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Why Manufacturing Companies Should Not Miss Out on Implementing AI and ML Tools

HomeTechnologyWhy Manufacturing Companies Should Not Miss Out on Implementing AI and ML Tools

If you look at the future scenario of Machine Learning (ML) and Artificial Intelligence (AI), you will see that more and more companies are going to use them in the coming years.

If you look at the research about future scenarios, you will find that companies that use AI over the next five to seven years can double their revenues. Again, the manufacturing industry will benefit the most from AI and ML applications.

Given the research on AI and ML, manufacturing can see a significant increase in efficiencies. In addition, AI and ML can provide new business opportunities for manufacturing businesses.

Machine learning for manufacturing companies can help to figure out how to solve their specific business problems, such as monitoring manufacturing defects, implementing specific steps in the production process, reducing waste by identifying faulty components earlier in the process, etc.

Now, let us examine how AI and ML can help manufacturing companies flourish:

Predictive Maintenance

ML algorithms can help manufacturers to know in advance when to repair their equipment by predictive maintenance.

So the company can schedule timely maintenance and reduce unnecessary downtime. In the absence of predictive maintenance, manufacturers need to spend much time fixing breakdowns.

But, predictive maintenance helps companies to spend time productively and allocate essential resources for maintenance.

Given the current trends, ML algorithms can predict machine failure with an accuracy of up to 92%. As a result, manufacturing companies can plan their maintenance schedules in advance. And that can improve asset reliability and product quality.

Again, manufacturing companies can increase their equipment efficiency from 65% to 85% using ML and AL.

Quality Control

Accurate quality control is another important advantage of AI and ML applications, which helps in product inspection and quality control.

With ML-based computer vision algorithms, manufacturers can distinguish good products from faulty ones from historical data.

In addition, the manufacturers can automate the inspection and supervision process. The algorithms have good samples in their training set to elicit dependable results.

Moreover, the algorithms allow the comparison of samples prone to the most common type of defects. Also, ML algorithms can result in significant savings in visual quality control for the manufacturing industry.

According to the information available in the public domain, ML-based automated quality testing can raise detection rates by up to 90%.

Logistics and Inventory Management

The effectiveness of the manufacturing industry depends on extensive logistics capabilities to run their production processes. As ML and AI tools can automate several logistics-related tasks, they can increase efficiencies while reducing costs.

Every year, manufacturing businesses lose significant money due to manual, time-consuming logistics and production-related paperwork.

But, ML and AI tools can help manufacturing businesses do away with such routine tasks by automating them. As a result, manufacturing companies can save thousands of man-hours by using the tools.

In addition, ML tools can streamline resource management. For example, Google reduced the data center cooling bill by 40% by using DeepMind AI.

Product Development

With ML tools, manufacturing businesses can develop products effectively. And it is a common use case of ML algorithms.

It requires extensive data analysis in order to design new products or improve existing products. And data analysis can help manufacturers to yield the best results.

With ML tools, manufacturing companies can effectively collect and analyze large volumes of product data to figure out consumer demand, discover hidden flaws, and identify new business opportunities.

Therefore, manufacturers can improve their existing product designs and, at the same time, can develop better products. As a result, businesses can create new revenue streams.

Furthermore, manufacturing businesses can reduce the risks associated with the development of new products because they can make more informed decisions with ML-delivered insights.

Conclusion

With AI and ML tools, manufacturing businesses can take their businesses to the next level. The tools can help businesses to increase efficiency and productivity at all levels and raise their revenue streams.

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