Using Machine Learning In Manufacturing Processes


The use of technology across all industries has been beneficial in improving processes, and convenience, lowering costs of operations and production. The manufacturing industry has not been left behind in this. The players are always on the lookout for new ways to optimize and streamline their operations. Many are turning to Machine Learning (ML) to help with this and reduce errors in the processes and drive more productivity in the human workforce.

The implementation of machine learning processes can help manufacturers make work easier, predict machine failures, identify production gaps, and reduce downtime. While the technology may have a steeper learning curve, business leaders can enlist the help of any good machine learning consulting company for education and brainstorming.

The consulting companies can help them identify gaps in existing systems, opportunities for improvement, and develop custom solutions for them, implement and monitor ML projects. This article explores the simple ways companies in manufacturing can leverage the power of ML.

What is Machine Learning?

Machine learning is a technology that uses data fed to computer programs through artificial intelligence to learn and create outcomes based on data analytics. These outcomes are as reliable as the data sets fed to the algorithms and they can help businesses in all industries make better decisions on issues. This works for the manufacturing industry in many areas.

ML systems help organizations leverage the power of their accumulated data to streamline more efficient processes that can lead to lower operational costs. There are various ways the manufacturing industry can benefit from ML. Here are a few uses of ML in manufacturing processes and how businesses use their various applications.

Predictive maintenance

Unfortunately, machines and gadgets can fail unpredictably. But if there is a way to know when that will happen, manufacturers would not worry about halted operations and losses from unplanned maintenance. Luckily, one of the main benefits of machine learning in manufacturing is the ability to predict the future. By analyzing historical data fed to it or reading real-time data from devices, trained ML algorithms can identify patterns that may indicate a potential failure.

This allows companies to schedule maintenance activities before a breakdown occurs, reducing downtime, extending machine lifespan, and ultimately reducing maintenance costs. In fact, predictive maintenance has been reported as one technology taking root fast in the manufacturing sector. Instead of battling with expenses from unscheduled maintenance, you can make the right and timely adjustments before your operations are halted by maintenance emergencies.

Quality assurance and control

ML processes can also help improve product quality by identifying defects during the manufacturing process.  By analyzing data collected from various connected devices, ML algorithms can identify defects that a well-trained human eye might miss and take corrective action. Catching defects early in the process reduces waste and improves product quality.

Research has shown that the quality of a product is a huge determinant of customer satisfaction. Therefore, making quality products should be a priority. Properly trained ML algorithms might help your business achieve that.

 Inventory management

Your ML algorithm can help you identify peaks and spikes in demand in your inventory or supplies. With the right data, they can give an output that can help your business managers make more proactive decisions in inventory management. By analyzing historical sales data, the algorithms can predict future demand patterns.

This leads to more informed decisions on when a particular item is in higher demand to avoid stockouts or overstocking when the demand is predicted to go down. Learning and accurately identifying demand patterns in the process chain makes it possible for businesses to maintain optimal order quantities for supplies. This reduces inventory holding costs and ensures that the right products are available at the right time.

Product development and optimization

The manufacturing industry can rely on ML in improving existing products as well as the creation of new lines. These two areas require extensive research and data analytics. With ML, data analysis is bliss. In addition, the algorithms can help cluster data more effectively to uncover flaws in the systems that lead to inefficiencies or unproductivity.

ML algorithms are a good way to identify gaps in processes and inefficiencies in production. If the algorithms have the right data to go by, they are capable of issues in production. This can help your business to find ways to streamline weak areas, improving throughput, reducing costs, and errors in the process.

Improved cybersecurity

All ML solutions are built on data, technology, and networks, be it on-premise or cloud platforms, making their security essential and critical. Access protocols and regulations are therefore highly regarded and ML can help in regulating and controlling information access on various platforms.

This is a critical matter in the manufacturing industry as a small hack in the process can lead to massive losses. Manufacturers can use ML to streamline data, applications, and systems access to protect them from malicious interference through early anomaly detection, leading business leaders to take corrective actions sooner.

Non-destructive testing

Non-destructive testing is one of the best ways for manufacturers to detect and avoid defective product lines. This process allows manufacturers to measure the quality, performance, and stability without causing any damage to materials under testing. This includes technologies like ultrasound that are used to detect and find cracks. When testing is done manually, it still leaves some room for error or missing small but important details.

With the help of ML algorithms for detection and segmentation, it’s easy to automate the process and allow the machine to learn and analyze data for patterns a human eye may fail to identify.

Use of robotics

While the manufacturing industry is still highly reliant on human processes and the workforce, automation is also taking its place. There is a growing adoption of the use of machines and robots to perform complex tests that are either too repetitive, require high concentration, or are too labor intensive.

When used with advanced ML techniques, robots can provide even greater value, while reducing manufacturing costs. You can leave the complex and tedious manufacturing processes to the learned robots, creating more flexibility for the human workforce.

Identifying opportunities for machine learning

The manufacturing industry is process oriented and it involves reading massive amounts of data that are regularly changing due to industry demands, updates,  and regulations. It can be difficult for business leaders to understand the importance and use of machine learning and associated technologies in manufacturing if they can’t identify opportunities and benefits. However, the experts in the field do. They can help you identify and understand specific challenges and recommend machine learning solutions that can help your business to be more efficient. Work with a reliable ML consultant and future-proof your business with reports and predictions from real data analytics.

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