4 Use Cases for Machine Learning in Manufacturing

Pressed by leadership, many process engineers and data scientists have already tackled the obvious optimizations within their factory. Continuing to find new ways to improve operations requires increased creativity and access to critical data.

Enter: Machine Learning. The goal of using Machine Learning is to identify opportunities to improve industrial operations and OEE at any phase of the manufacturing process—streamlining your process from raw materials to product requires data. Before you can leverage the power of Machine Learning, you need to be connected with the ability to measure the right data points, so that you can uncover optimizations. Once connected, Machine Learning can help manufacturers analyze Big Data faster and more efficiently than the human mind can physically complete.

Many in industry are looking to use their data in a better way. Here are the 4 ways Machine Learning can improve your production:

Cost Reduction

In manufacturing, continuous improvement is the goal. Everywhere you turn, there are increasing costs from raw materials to workforce training and preparation, and especially machine and asset maintenance. All of these interruptions can cause hours or even days of lost productivity. It can back up your supply chain, impact your overall quality, and prevent your process engineers from focusing on optimizations to uncover new savings. Process engineers, data scientists, and/or analysts need to find creative ways to cut costs and run production more efficiently. 

Machine Learning is improving productivity by uncovering better strategies for predictive and preventative maintenance. Once uncovered, industrials are then able to scale the workflows, systems, and discoveries they find and deliver these learnings to other factories globally. 

“Braincube focuses us to have discipline around strategic thinking. Previous technologies only let us look at immediate issues, put out fires, or solve the problem of the day. Now we can look at our overall company objectives and use Braincube to help us get there.” 

A Braincube building materials customer

With Machine Learning, industrials are able to anticipate required maintenance and in some cases, are even able to avoid unexpected (and costly) downtime. Industrials can use Machine Learning to anticipate needed maintenance on machines to proactively schedule and execute critical maintenance. This helps launch companies into a maintenance schedule that revolves around machine need instead of operating on a cyclical calendar where machines may or may not need service. It can also help prevent those unwanted fire drill service calls that slow down production and decrease OEE.

Advanced Analytics and pattern recognition can help process engineers identify problems they were previously unaware of. Machine Learning can help to identify hidden bottlenecks in manufacturing processes, opening up the opportunity to create further efficiencies of resources and automation whether it is space, goods, personnel, or time.

Improve the People Shortage

The manufacturing industry is facing a global labor shortage. Interest in manufacturing roles has decreased over the years due to the misconception that these are dangerous, tedious roles.

AI technology, including Machine Learning, is able to augment or in some cases, even replace human labor in the factory. These technologies expand the capacity of your workforce rather than outright replace it. This common misconception of AI replacing jobs, however, is actually unlikely, as there are many things that humans can and will always do better than technology. Here are a couple of ways we see AI and Machine Learning improving a data scientist and process engineers day-to-day:

Some processes within the factory are inefficient and/or ineffective when completed by a human. Take identifying product defects or quality issues for example—having a person conduct quality assurance slows the line down and introduces the possibility for miscategorization. This is one of the obvious use cases within manufacturing where AI might be used to evolve humans’ roles in the factory to increase efficiency and accuracy, opening up the opportunity to focus on continuous improvement. 

The processing capability of a human is powerful, but AI is more effective when it comes to ingesting and contextualizing Big Data. The analysis will always need to be done by a human, because without major breakthroughs in Machine Learning and AI, robust and accurate analysis capabilities require too much time and effort. Machine Learning can be used by humans to uncover opportunities or identify bottlenecks faster and with improved accuracy for decision making.

Industry 4.0 has brought the need for new technology and skill sets among employees that are advancing faster than existing personnel could adequately learn them. This results in underutilized technology on the plant floor and oftentimes overused human hours. At the same time, new employees raised on the digital frontier have the technical skillset to use modern technologies but lack the finesse and expertise of experienced operators, SMEs, or plant managers.

AI has the power to make the manufacturing industry appealing again. The use of technology in manufacturing alone is likely to attract a larger pool of candidates—from those looking for a digital and data-driven career path to those long-time industry veterans looking for a better way to do things. 

Machine Learning enables people to do their jobs better, faster, and with more efficiency.

Machine Learning has the ability to make ongoing analysis and management of production lines faster and easier for all. This technology enables people to do their jobs better, faster, with more efficiency. With AI and Machine Learning, it’s not about replacing humans, but rather helping people be more efficient and connected to their work. The insights and optimizations Machine Learning can bring to Industry could improve overall working conditions and safety, though it is a daunting subject to tackle for many. For many, Machine Learning and AI have the power to increase the demand for careers in manufacturing over time, but organizations need a smart strategy to implement and manage this change.

Improve Quality & Recognize Defects

Defects are a costly mistake that have a trickle-down effect whether they are impacting your customer experience, employee morale, or general manufacturing capabilities. Identifying defects quickly and preventing them takes constant oversight, testing, and improvements. Machine Learning-powered analysis and identification will help manufacturers leverage these data science tools to get alerted when a defect is likely to occur. According to Forbes, machine learning has the ability to identify anomalies with a high rate of accuracy—better than any human inspection.  

For example, the Counter App from Braincube automates the process of counting good and defective parts. By leveraging Machine Learning strategies, this app can monitor your process, create responsive dashboards, and track your production goals via Edge data.

If we look at continuous process manufacturing, say diapers or tires, even a few minutes of out-of-spec production can lead to hundreds of scrapped products, countless wasted resources, or even as a worst-case scenario damaged products shipped to customers. It can cause a bottleneck in your QA, take time from process engineers to uncover the root cause, and more generally slow down output.

By leveraging Machine Learning strategies, recognizing defects becomes an automated part of data collection, monitoring, and control. And for long-term optimization, process engineers can spend their time preventing these defects from happening in the future through Big Data mining, root cause analysis, and pattern recognition models to prevent future stops.

Achieve Sustainability Outcomes

With production comes consumption. Manufacturers are challenged with this conundrum of producing more while using fewer resources. However, there are often times when we need to quickly ramp up production (i.e., roofing materials during hurricane season, box production around the holiday season, or CPG products during a time like COVID-19). We’re often left with a choice: increase energy consumption to produce faster or higher quantities of products or keep energy consumption the same, knowing your company might fall behind market demand.

Whether you are looking to conserve energy or convert to more renewable resources, AI and Machine Learning can help you get there. For example, Braincube customer Cargill, wanted to operate their oil boiler more efficiently. The company only used 30% of the oil boiler’s full capacity to produce steam for its operations. Running the machine more efficiently would enable Cargill to spend less money while generating the same amount of steam. 

By using Braincube’s CrossRank Analysis AI, Cargill’s process experts accurately identified the correct operating conditions to stabilize steam output. Putting Braincube’s insights in front of Cargill’s analysts enabled them to find faster solutions to their problems. 

In general, predictive analytics powered through Machine Learning enables process engineers to test, identify and scale processes that optimize their use of critical resources. By identifying optimum operating set points to optimize throughput, they can isolate areas of improvements upstream without too negatively impacting the process downstream.

As many companies continue to focus on their sustainability efforts, Machine Learning will continue to serve as an opportunity to drive these initiatives forward.

Get Started with Machine Learning for Manufacturing 

The impact of Machine Learning in manufacturing can be felt across departments. From improving the bottom line to solving some of the biggest challenges faced by manufacturers today, Machine Learning is a useful tool for process engineers, data scientists, and analysts focused on continuous improvement. 

To begin leveraging the power of Machine Learning in your factories, identify clear learning and business objectives, connect your data to a powerful IIoT platform, and drive the shift to a more data-centric decision-making process. 

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