Artificial intelligence is involved in various current industrial applications; a technological transition is known as Industry 4.0. This highlights the present trends in the automation sector, and machine learning (ML) is one of the foundations of this digital revolution. This blog describes how companies may profit from the great advantages of machine learning in manufacturing.
The use of machine learning is higher (79%) in the manufacturing and high tech sector specifically, as is the institutionalization of enterprise knowledge using AI (66%) and cognitive AI-led processes/tasks (60%). Most companies want to automate manufacturing to increase productivity (66%), minimize manual errors (61%), reduce costs (59%) and refocus people’s efforts on non-repetitive tasks that benefit from human intervention (50%). –Infosys
Table of Contents:
What is Machine Learning in Manufacturing?
Machine learning software development in manufacturing leverage data extracted from various machines and sensors and real-time data, offline data and data from historians or MES and ERP systems at different stages of the production process.
Machine learning is usually classified as supervised, unsupervised or semi-supervised and enhanced earning. The two models commonly used within manufacturing are:
Supervised machine learning
Can be trained to find patterns in data using predefined criteria. Usually, this is done with one of two models:
- Regression model – This analyses historical data sets to anticipate things such as how long a component of the machine will survive based on previous experience. This is known as the Remaining Useful Lifespan or RUL.
- Classification model – For instance, this model type can forecast a machine or component failure probability in a given time frame.
Unsupervised machine learning
It provides its patterns from data sets without predetermined results and cannot be trained like supervised learning. Common applications include:
- Clustering – Creates various data points clusters related to pattern identification by specific properties.
- Anomaly detection – May identify odd patterns in datasets—such as fraudulent behaviour or defective components in manufacturing.
- Association mining – Usually used for retail purposes to identify sets of items that commonly occur together in a basket.
- Latent variable models – Used in general to decrease the number of points in a dataset in pre-processing.
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.
Machine Learning Empowering Smart Manufacturing
Considering numerous benefits offered by ML, manufacturers are enhancing their business with machine learning to gain incredible profits. The list below shares the top five ways ML can shape the manufacturing industry.
- Machine maintenance
Machine learning offers maintenance of machines, often known as preventive maintenance, helping manufacturers to understand systems failure or system malfunctions. This makes it possible for manufacturers to identify which gadget can break in the future. It also allows producers to avoid or prevent failures of machinery.
- Business productivity
Machine learning techniques can learn and adapt to new environments over time. Moreover, it can also help machines learn from their mistakes. With the collaboration of ML algorithms and tools, machines are automated, which allows manufacturers to enhance their business capacity. The devices then offer better decisions than humans, which helps manufacturers gain tremendous advancements in their productivity.
- Product quality
Machine learning allows manufacturers to analyze if all initial process objectives for their products and services are fulfilled. Machine learning algorithms may be used to identify which product has more effect. Machine learning minimizes errors and losses and removes unnecessary human effort, which improves overall product and service quality.
- Better insights
For the manufacturing industry, the collection of appropriate data is a major concern. Machine learning helps to collect the appropriate information and to improve sales. The insights generated by machine learning techniques enable manufacturers and clients to cooperate and optimize the supply chain.
- Customer relationship
Predictive analysis may be used to analyze the behaviour of customers towards or in their interest in a specific brand. Manufacturers can utilize this client information and provide them discounts accordingly. It also helps manufacturers to locate the appropriate clients who want to purchase their products. Manufacturers may use this information to improve their businesses and, if required, make changes based on customer requirements.
AI and ML are the strengths of Industry 4.0. These technologies constantly improve the quality of production and collect data from products and equipment in the field. This data serves as vital information that forms the foundation for product development and essential commercial decisions. – Gartner
Summing-up:
The Digital revolution is essentially revamping a lot of industries. It is set to enhance the processes involved and can help implement better strategies for machine learning development. You will improve the processability, reduce downtime, and hire better resources with machine learning capabilities in your business.
To get the most out of an Industrial machine learning solution; manufacturers need to know which solution best suits their own unique sets of challenges.
Ready to revolutionize the manufacturing business with ML? Dash Technologies can help you realize your vision. Get in touch with us to learn more!
Learn the simple yet effective methodology for picking the right technology for healthcare; read our blog 5 Ways Machine Learning Is Revolutionizing the Healthcare Industry.
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