Why Manufacturing Leaders Choose Custom Machine Learning Solutions?
Updated on Apr 20th, 2021
The machine learning provides its users with meaningful insights from raw data so that they can quickly solve complex, data-rich, business issues. Moreover, with all the buzzword around the big data, AI and ML, in manufacturing enterprises and organizations have become curious about the application and businesses are reaping the advantages of machine learning. The machine learning is evolving at a rapid rate and is largely being driven by computing technologies. Giants like Amazon, Microsoft Azure, Google, are launching their Cloud Machine Learning platforms, therefore, we can now witness the Artificial Intelligence and Machine Learning have gained prominence currently. Increase in the volumes, trouble-free availability of the data, faster computational processing are some of the benefits the businesses are taking advantage of and it is surprising to see, we all are witnessing Machine Learning technologies in our daily lives without even knowing, For Instance, photo “face” tagging by Facebook, “spam” detection by e-mails, and much more.
Whereas the manufacturers often feel pressured while decreasing costs, and at the same time delivering high-quality products and services. But some of the manufacturers are actively participating to streamline their operations with the Artificial Intelligence and Machine Learning.
AI in Manufacturing Examples
1. Predictive Manufacturing Design
There are many traditional manufacturing processes that require a certain amount of investment in prototyping and destructive testing to reach out to cost-effective solutions. The manufacturing development is expensive not only when it comes to the raw materials but also consumes a lot of time. But, at the same time, it is very essential to prove that the design is worth to meet the client’s needs. Manufacturers can use AI/ML to look for the patterns in manufacturing data to get the best possible manufacturing solutions. Machine learning helps the manufactures to find a wide variety of data that also includes raw materials properties, configurations, test results, etc. These high-probability solutions provide opportunities to the manufacturers so that they can reduce costs and improve the quality.
2. Predictive Maintenance
Cost reduction through predictive maintenance is the direct benefit of machine learning in manufacturing. It, in general, leads to less maintenance activity which automatically reduces the labor costs and eliminated inventory wastages. According to reports by Deloitte poor maintenance can reduce the production capacity up to 5% to 20%. The maintenance process requires machines to be repaired at a certain period of time according to the way they are being used. But this still leads to machinery or equipment failure, failure resulting in the idle workers, an increase in the scarp rates, due to this, the factory at times lose revenues, as well as have to face customer dissatisfaction. The maintenance process demands parts replacement which ultimately costs time and money. AI and ML-based predictive maintenance make use of the data and IoT sensors integrated into the equipment, it also helps in identifying the components that should be replaced and are the reason for the breakdown. The AI and Machine Learning models help the manufacturers to identify the patterns that could easily detect the failure mode for particular components and generate the predictions accurately, especially at the time of equipment breakdown. So, in brief, the role of machine learning is to look for the ideal moment to make the right choice. And eliminate the guess and stressful work.
So this makes it clear that in order to avoid cost inefficiencies, machine learning has to be kept at the core of the equipment.
3. Supply Chain Optimization
Some of the direct benefits of machine learning also include supply chain optimization with the help of effective and efficient inventory management, monitored and synchronized production workflow. Supply chains are a vital part of the manufacturing process. As the complexity is increasing day by day in the global economy, and so does the supply chain optimization challenges. Changes in the weather conditions, ships being damaged as well as an increase in the fuel prices can reverberate throughout the supply chains and can affect the business strongly. These are the complex factors that are taken into consideration by machine learning and optimizes them in return. This includes the time of shipment, location, where to ship from depending on current weather conditions.
Apart from this, at a time machine learning algorithms take dozens of factors into consideration before making the best choice for your business.
4. Transportation Optimization
Here we can say, closely connected with supply chain optimization, machine learning has a similar impact on optimizing transportation. The machine learning in transportation ensures, optimal quality of products delivered at the time of transportation. The manufacturers have to ensure the goods delivered in the right condition, apart from the types of goods they produce. Food and Agricultural Organizations of the United Nations have reported that around 40% of the food have been destroyed in the processing and development stage, in many developing countries. This has a straight impact on food manufacturing, processing, as well as transportation industries.
Quality management is vital. Manufacturers can predict the quality of their products. Artificial Intelligence and Machine Learning provide manufacturers with the opportunity to improve refrigeration (for perishable goods) or optimize routes, especially for raw materials and finished products. The transportation industry can leverage the benefits of AI to get route information, current weather conditions, fuel costs, to keep track of the quality of the product until at the destination point.
How AI Helps in Manufacturing?
