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8min January 4, 2021 8min Jan 4, 2021
January 4, 2021 8 minute read

Guide to Machine Learning in Manufacturing

The connected factory enables manufacturers to collect, analyze, and innovate from their data. Introducing Machine Learning to this equation can accelerate the detection, optimization, and innovation capabilities of your workforce.

What is Machine Learning for Manufacturing?

Machine Learning has garnered a lot of attention in recent years among industrials. With it has come confusion between the buzzwords Machine Learning, Artificial Intelligence, and Data Science. Simply put, Machine Learning is a facet of Artificial Intelligence aimed at enabling machines to learn to think like humans in an autonomous way

Leveraging predictive analytics and prescriptive models, Machine Learning ingests historical data to learn and improve through experience. Over time, it can recommend solutions and identify anomalies based on what it has learned. Machine Learning may be applied through models, but also by Data Scientists, a third-party software, or some type of hybrid model/app.

What AI Means for Your Workforce

Manufacturers across a variety of sectors can achieve digital transformation by employing the technologies and practices of Industry 4.0. But what does Industry 4.0 mean for the human workforce? Many fear AI and Machine Learning will fully replace humans. Laurent Laporte, CEO and Co-Founder of Braincube, makes the case for how digital transformation enables companies to stay competitive and empowers manufacturing’s human workforce.

Advantages of Machine Learning for Industry

Automated Big Data Processing

Machine Learning can process far more data per second than any human, saving industrials lots of time from pulling and formatting results. The time your process engineers save manipulating data can then be used elsewhere to drive innovation and continuous improvement. In addition, the vast amount of data points it can cross-reference outweighs the physical capacity that limits human discovery alone.

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Pattern and Anomaly Recognition

Machine Learning is most often known for pattern and anomaly recognition (i.e., unlocking your phone with your face or isolating defective products). Supervised, Unsupervised, and Reinforced Learning algorithms can be used to identify common patterns based on your historical production data. Machine Learning is then used by manufacturers to identify anomalies such as defects, predictive maintenance, or bottlenecks.

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Continuous Improvement

Algorithms such as Braincube’s proprietary CrossRank, and apps such as the Comparator App and Advanced Analysis App are capable of learning from your production data. These apps not only crunch through Big Data quickly, but are further able to dynamically (and visually!) update in live time to make use of current and past data collection. With insights at your fingertips, continuous improvement can happen more rapidly.

Challenges of Machine Learning

Requires Centralized, Contextualized Data 

A barrier for some manufacturers is accessing their data in a centralized location and even more importantly, accessing valid, accurate data with context is a challenge for many. Leveraging Machine Learning in manufacturing requires a platform to pull historical data from systems, devices and things and then extract, transfer and load that data into a centralized environment that can be accessed by all (IIoT).

By implementing an IIoT platform that connects and contextualizes your data for you, your opportunities to make use of data have begun! From there, choosing an IIoT platform vendor or a third-party software that contains Machine Learning capabilities is critical to a long-term strategy for a data-driven culture. Whether you have an immediate need for Machine Learning or not, thinking about the future will set you up for immediate and long-term success. 

Aligning Technology and People

Within Industry there is often a concern of technology replacing people, but we don’t see it this way. The value of technology like Machine Learning is that it enables humans to do their jobs faster and more effectively. It’s not about replacing people, but rather augmenting the tedious, manual work that can be done faster and more efficiently with AI and/or Machine Learning.

Focus on investing in the people who will use the technology, not just the technology itself. Even the most advanced AI and Machine Learning programs rely on knowledgeable operators to make sense of trends, anomalies, and variations that IIoT platforms uncover. The people using this technology—operators, engineers, plant managers—are the ones who discover the big wins that will move your company forward.

Machine Learning Use Cases in Manufacturing

With many in Industry looking to use their production data in a better way, Machine Learning has become an asset in uncovering often unexpected opportunities and optimizations. Here are some of the ways Machine Learning has improved industrial performance:

With costs continuously rising, manufacturers are looking for a way to reduce or cut costs altogether. From increasing prices on raw materials to workforce training and preparation, and machine and asset maintenance, industrials have already tackled the obvious optimizations. Now, there is an opportunity to drive results even further with AI.

