine which variables are most important for achieving these goals. This reduces the defect rate and greatly reduces the waste generated, thereby saving time and money.

McKinsey pointed out that the most expensive aspect of the semiconductor industry is manufacturing, as long multi-step production cycles may take weeks or months. Most of the time costs are attributed to the QA testing that must be carried out at each step and the delays caused by defects. AI not only simplifies these QA steps, but also improves overall efficiency and yield loss by aggregating data from all production stages.

3. Defect inspection

Now it is possible to 'outsource' the work of finding defects, thanks to AI being able to visually inspect items faster and more thoroughly than humans.

The correct system can be trained on relatively few images and then deployed to perform the same tasks that typically require dozens or hundreds of staff to complete. In addition, it can conduct Root cause analysis, so that the company can solve potential problems that may be ignored, thereby increasing output and optimizing production.

4. Warehouse automation

Consumers are shifting their purchasing habits towards e-commerce, which means warehouse efficiency is becoming a top priority for businesses that require excellent logistics to maintain competitiveness.

Warehouse automation covers everything from implementing AI solutions for processing invoices, product labels, and supplier documents, to utilizing algorithms to optimize shelf space, which can bring huge investment returns to warehouse operations.

5. Assembly line integration and optimization

To truly optimize production and reduce costs, it is not just necessary to collect data from the manufacturing workshop. Information must be scanned, cleaned, and constructed in a way that allows for functional analysis. AI can quickly and easily classify and structure the aggregated data of the entire facility, providing personnel with an actionable and practical overview of what happens at each stage of the production process.

This also allows a certain degree of automation of the Assembly line, such as restructuring the production line in the event of a machine failure.

6. AI Product Development and Design

With the continuous progress and improvement of technology, AI is expected to have the most significant impact on product development and design in the next five years. Manufacturers have used it for generative design to create innovative prototypes and accelerate networking