1. Enhance the Precision of Financial Rules and Models
Undeniably machine learning has a huge impact on the financial sector as well. With a large amount of quantitative data, ML can be used in financial analysis. The sector is reaping the benefits in the way like; fraud detection, portfolio management, loan underwriting, algorithmic trading. Moreover, in a report by Ernst and Young “The Future of Underwriting” It has been mentioned that Machine Learning facilitates data assessment in order to detect nuances. And soon in future ML in finance will have chatbots and conversational interfaces for security and customer service. This would definitely help in improvising the functioning of financial rules and models.
2. Customer Lifetime Value Prediction and the Name
Lifetime value prediction and the name are the major challenges faced by marketers today. Machine learning helps marketers to get relevant and enormous data sourced from different channels, for instance, lead data, website traffic, and email campaigns. Machine Learning helps in achieving an accurate prediction and data-driven marketing by eliminating the guesswork. For instance, the e-commerce website can browse behaviors and purchase histories of the users to cater to the right products and deals to them. Not only this, data representing the behavior pattern of the users at the trial period, helps the business to predict the probability of conversion. This helps marketers to engage the customers and convert them at the trial period only.
3. Continuous Improvement
Since the ML algorithms are gaining experience, they help businesses improve efficiency and accuracy. Additionally, they can make better decisions and improvise them whenever needed. For instance, when you need to prepare a weather forecast model. Since the companies have an increasing amount of data, the ML algorithms learn to make accurate predictions easily and quickly. Since we all understand the concept of machine learning we can see how pervasive it is becoming, as it enables computing systems to identify patterns in the data and improve in the continuous manner, the outcomes of those patterns through “learning.” for instance, we have witnessed how “Netflix” make use of machine learning to predict the shows and movies depending on the viewers history and attributes.
Machine learning enables automation. Since the automated machine learning has made it possible for businesses in every sector- from healthcare to retail, manufacturing, and sports, to leverage the Artificial Intelligence technology and machine learning- the technology which is earlier available only for the organizations with vast resources at their disposal. Automation in machine learning has helped many business users to reap the benefits of machine learning solutions with ease, thereby allowed the data scientists to focus on more complex problems. Automated machine learning has represented the fundamental shift in a way the organizations approach machine learning. While using ML you do not need to keep track of every step in your project. Since the machine is able to learn all by itself, it predicts and improves algorithms on its own. Additionally, AI/ML can recognize spam way more easily.
5. Handle Multi-Dimensional and Multi-Variety Data
Inaccurate data and duplicate data are some of the common issues faced by organizations nowadays. Predictive modeling algorithms and machine learning can significantly handle multi-dimensional and multi-variety data and can avoid the errors done by manual data entry. The machine learning programs make these processes go seamlessly with the help of discovered data. Moreover, the algorithms are capable of handling data even in uncertain conditions. Including this, machine learning programs can easily perform time-intensive data entry tasks, making us free to focus and carry out other value-adding tasks to the business.
6. Detects Spam
The increase in the volume of unwanted email called spam has created an intense need for the development of robust anti spam filters. And machine learning in spam detection has been in use for quite some time and is one of the earliest problems solved by machine learning. Machine learning methods are being used to detect and filter spam emails. To handle the email threats effectively, leading email providers like Gmail, Yahoo mail and outlook have employed the combination of various machine learning techniques, like neural networks in its spam filters. This kind of machine learning technique is capable of identifying spam mails as well as phishing messages, through analyzing loads of messages across the huge network of computers.
The machine learning is capable of adopting varying conditions, Gmail and yahoo spam filters do more than just keeping track of junk mails using pre-existing rules.
7. Image Recognition
Image recognition represents numeric and symbolic information from images and high dimensional data. Additionally, the process also includes data mining, machine learning, pattern recognition, etc. It may seem like many technologies and innovations are image recognition, and that’s right. The technology behind facial or image recognition, in our mobile devices, cars or be it diagnostic images in healthcare, has made huge strides in recent years. All of them use solutions that make sense of objects in front of them. Therefore, they often called “computer vision.” and these computers make exact decisions depends on what they see. More of the modern innovation in image recognition is related depends on deep learning technology, (an advanced form of machine learning) and artificial intelligence. The machine learning akes in the data, passes it through algorithms and gives a prediction. This provides us with the impression that the computer is working on its own, “thinking” and presenting conclusions.
To get the most value of machine learning, you need to know how to pair the best Artificial Intelligence and Machine Learning algorithms with the right tools and processes. Matellio offers top-quality software development services to its clients. Furthermore, we offer bespoke software development solutions to cater to the varying requirement of the clients. Our manufacturing technology solutions aim to help businesses to gain visibility across the day to day enterprise functions and enhance efficiency.
So, if you’re looking forward to building a cost-effective and reliable software development service provider, then look no further and get in touch with us to digitize your manufacturing operations.
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