Machine Learning can be used to identify better strategies for predictive and preventative maintenance, pinpoint optimal settings, uncover hidden bottlenecks, and identify new product opportunities that can then be scaled to workflows, systems, and other factories globally.

Learn More Cost Reduction Opportunities —>

Challenged by decreased interest in manufacturing jobs and an aging workforce, the manufacturing industry is facing a global labor shortage. This shortage is impacting manufacturers’ ability to scale and/or innovate, hire and retain talent, and train a new generation of digital natives to do notoriously habit-driven manufacturing jobs.

Machine Learning has the ability to make manufacturing appealing to more candidates and turn engineers and analysts into Citizen Data Scientists. This brings the ability for ongoing analysis and management of production lines, making processes faster and easier for all, and leaning into Big Data analytics. In short: Machine Learning will enable people to do their jobs better, rather than replace them.

Learn more about Improving the People Shortage —>

Few things erode customer confidence faster than a product defect or quality issue. Within continuous manufacturing, identifying these issues and finding a feasible solution are crucial. 

Machine Learning-powered analysis and identification will help manufacturers leverage these data science tools to get alerted when a defect is likely to occur. With a higher rate of accuracy and speed, Machine Learning can replace human recognition and categorization to free up time for more effective tasks.

Learn more about Recognizing Defects —>

Despite strains on the supply chain and critical resources, manufacturers are challenged with this conundrum of producing more while using less. Whether you are looking to conserve energy or convert to more renewable resources, Machine Learning can help you get there.

Machine Learning enables predictive analytics that process engineers can use to test, identify, and scale processes that optimize their use of critical resources. Operating at the optimal set points to increase throughput, manufacturers can isolate areas of improvement upstream without too negatively impacting the process downstream.

Learn more about Reducing Energy Consumption —>

The Future of Machine Learning in Manufacturing

Today, data scientists and engineers look to Machine Learning to understand how to apply changes in the moment. In the future, Machine Learning will be used to run automated tests, changes, and day-to-day operations by sending these decisions back to the edge, known as the autonomous factory.” 

— Sylvain Rubat du Mérac, Co-founder and Chief Technology Officer, Braincube


Laurent Laporte, CEO and co-founder of BraincubePeople are still spending a lot of time teaching AI and Machine Learning to learn and behave in the way we would expect. There is a lot of value in Machine Learning today, but the future is sure to hold new technology and growth in AI that will learn faster, require less energy and further increase accuracy.”

— Laurent Laporte, Co-founder and Chief Executive Officer, Braincube

The Takeaway 

Regardless of your current use, ideal use, or in-progress use of Machine Learning, research shows that it’s a phenomenon that’s here to stay. From improving your day-to-day product quality, to Big Data mining, to augmenting some of the less desirable tasks your teams carry out, there are many use cases where you can leverage Machine Learning to do some of your heavy lifting.

AI vs. Machine Learning vs. Data Science

The popularity of Artificial Intelligence (AI), Machine Learning, and Data Science have grown in manufacturing from the pursuit of the connected factory and Industry 4.0. But with this expedient growth has also come confusion. Understand the key differences, how they intersect, and use cases for each. 

Who Can Leverage Machine Learning?

The increase in analytics-driven decision-making in Manufacturing leaves some wondering—do you need to be a Data Scientist to leverage the power of augmented analytics and Machine Learning?

The Importance of Legitimizing AI

Production teams use AI-enabled analytical tools in an IIoT platform to gain access to the data that can answer their questions or offer them prescriptions at the right time. But how legitimate are these AI solutions? How can industrials ensure the suggested parameter modifications that AI proposes are the “best”? CEO of Braincube, Laurent Laporte, discusses the importance of legitimizing AI in Industry.

4 Ways to Use Machine Learning

Continuing to find new ways to improve operations requires increased creativity, capacity, and access to critical data. Industrials use Machine Learning to identify opportunities to improve OEE at any phase of the manufacturing process. Learn how to use Machine Learning to solve some of the biggest challenges faced by manufacturers.