Machine Learning can help increase yields while reducing operational costs, improve equipment monitoring and contribute to better anomaly reporting. This blog takes a look at these and other examples of how to improve the quality of your manufacturing operations with the help of technology.
Preventing equipment failure with pinpoint accurate predictions
Even though downtime is an issue every manufacturer has to face once in a while, reducing it by a meaningful percentage can significantly increase efficiency. Combining Machine Learning with advanced analytics creates a powerful tool that lets you know if a component is about to break down and cause your operations to stop. Predictive maintenance enables manufacturers to save huge amounts of resources by reducing downtime to a minimum.
Machine learning enables a highly proactive approach to problem-solving which is based on predicting machine processes that slow down or fail well ahead of time. Quality assurance becomes much easier once you have an autonomous, self-improving network able to track various manufacturing processes and let you know when it’s time to act.
Even though AI can’t match the cognitive ability of humans and help with the fine-tuning of certain quality assurance tasks, it can dramatically impact the speed at which this process is performed and continuously improve manufacturing systems through sheer repetition and advanced data analytics. You can read more about QA in manufacturing processes here.
Improving yields and efficiency
Given the fact Machine Learning models can carry out root cause analysis (RCA) to get to the middle of various issues and solve them, manufacturers can expect greater operational efficiency and reduced scrap rates. Tasks such as testing, which used to require the attention of entire companies, are now becoming increasingly automated, saving time and resources along the way. Machine Learning enables continuous improvement of every business process in your organization.
There are times when striking that perfect balance between your demand and supply seems like an impossible task – especially in the earlier stages of a company’s existence. Machine Learning could be the answer to future supply chain challenges.
The solution lies in collecting large amounts of data and using advanced analytics and AI to process and turn it into highly accurate predictive analysis and demand forecasting tools. Machine Learning makes demand forecasting immensely better as time goes on – the larger the amount of data a system can work with, the better.
Monitoring, decreasing and ultimately eliminating the negative effects manufacturing processes can have on the environment is becoming a priority for the entire world. With Machine Learning behind them, intelligent manufacturing systems can autonomously fine-tune production in a way that creates much less emissions and waste, while retaining a high level of efficiency.
To find out more about how you can leverage Machine Learning to boost your organization’s performance, visit this page.