Machine learning (ML) is a rapidly developing technology that impacts almost every aspect of a business. It enables computers to “think” and learn alike humans, basing their conclusions and future predictions on analysis of historical data and real-time data. One of the industries that can particularly benefit from machine learning applications is manufacturing.
The global market of ML in manufacturing is likely to reach $16 billion by 2025. Manufacturing companies invest, among other things, in machine learning solutions to automate processes and reduce operating costs.
Let’s take a closer look at some machine learning in manufacturing applications.
Machine Learning Applications in Manufacturing
In order to make a real difference in your manufacturing business, you need to work on everyday processes that span the entire cycle. Machine learning tools are able to deeply analyze data and determine different kinds of areas which should be improved.
Business leaders now have insights on the efficiency of logistics, management of supply chain, and complex information about the current level of inventory and assets. Only with a complete overview of these matters can manufacturing companies open up to new opportunities, prepare an effective business strategy, and invest in the most valuable development processes.
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The product development phase can greatly benefit from machine learning solutions. In order to plan the introduction of new products and the improvement of existing ones, a huge amount of information needs to be taken into account.
Machine learning technology is irreplaceable when it comes to collecting and analyzing customer data. It allows companies to assess the level of demand, take into account all consumer needs and spot emerging trends.
Thanks to the insights gained, both existing products and future projects can perfectly match the needs of customers. In addition, new information enables business leaders to efficiently plan production processes and avoid undesirable risks.
Machine Learning In Manufacturing – Quality
The quality of the end product is crucial for any company looking to increase revenues. Machine learning technology can significantly improve this. How?
First, by identifying anomalies in both products and packaging. Manufactured products undergo a deep examination that identifies defective products that are eliminated and never reach the market.
Second, the quality of the manufacturing process can be increased through machine learning applications. Companies can now perform a comprehensive analysis of the availability and performance of equipment used in production.
Based on the information obtained, predictive maintenance can be implemented. Experts can estimate the optimal time for given equipment to minimize downtime and extend its life. Companies may experience a decrease in costs after making these changes.
Machine Learning In Manufacturing – Security
Today, the security threat is more real than ever. We share huge amounts of data via a variety of mobile devices and applications. Manufacturing companies also use these technologies, which is why they must invest in reliable security systems.
Machine learning in manufacturing offers a unique solution – the Zero Trust Security (ZTS) framework. It enables companies to control and limit digital access to confidential information.
ML models can be used to observe and analyze the activity of individual users who gain access to, particularly valuable information. Business leaders get answers on which applications allowed access and connect to databases.
In addition, machine learning software can detect anomalies and automatically send alerts to specific employees.
The use of a zero-trust framework is still new to most manufacturing companies, but will certainly grow in popularity in the upcoming years.
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Perhaps one of the most exciting potential machine learning solutions in manufacturing includes robots. Machines powered by artificial intelligence can take over routine tasks that are time-consuming and dangerous to humans.
Robots evolve rapidly and are capable of performing increasingly complex tasks. Manufacturing will soon forget the era of simple assembly lines and replace them with AI robots capable of automating complex processes. Manufacturing companies can accelerate and expedite production while lowering personnel costs.
In a few years, robots will become partners for employees who will be able to cooperate on complex tasks. This machine learning solution will improve productivity and reduce human error.
Machine Learning In Manufacturing – Market Adaptation
Business leaders need to create effective strategies that match the current market trends. To design a complex plan for the future of the company, managers need reliable forecasts.
Machine learning models based on AI technology can analyze huge amounts of data, combine various factors such as consumer behavior, political situation, economic status, etc., and provide accurate forecasts for the future. This solution can give your company a competitive advantage and improve your business results.
In addition, machine learning algorithms can calculate the number of inventory, personnel, and material supply needed. Since the numbers are based on AI predictions, the new calculations are saving companies a lot of money.
Each company makes every effort to minimize downtime caused by hardware failures. Experts are trying to determine when equipment maintenance should be carried out to prevent major breakdowns.
Machine learning algorithms can do this job faster and better. By analyzing multiple data sources, ML programs can predict and plan optimal repair time. Errors are noticed immediately and the relevant employees are instantly informed.
By analyzing historical data, machine learning models can identify hardware failure patterns and determine when to perform regular maintenance.
Reliable supply chains are essential for any company operating in the manufacturing industry. There are many factors that can change and, as a result, generate additional costs. Manufacturers can expect equipment damage, ship errors, changes in fuel prices, and unexpected weather conditions, among other things.
Machine learning algorithms analyze each of the above-mentioned factors and optimize these elements, resulting in the creation of an efficient supply chain. ML models will answer questions such as how much extra time is needed for shipping and where it should be shipped. All possible scenarios are analyzed so that business leaders can make the best decisions.
The increase in productivity translates directly into an increase in production, which often results in an increase in revenues. Investing in machine learning solutions is essential to successfully running a manufacturing business.
While supply chain optimization is a popular topic, less attention is paid to inventory optimization. Storage costs are huge, usually around 25% of production costs. That is why inventory optimization is so important.
Machine learning algorithms are experts at calculating the best possible decision from an economic point of view. ML software can evaluate what is more beneficial to the company at any given time – sell or hold inventory, and increase or decrease production. All of them take into account current market prices, production capacity and storage costs.
Machine learning models have already exceeded the human ability to judge the situation when considering all available factors. With the amount of data collected on a daily basis, analysts would have to spend too much time calculating to respond in time to market needs.
Machine Learning In Manufacturing: Conclusion
Machine learning in manufacturing offers numerous solutions to the most common problems. Businesses can improve their manufacturing processes and reduce related costs.
Companies operating in manufacturing should observe the latest solutions and invest in machine learning technology as it will significantly reduce their cost and potentially increase revenues.