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Biggest Benefits of Machine Learning in Manufacturing

Biggest Benefits of Machine Learning in Manufacturing

Manufacturing may greatly benefit from artificial intelligence and machine learning, which can also assist to open up new business options. Today, manufacturers are interested in learning how machine learning may help them address their unique business issues, such as recognizing defective components early in the production process to reduce waste, tracking manufacturing defects back to particular phases in the production process, and more.

Manufacturing Examples of Machine Learning

ML may be used across the supply chain in the manufacturing industry to produce tangible commercial benefits like:

  1. Optimizing the manufacturing floor using ML to save costs and increase operational efficiency. Consider Google, which was able to utilize custom ML to regulate the air conditioning in its server farms and cut the amount of power used in its data centers by 40%. Even though Google has previously invested a significant amount of time manually optimizing its procedures, the improvement was nevertheless made. Google is not alone in using machine learning to improve internal operations; more than 80% of early adopters of ML report that it is assisting them in reducing expenses.
  2. By utilizing ML to create optimized maintenance plans based on how equipment is really utilized, it is possible to save maintenance costs and increase dependability. The same strategy may be used to provide consumers personalized maintenance schedules. A trucking business, for instance, was able to save millions of dollars annually on components by determining when each vehicle would require maintenance based on the actual routes and terrain it had traveled.
  3. Lowering inventory levels and waste by using ML to improve production plans and more precisely estimate demand. By utilizing ML to more precisely estimate consumer demand, a textile factory was able to switch to just-in-time production, which allowed it to lower stock levels by 30%. In general, a quarter of early ML users claim that technology has improved their comprehension of clients and prospects.
  4. Applying ML to find defective items will help to improve quality control on the production line. The Google Vision API is being used by baby food maker Kewpie to identify discolored potato cubes that, while entirely safe for newborns to ingest, may raise parental concerns. The "human" inspection process was replaced by the machine learning system because it was time-consuming, stressful, more expensive, and prone to errors for production line personnel.
  5. ML may be used to better understand how things are really utilized, how they function, and what goes wrong with them in order to build new products that are more effective. These observations may be shared with design teams, and ML can also be used to forecast how suggested design modifications would perform. A third of early ML users claim that ML has improved their R&D capacities.
  6. Robots: Robots, maybe the most well-known manufacturing partners, are also becoming more intelligent thanks to machine learning. Robots that employ artificial intelligence are able to do everyday jobs that are difficult or hazardous for people. Because of their ML capabilities, these new robots are more advanced than the manufacturing lines to which they were previously consigned. That is the exact goal that Universal Robots is pursuing with its line of industrial robots. It has developed robots that can collaborate with people and work alongside them. And in that regard, the business is integrating its robots. High-performance sensors on an intelligent robot enable it to carry out challenging jobs alongside people while learning how to increase their productivity.

Machine Learning's Advantages in Production Context

Manufacturers stand to benefit a lot from implementing machine learning in their manufacturing processes because to the various application cases. The following are some of the most noteworthy advantages of machine learning:

  1. Maintenance planning: By anticipating equipment breakdowns before they happen, planning maintenance in a timely manner, and minimizing unneeded downtime, machine learning allows predictive maintenance. Too much effort is spent by manufacturers repairing malfunctions rather than committing money for scheduled maintenance. Machine learning algorithms have a 92% accuracy rate in predicting equipment failure, enabling organizations to better plan their maintenance plans and boost asset dependability and product quality. According to studies, the overall equipment efficiency rose from the industry average of 65% to 85% by implementing machine learning and predictive analytics.
  2. Better supply chain management: Manufacturers can guarantee that the production has enough materials at all times by using machine learning in the right way. As a result of orders being completed and delivered on time, customer happiness increases.
  3. Improved quality assurance: As was already said, manufacturers use machine learning for predictive maintenance and quality. By doing so, it is possible to keep the manufacturing line's machinery in top condition and lower the possibility of faulty goods leaving the line.
  4. Lower manufacturing costs: In the aforementioned application situations, using machine learning reduces production costs. As a consequence, the company pays less on space use, electricity costs, and maintaining faulty equipment.
  5. Quality assurance: Additionally, quality assurance and product inspection are carried out using machine learning models. Automating the inspection and monitoring process, ML-based computer vision systems may learn from past data to differentiate between quality items and defective ones. These algorithms don't need a library of potential flaws because they simply need excellent examples in their training set. On the other hand, an algorithm that compares samples to the most prevalent kind of flaws may be created. Visual quality monitoring in production may be significantly less expensive thanks to machine learning. According to Forbes, automated quality testing based on machine learning can boost detection rates by as much as 90%.
  6. Improved worker safety: By using malfunctioning machinery, personnel won't be harmed or placed in danger thanks to predictive maintenance. Additionally, the architecture and use of space optimally guarantee that workers never need to venture outside of protected areas.
  7. Better product design: By taking into account consumer wants, machine learning enables businesses to create in-demand items. Businesses might provide items that are ready for the market if they are confident that they will address a specific market pain point.

Manufacturing has the most potential to increase its cash flow among all industries, according to McKinsey, because of its dependence on data more than any other sector. A typical ML project would provide a ROI of between 2x and 5x in the first year, according to research by Deloitte. It makes sense that 60% of organizations are implementing ML strategies and that a quarter of early adopters are allocating more than 15% of their budget to ML initiatives, according to a different MIT Technology Review research. Contact a specialist today about incorporating critical machine learning initiatives into your manufacturing agenda.




Sources: https://www.itconvergence.com/blog/6-benefits-of-machine-learning-in-manufacturing/; https://www.ancoris.com/blog/5-ways-machine-learning-manufacturing; https://tulip.co/blog/machine-learning-in-manufacturing-an-introduction-to-industrial-ai/

Jan 26th 2023

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