The modern manufacturing scene is highly competitive, requiring businesses to tap into various resources to gain an edge over the competition. This has led to the rise of Industry 4.0, an era defined by constant interaction between factory machines, workers, and computers over the internet.

Tapping into the power of the Internet of Things (IoT) has allowed manufacturers to better manage productivity, efficiency, and safety on the shop floor. To further improve operations, manufacturers have turned to artificial intelligence and machine learning in order to apply the massive amounts of data that is created during production.

As a result, the artificial intelligence availed by machine learning programs draws patterns and highlights areas of interest, providing critical insight into the production process and opportunities for improvement. As a result, manufacturers can make better production-centric decisions, driving the business forward.

In this post, we’ll review how machine learning is being used in manufacturing and how it can elevate your production processes and overall business standing.

What exactly is machine learning in manufacturing?

Before exploring the various applications of machine learning in the manufacturing environment, it’s important to understand the technology itself. Machine learning essentially entails feeding algorithms with lots of data, allowing them to progressively learn and improve processes on their own, similar to human beings.

Machine learning and artificial intelligence are often used interchangeably, but are different in that AI refers to the broader concept of using computers and machines to simulate human thinking, whereas machine learning is the application of AI that enables computers and machines to learn without being explicitly programmed by humans.

Manufacturers utilize two main machine learning models that lend themselves well to the production process and its associated activities. These include supervised and unsupervised machine learning.

The former involves leveraging AI to draw patterns from large data sets with a predefined end. This is particularly useful in determining the remaining useful life of a machine and the chances of specific equipment failure.

On the other hand, unsupervised machine learning concerns itself with identifying patterns from data sets whose outcome isn’t yet known. For instance, engineers and supervisors can use machine learning to detect unknown anomalies and faulty components in the production process.

This brings us to the practical applications of machine learning within a manufacturing facility.

Examples of machine learning in manufacturing

Manufacturers are continually finding new ways to apply artificial intelligence to various facets of their production operation. Some of the common use cases for AI in manufacturing today include:

Predictive maintenance: The tech draws from machine and equipment data to identify patterns that point to possible failure. This allows the relevant operators and maintenance crews to fix machines before they break down, reducing downtime so that the line continues moving.

Logical and analytic supply chain management: Manufacturers need to bank on their suppliers to provide adequate and high-quality materials for the production process. AI and machine learning can sort through different supply-chain-related tasks like warehouse and inventory management, inbound and outbound shipments, and customer demand for products.

This prevents manufacturers from falling behind on order fulfillment and overall productivity.

Advanced digital twin utilization: Digital twins are virtual representations of physical objects. In the manufacturing realm, this can be applied new products, equipment, and even shop floor layouts. Additionally, manufacturing businesses leverage machine learning to optimize product design and customize the production line for more efficient operation.

Energy utilization and prediction: Machine learning analyzes raw data from factory machines, drawing patterns regarding prevailing energy consumption. Additionally, manufacturers use the technology to predict future consumption, allowing proper planning.

Predictive quality and yield: Manufacturers apply machine learning to detect possible causes of yield losses and quality defects. The tech identifies probable wastage causes, allowing manufacturers to adjust their operations accordingly.

Benefits of machine learning in a manufacturing environment

With the different use cases, manufacturers stand to gain significantly from adopting machine learning in their production operations. Some of the most notable benefits of machine learning include:

Better supply chain management: With the proper deployment of machine learning, manufacturers ensure that the factory always has adequate materials. This improves customer satisfaction due to timely order completion and delivery.

Improved quality control: As earlier stated, manufacturers leverage machine learning for predictive quality as well as maintenance. This allows the production line equipment to remain in perfect working order, reducing the chances of defective products coming off the line.

Lower production costs: Utilizing machine learning in the earlier use cases leads to lower production costs. As a result, the business spends less on fixing broken machinery, utility bills, and space utilization.

Better employee safety: Predictive maintenance ensures that employees aren’t affected or put in harm’s way by operating faulty machinery. Additionally, the ideal space utilization and layout ensure that workers only ever need to operate in safe zones.

Improved product design: Machine learning allows manufacturers to design in-demand products by considering customers’ needs. Businesses can offer market-ready products based on the confidence that their products solve a particular pain point in the market.

How to get started with machine learning

Getting started with machine learning requires manufacturers to develop systems and processes that enable consistent, reliable data collection across all production activities in order to feed their models.

Additionally, machine learning can be particularly challenging for manufacturers if they don’t have the expertise to translate the tech’s power into practical uses in their production operation.

Using Tulip, manufacturers are able to leverage an interconnected ecosystem of applications that allow accurate, consistent data collection to feed their models with high volumes of quality information upon which their models can learn. This allows for an all-round infusion of the tech into the various production processes, promoting many of the benefits described above.

Ultimately, manufacturers should be patient with machine learning because it keeps evolving as it acquires more data to learn from. As such, businesses need to continuously apply the technology over their operation lifetimes to ensure they can realize the massive benefits that come with this next-gen technology.

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