Manufacturing and Inventory Management Software
We’re quickly learning the importance of machine learning as more and more businesses start looking to AI for help with their processes. However, because machine learning is such a new concept to many — and some assume it’s simply a part of artificial intelligence — it can be tough to figure out how to use it. So, let’s dive straight into how you could use machine learning to improve your manufacturing business.
Data has become a precious resource across almost every industry, and it’s never been easier to capture and store important data. Data can guide marketing strategies, improve processes and boost productivity. That’s why so many manufacturers are switching to a data-driven model with great success, thanks to artificial intelligence and machine learning.
IBM defines machine learning as “A branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”
Machine Learning uses large volumes of data to train sophisticated algorithms, such as artificial intelligence. It can then be applied to new data to identify hidden patterns and predict future outcomes. By incorporating machine learning into manufacturing, industrial AI can use data to create solutions to several crucial manufacturing problems.
You can use machine learning in the manufacturing industry in a range of ways that deliver tangible benefits to a business, including:
It’s worth keeping in mind that machine learning is a very new concept in manufacturing, and its very nature is to learn from every single experience. The benefits are already great, but the more you utilize ML, the better the benefits will be.
So, you’re convinced machine learning is the future of manufacturing — now what? It can be difficult to see how machine learning will fit with our current processes and where to deploy it. So, let’s look at how you can start using machine learning in your manufacturing business.
Without the machines that create your product, manufacturing companies have no business. So, when your equipment goes offline for maintenance or a complete breakdown, that will cost you both productivity and revenue.
Traditional preventative maintenance processes require equipment repairs at regular intervals based on time or usage. Not only does that affect your bottom line, but these methods can also be less than dependable and leave you open to surprise breakdowns, despite putting the appropriate time and care into maintenance. This is especially dangerous for the heavy machinery often used in manufacturing, as one of those surprise breakdowns can come at the expense of your employees' safety.
Using a combination of machine learning and artificial intelligence can access data from a range of sensors to identify issues before they become a problem. This means you only need to take action when there is real potential for machine failure rather than abiding by arbitrary time frames.
Using ML, companies can use data to guide their strategies. Predictive analytics helps businesses make better decisions based on real data. Predictive analytics can help them find gaps in the marketplace, improve customer retention, and predict and quantify risks.
In the manufacturing sector, predictive analytics are beneficial for preventing out of stocks, inventory management, pricing, and many more ways that help reduce waste and boost customer satisfaction.
To make great products, you need great materials. This can be difficult when your supply chain is less reliable, as we see at the moment.
Thankfully, AI and machine learning can sort through different supply-chain-related tasks, such as warehouse and inventory management, inbound and outbound shipments, and customer demand for products. This can help manufacturers avoid falling behind on order fulfillment and overall productivity.
An AI can quickly learn how a product should be with machine learning and spot signs that deviate from that. This can dramatically speed up the quality control process, by catching defective items early in the production line — well before they make it to the customer.
The German car manufacturer BMW offers a great example of how machine learning can improve quality control. They have been using big data since 2014 to identify issues with their prototypes during testing and collect data on cars that have been sold. By doing this, they were able to reduce the number of product recalls, improve product safety, and increase their bottom line.
Digital twins offer manufacturers a sandbox to test products using a digital representation of the product. Essentially, manufacturers can create in-depth digital prototypes, much like digital developers would make wireframe versions of their website for testing. Digital twins can be used to carry out instant diagnostics, evaluate production processes, and make predictions of the product’s performance.
Using data from multiple sources, an ML-powered digital twin will continuously learn and update itself to represent the current working condition of the product. This allows decision-makers to understand the product and improve and optimize performance.
Machine learning is just one way technology is transforming the manufacturing industry. If you’re looking to update your operations with the latest and greatest tech, Brahmin Solutions is ready to help! Contact us today, and we’ll show you just how easy it can be.