For businesses that don’t have the right resources in place to produce their goods, the manufacturing process can be time-consuming and costly. Recently, machine learning has become more predominant in the development and assembly of products, using sophisticated technology to reduce the length and cost of manufacturing.

Machine learning, in particular, is based upon large datasets, which can be used to provide insights into the manufacturing process.

Using machine learning in the manufacturing process helps build what is known as smart manufacturing, where robots bring products together with pinpoint accuracy, while the system fixes in real-time any mistakes to minimize spillage.

The use of machine learning algorithms, applications and platforms will definitely revolutionize business models by measuring the quality of its production process, while also improving different procedures.

How is machine learning changing the manufacturing industry?

  • Machine learning can improve semiconductor manufacturing yields by up to 30%, just by minimizing scrap rates and improving operations. Through optimizing development workflows, the system will use root-cause analysis and reduce test costs. More than that, manufacturing equipment that operates on machine learning is expected to be 10% cheaper in annual maintenance costs, while at the same time minimizing downtime by 20% and lowering inspection costs by 25%.
  • Using real-time tracking systems to create reliable data sets that capture pricing, inventory velocity, and related parameters give machine learning apps what they need to evaluate cost behaviors through several manufacturing scenarios.
  • Machine learning helps manufacturers overcome seemingly impenetrable problems and expose the ones they never knew existed, like unknown gaps or unprofitable chains of production.
  • Machine learning can measure the accessibility, efficiency, and quality of assembly systems. They can all be optimized with the introduction of deep-learning neural networks that easily learn and help reduce the flaws of these machines.
  • Machine learning has the ability to reduce the severe labor shortage in manufacturing and at the same time searching for new ways to attract workers.
  • Adoption of AI and machine learning is expected to dominate robotics in the next years, becoming the main use case in manufacturing. The dynamics and restrictions of supply chain processes are an excellent use case for machine learning algorithms to offer better solutions.
  • Machine learning also plays a significant role in increasing the value of a business by optimizing its logistical strategies such as asset management, supply chain management, and mechanisms for inventory management. A contemporary organization needs to effectively integrate AI & IoT in order to make sure the supply chain functions at the maximum level. Lately, those in the manufacturing industry have begun to look for new ways to mix innovative technologies such as ML, AI, and IoT with improving asset tracking accuracy, inventory optimization and the visibility of supply chain.
  • One of the most important objectives of artificial intelligence and machine learning is to facilitate the implementation of a digital twin of the production floor. The development of the digital twin would occur as an initiative under a model-based systems engineering process, using the machine learning algorithms ant the expertise acquired as a basis. The digital twin represents the concept that helps the manufacturers to explore what-if scenarios. It can also be used as an end-to-end interface in developing higher reliability elements and optimizing interactions between shop floor machines to enhance performance. The possibilities are endless. You can read more about the impact of the digital twin in the manufacturing industry in our article: How Digital Twin Technology is Transforming the Manufacturing Industry

To conclude

The progress in machine learning will allow businesses to quickly improve their manufacturing processes.

By understanding the advantages of machine learning, industrial professionals will continue to stay ahead of the competition that still needs to step away from outdated, old-fashioned practices.

From quality management to inventory management, supply chain strategies and lower spending, there are various ways in which machine learning can change the future of manufacturing.

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