5 Ways AL & ML Are Improving Manufacturing Process

Artificial intelligence and Machine Learning are benefiting a wide range of industries, and the manufacturing sector is the one that listed the top. Advances in technology through machine learning (ML) have provided an opportunity to accelerate innovation processes and improve decision making.
Nowadays, most of the businesses across the globe are adopting machine learning solutions in manufacturing to improvise their processes and getting better results.
According to the research report of McKinsey, around 40 per cent of the potential value that can be generated by analytics today comes from trending technologies like machine learning and artificial intelligence. In total, ML ranges from 3.5 trillion dollars to 8 trillion dollars of revenue annually.
Trendforce firm has found that smart manufacturing is in line with the rapid growth rate. The company forecasts the market of smart manufacturing to reach $ 320 billion by the year 2020, with a compound annual growth rate of 12.5%.
According to Allied Market Research, the global market of Artificial Intelligence in Manufacturing Industry was $513 million in 2015 and is forecasted to reach $15,273 million by 2025, growing at 55.2% CAGR.
Global AI Market technological trend
In our previous blog, we have clearly discussed the importance of AI in manufacturing and listed top 10 applications of Artificial Intelligence in the manufacturing sector.
Click here to learn the Top 10 Use Cases of AI in Manufacturing Sector
Today, in this post, we will be especially discussing how machine learning and AI are making the manufacturing process simpler and easier.
#1. Manufacturing Process Improvement
Manufacturing is the one industry that is greatly benefiting with ML. Using this machine learning technology; manufacturers will be able to identify all sorts of problems on their common production methods, from obstacles to non-profit production lines.
By integrating the ML-based tool with the Industrial IoTs‌, organizations are taking an in-depth look at their inventory management, assets, logistics, and supply chain management. It brings high-value insights that explore potential opportunities not only in the manufacturing process but also in product packaging and distribution.
Example:
A great example of this can be seen in the German Conglomerate Siemens, which uses neural networks to observe its steel plants for potential problems affecting its efficiency. By just installing a combination of sensors in its devices, and with the help of Mindsphere (its own smart cloud), Siemens can monitor, record and analyze each and every step that is involved in the production process. This is also called Industry 4.0, a trademark of the era of smarter manufacturing.
#2. Product development
The product development phase is obviously the most widely accepted uses of machine learning. The planning and designing phase of new items and the improvement of existing products are tied to the data that needs to be considered to give the best outcomes.
Therefore, Machine learning solutions help to collect customer real time data, analyze and also identify new business opportunities. All of these end up with good products from an existing catalogue and new products that can reveal new revenue streams for the organization.
Example:
Coca Cola is using machine learning tool for the development of products. In fact, Cherry Sprite’s experiment was the result of Coco Cola’s use of Machine Learning.
This soft drinks supplier uses interactive soda fountain dispensaries, where consumers can add a variety of flavours to the catalogue drinks. The company gathered the total amount of data and ML to detect the most often combinations.
#3. Security
As all the ML solutions are highly depending on the operating systems, cloud and on-premise platforms, apps, and networks, the safety and security of mobile devices, apps, and data being is needed for smart manufacturers.
Thankfully, here is an answer to machine learning technology in the ZTS framework (Zero Trust Security). Due to this technology, user-access to valuable information and digital access is heavily limited and regulated. Therefore, machine learning models helps greatly in accessing their protected data, what applications they are using and how they are connecting to it.
Unfortunately, the use of frameworks and zero trust architectures is not strictly standard for the manufacturing sector. In a recent survey, only 60% said they plan to introduce zero-trust policies in their digital landscapes.
#4. Quality Control
ML technology can improve final product quality by up to 35%, particularly in private manufacturing fields. There are two ways ML can do this. First, detect irregularities in the items and their packaging.
Through in-depth scrutiny of manufactured products, organizations can prevent defective products from ever entering the market. In fact, some studies talk about up to 90% development in error detection compared to human inspections.
And then there is the possibility of increasing the quality of the entire process of manufacturing. Through ML apps or IoT devices, companies can analyze the performance and availability of all devices used in the manufacturing process. It will allow maintenance hazard management to forecast the best time for specific equipment to extend its life and avoid costly time intervals.
Example:
General Electric is one of the leading electric investors in the field of quality control, particularly in everything related to appraisal management. In addition, it has created and implemented its Machine Learning-based tools in over a million assets across its customers and business units, including power generation, the aerospace, and transportation industries.
Its system identifies early warning signs of disorders in its productive pathways and to provide prognostics with long-term expectations of behavior and life.
#5. Robots
Finally, there are some well-known partners for manufacturers who are smart with ML: Robots. Industrial AI usage in robots enables humans to perform dangerous or complex simple tasks. These latest robots have surpassed assembly ranges because their ML capabilities enable them to solve more complex processes than ever before.
Example:
This is exactly what KUKA, a German manufacturer in China, is targeting with its industrial robots. Its aim is to develop robots that can work with humans and act as their partners. And, in that way, the firm is folding its robot named the “LBR Iva”.
This intelligent robot is instilled with high level-performance sensors that enable to perform complicated tasks while working beside humans and know how to improve their manufacturing productivity.
KUKA Company uses AI robots in its factory. Well-known auto brand BMW is one of its largest customers and one of the businesses that have already discovered that robots can minimize human error, improve productivity and raise value across the entire production chain.
Wrapping Up
I would say that the manufacturing industry is a technologically advanced sector. For decades, manufacturing companies initially embraced all the technologies available, ranging from sophisticated digital solutions to robotics and automation. So, it is no wonder that manufacturers across the globe are already heavily investing in machine learning algorithms to improve their processes.
Reduced equipment failures, increased productivity, improved distribution, etc. are some of the major advantages of using machine learning technology in the manufacturing industry. While we are far from embracing these solutions widely, the path has already been paved, and many businesses are leading the smart way of products manufacturing.
Take Advantage of Machine learning to reap the benefits from the smart manufacturing factory
USM Business Systems is moving towards delivering innovative and next-generation ML solutions to create a manufacturing industry smarter. From design and development to production and distribution, we build an ML strategy for your smooth journey.
If you have any queries, please do not hesitate to Contact us.
 
 

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