Modern companies understand the value of data. Data allows factories and plants to run their processes more efficiently to increase capacity and quality.

It also allows them to organize their maintenance work effectively to reduce downtimes and get the most value out of their assets. These improvements come together to reduce the company’s costs and increase its profit margins.

At least, that’s the idea. Many companies are collecting more data than ever before from their manufacturing processes but they are struggling to convert the data into valuable, actionable information.

Unfortunately, raw data has to be analyzed and processed to yield the insights that will actually help a company improve its processes.

Until recently, advanced data analytics was a highly specialized skill that was not available to many manufacturing companies.

In this article, we will show you how recent advancements in machine learning are making it easier to apply machine learning to manufacturing data to extract insights from data and use those insights to make informed decisions on maintenance and quality monitoring.

After showing you how machine learning is already being applied in manufacturing, we will explain how you can use software solutions to build up the data collection and analysis capabilities on your factory floor today.

In this part, we will talk about machine learning-powered solutions and solutions that involve traditional data collection and monitoring.

Before we look at how machine learning can be applied to the factory floor, let’s talk about the problems that modern factories and plants face.

Problem

Most machines running on the shop floor are designed to report failures, not to predict them. Control systems are used to collect and process the data from the machines in real-time.

HMIs and SCADA systems are designed to show what is happening in the manufacturing process right now or in the past. They cannot tell you what will happen in the future.

That’s a shame because every failure that occurs can lead to expensive downtimes that halt production.

Some manufacturers have worked around this problem by implementing a preventative maintenance system. With preventative maintenance, you replace parts of a machine long before they fail to avoid production outages.

This strategy helps to avoid production outages but it is wasteful and it increases costs. It is very expensive to replace parts that are working and may continue to work for a long time with new parts.

Machine learning enables manufacturers to move from a preventative maintenance system to a predictive maintenance system.

With predictive maintenance, you use an algorithm based on historical data to determine if a part will fail soon and replace it as late as possible.

This strategy avoids production failures by replacing parts when required and reduced costs by only replacing parts that actually need to be replaced.

Unfortunately, most machines and process equipment are not designed to handle machine learning algorithms.

Although this equipment generates all of the data required to feed an algorithm, they do not have the processing capabilities to run these algorithms.

Even if they did, the engineering staff at these companies do not have the know-how to realize a complex machine-learning algorithm that accurately models the process anyway.

Data analysis and interpretation

Right now, many companies have started implementing a digitalization strategy where they use modern technologies like Single Pair Ethernet to collect data from the factory floor, IoT gateways to transmit the data, and on-premise or cloud-based databases to store the data.

As the pool of available data grows, the challenge becomes the interpretation of the data.

How can manufacturing companies analyze and interpret their data to improve their production processes and maintenance systems?

Solution

Although there are many different ways to analyze large data sets, machine learning is quickly emerging as the most efficient and cost-effective way to generate results.

In other industries, machine learning is revolutionizing the way data is analyzed and decisions are made. Companies have already seen that machine learning can create real value from collected data.

The manufacturing industry has been slow to deploy machine learning technology because it has always been difficult to do.

Until recently, using machine learning on your data involved hiring a team of data scientists to build a bespoke model, waiting for months while they trained the model, and eventually getting some results from the project.

The problem with this approach is not just that data scientists are specialist resources that are difficult to find.

Since data scientists are so rare, it has been hard for manufacturing companies to hire people with the right combination of data science skills and manufacturing knowledge.

What was required to deploy machine learning on the factory floor was an easy-to-use software that allows engineers to build, train, and deploy machine learning knowledge without data science skills.