Call it the repercussions of the COVID-19 pandemic or the opportunities that came along with it. Still, nothing denies the fact that digitization of business processes has created an all-new business environment in Industry 4.0.

Emerging technologies such as IoT, RPA, AI, and ML have been transforming the face of manufacturing with the rising advent of digital transformation.

At times, AI and ML are used interchangeably, as ML is a subset of AI and varies from each other.

Simply put, AI refers to a technology that utilizes a broader concept of using computers and machines to simulate human thinking. In contrast, machine learning is the application of AI that enables computers and machines to learn without being explicitly programmed by humans and deliver automated services.

Research, too, suggests that AI and ML play a vital role in transforming the manufacturing industry in unimaginable ways.

According to recently conducted research, as many as 50% of companies that embrace the applications of AI and ML over the next five years have the potential to double their revenues and cash flow due to their heavy reliance on data.

The below-discussed pointers talk about the significance ML holds for manufacturing industries and how it is changing the face of manufacturing.

1.Leveraging the Predictive Maintenance

Machine learning lets organizations drive predictive maintenance by predicting equipment failures before they even occur, scheduling timely maintenance, and reducing unnecessary downtime, thereby providing resilience to machines, equipment, and the manufacturing industry overall.

Manufacturers usually spend too much time fixing breakdowns and allocating resources for planned maintenance.

Machine learning algorithms help them predict equipment failure with an accuracy of 92%, allowing businesses to plan their maintenance schedules more effectively, and improving asset reliability and product quality by delivering overall equipment.

2.Logistics and Inventory Management

The manufacturing industry deals with logistics and inventory massively and requires extensive logistics capabilities to run the production process efficiently.

Machine learning-based solutions help organizations automate several logistics-related tasks, boost efficiencies, and reduce costs for manual, time-consuming tasks such as logistics and production-related paperwork, thereby providing companies with digital solutions.

3.Focus on Cybersecurity

Machine learning solutions utilize networks, data, and technology platforms and function effectively, and play a significant role by regulating access to valuable digital platforms and information.

Machine learning streamlines individual users and accesses sensitive data, which help companies protect their digital assets by detecting anomalies quickly and instantly, triggering corrective action.

Moreover, Machine Learning and AI add value across the entire value chain of manufacturing by delivering automated, streamlined, and resilient manufacturing processes, which hold massive potential not only in today’s time but in the future as well.

Conclusion

Like any other emerging technology, AI and ML transform manufacturing processes by delivering hassle-free automation to the entire manufacturing value chain.ML not only eases the supply chain but also helps organizations automate their logistics processes in digitally driven ways.