When manufacturing defects or nonconformities, stopping production machines can cause huge losses. Especially for those businesses that don't have the tools and resources to develop quality products, this is a very complicated process for them. In the current era, artificial intelligence and machine learning have become more common in the production and assembly of items, which reduces costs and production times. According to Mckinsey's forecast, 40% of the potential value created by analytics today comes from AI and Machine Learning techniques, of which Machine Learning accounts for between 3.5 and 5.8 trillion VND in value. annual value.
Top growth strategies involve integrating machine learning platforms that generate insights, to improve product quality as well as manufacturing productivity. Machine Learning - where robots can place their items along with granular precision, analytics can identify upcoming scenarios and automated processes can develop error-free outputs.
Businesses need to take advantage of smarter solutions to make the whole process more efficient and then scaleable as the amount of data is increasing day by day. Helped businesses a lot in automating and even predicting and monitoring machine performance.
The big digital transformation for manufacturers is algorithmic evolution through machine learning and artificial intelligence-powered products.
Get data from products that are then fed into a Machine Learning model to further improve the manufacturing process. In the future industrial activities will be transformed by robots. Machine Learning is a key element of advanced predictive maintenance by identifying, monitoring, and analyzing critical system variables in the manufacturing process. Through this, operators can be warned before the system fails, and in some cases without the interaction of managers.
When thinking about solutions based on artificial intelligence and machine learning as how they can serve day-to-day processes throughout the entire manufacturing process. With this technology, manufacturers can detect problems with their conventional production methods.
With a combination of Machine Learning tools with Internet vạn vật, bring value not only to the manufacturing process, but also to product packaging and distribution (logistics, inventory, assets, supply chain management).
The most widely adopted application of Machine learning involves the product development phase, the design and planning stages of new products, as well as the improvement of existing products, associated with a wide range of products. information must be reviewed to yield the best results.
With a Machine Learning solution, businesses collect consumer data and analyze it to understand needs and uncover hidden needs, thereby uncovering new business opportunities.
Applying a good Machine Learning solution can improve the final product quality up to 35%. Especially for specific manufacturing industries. To apply this solution well, businesses need to find anomalies in their products and packaging. Thanks to that, businesses prevent defective products from reaching the market.
Through IoT devices and ML, businesses will analyze the availability and performance of all devices used in the production process. Improve production quality, predictive maintenance, estimate the best time to engage specific equipment to prolong life and avoid downtime.
Determining when to perform equipment maintenance is a difficult task for businesses, every time a machine is brought in for maintenance, it does not work or even requires machine downtime until when repairing. Regular repair will cost the business, just as infrequent maintenance will lead to even more costly breakdowns.
Machine Learning algorithms are great at balancing multiple data languages for prediction, determining optimal repair times. Identify errors when they occur to find solutions in time.
Besides, thanks to this algorithm to identify old data to know damaged equipment, helping businesses when to maintain. In particular, data can also be obtained automatically from within the enterprise thereby eliminating the need for manual checks. Increase speed and efficiency, reduce manpower costs.
Predictive maintenance accounts for a significant portion of all manufacturing operations costs. Give your business a lot of value, so predictive maintenance becomes a common goal for every business.
In Industrial AI, Machine Learning algorithms detect anomalies and check for correlations while searching for patterns of different feeds, revealing the power of real-time analysis of data from them. suggest actionable responses to problems that arise.
The application of artificial intelligence to robots allows them to take on complex, dangerous everyday tasks. They break through chains where they were previously brought down, because their Machine Learning capabilities allow solving more complex processes.
Hopefully through this article, New Ocean will provide you with the most useful information to help you better understand the application of artificial intelligence in the factory, thereby applying it to your business in the most effective way. Contact us if you have any questions? Our solution experts are ready to answer all your questions and help you choose the right solution.
