According to McKinsey, 50% of companies that embrace AI over the next five to seven years have the potential to double their cash flow with manufacturing leading all industries due to its heavy reliance on data.
Artificial Intelligence and Machine Learning in manufacturing can result in a significant increase in production and supply chain efficiencies, and help in creating new business opportunities. Today, manufacturers want to know how machine learning can help solve their specific business problems, such as tracing manufacturing defects back to specific steps in the production process, reducing waste by identifying faulty components earlier in the process, etc.
Benefits of Machine Learning in Manufacturing
1. Predictive Maintenance
Machine learning enables predictive maintenance by predicting production line equipment failures before they occur, scheduling timely maintenance, and reducing unnecessary production downtime. Manufacturers spend far too much time fixing breakdowns instead of allocating resources for planned equipment maintenance. Machine learning algorithms can predict production equipment failure with an accuracy of 92%, allowing businesses to plan their maintenance schedules more effectively, improving asset reliability and product quality. Studies show that by deploying machine learning and predictive analytics, overall equipment efficiency increased from an industry average of 65% to 85%.
2. Quality Control
Machine learning models are also being used for product inspection and quality control. ML-based computer vision algorithms can learn from historical data to distinguish good products from faulty ones, automating the inspection and supervision process. These algorithms only require good samples in their training set, making a library of possible defects unnecessary. On the other hand, an algorithm can be developed that compares samples to the most common type of production equipment defects. Machine learning offers significant savings in visual quality control in manufacturing. According to Forbes, machine learning-based automated quality testing can increase detection rates by up to 90%.
3. Logistics and Inventory Management
The manufacturing industry requires extensive logistics capabilities to run the entire production process. Machine learning-based solutions can automate several logistics-related tasks, boosting efficiencies and reducing costs. It is estimated that the average US business loses $171,340 each year due to manual, time-consuming tasks such as logistics, supply chain and production-related paperwork. These routine tasks can be automated using machine learning and save thousands of man-hours annually. Machine learning algorithms can also be used to streamline supply chain and resource management – for example, Google was able to reduce its data center cooling bill by 40% by using DeepMind AI.
4. Product Development
Product development is one of the most common use cases of machine learning. Both designing new products or improving existing products require extensive data analysis to deliver the best results. Machine learning solutions can help in collecting and analyzing a large amount of product data to understand consumer demand, uncover hidden flaws, and identify new business opportunities. This can help improve existing product designs as well as develop better products that can create new revenue streams for the business. Companies can reduce the risks associated with the development of new products, as they can make more informed decisions with better insights.
5. Cybersecurity
Machine learning solutions rely on networks, data, and technology platforms – both on-premise and in the cloud to function effectively. The security of these systems and data is critical and machine learning can play a significant role by regulating access to valuable digital platforms and information. Machine learning can streamline how individual users access sensitive data, which applications they use, and how they connect to it. This can help companies protect their digital assets by detecting anomalies quickly and instantly triggering corrective action.
6. Robotics
Modern manufacturing is still heavily reliant on a human workforce. But automation in manufacturing is rising as robots are now able to perform many complex supply chain tasks, except for a few areas which require very high precision which only human specialists can provide. A major part of manufacturing could be taken over by robots in the future that are flexible enough to work together with humans. They will be able to operate in diverse and dynamic environments with minimal human supervision. Robotics provides a great opportunity for advanced machine learning techniques, helping manufacturers develop complex strategies and supply chain processes.
According to the 2019 Gartner CIO Survey, Artificial Intelligence, and Machine Learning continue to be viewed as the #1 game-changing technology by CIOs. Download this infographic to learn how manufacturing business leaders are effectively employing machine learning to align with various use cases.