The advent of Artificial Intelligence and Machine Learning is providing benefits to many fields. However, the area that is benefitting the most out of all the related fields is none other than the manufacturing sector. Many significant companies worldwide have heavily invested in machine learning technologies for their manufacturing processes and have reaped outstanding results.
With the assistance of the industrial Internet of Things, artificial intelligence has brought down labor costs and has reduced downtime. It has also increased workforce productivity and overall production speed, which has helped start the era of smart manufacturing. The numbers always show accurate results; it has been estimated that the smart manufacturing market will increase annually to almost 12.5% in the next year and the year after that.
Believing this estimate is undoubtedly easy. This is because many organizations and businesses have already surrendered themselves to machine learning’s technological advancements and cooperating with QA testing services to yield great results. Some of the examples of current implementations of machine learning are as follows.
1. General Process Improvement
When we talk about machine learning-based solutions, the first thing that always comes to mind is its use in improving daily processes that occur throughout the manufacturing cycle. This technology has helped manufacturers realize their mistakes and issues in the operations and methodologies of production they have adopted over the years, such as bottlenecks, unprofitable production methods, etc.
Companies have now focused their energy on taking a more in-depth look into inventory, assets, logistics, and supply chain management with machine learning and the industrial internet of things. This has opened their eyes to many confidential matters and opportunities for profit in the manufacturing process and the distribution and packaging.
A great example of this can be seen by the German conglomerate Siemens. They have used their neural networks to find and monitor any potential problems in their steel plants that may decrease their efficiency. Siemens has installed many sensors in its equipment. With the help of its personal cloud space, called Mindsphere, it has recorded, monitored, and analyzed each step involved in the manufacturing process. This innovation is called Industry 4.0, which is a trademark of the smart manufacturing era.
2. Product Development
The phase where machine learning is mostly implemented and great results are expected in the product development phase. This is because the planning and designing of new products and the updates and development of existing products are mostly bound by the extensive information that directly affects them and has to be considered to get the best results.
The machine learning solutions provide great solutions by gathering customer data and analyzing it to interpret customer demands, expose hidden desires and necessities, and find new business opportunities. This always results in better quality products than those already listed in the catalog and opens new revenue streams for the business. Machine learning supports this process the most, as it is excellent in evaluating risks that come with the development of new products. It also gives an excellent insight into the planning process by giving it more informed decisions.
One of the leading companies in the world, Coca Cola, is also using machine learning in product development. In fact, the launch of its current favorite product, Cherry Sprite, resulted from the decision taken by machine learning. The company set many interactive soda dispensaries where different flavors of soda were available. Customers choose their favorite flavor, and the data was recorded. With the help of machine learning, the most frequent combination was discovered by analyzing the data collected. The result was the detection of a large market that was enough to introduce a new beverage across the country.
3. Quality Control
It has been proved that if machine learning is put to fair use, it can increase the final product’s quality up to 35%, especially in the discrete manufacturing industries and businesses. There are two basic ways in which machine learning can do this. The first way is to find defects and faults in the final products and their packaging. A lot of loss is incurred due to returned and unsaleable defective goods. Machine learning can decrease this loss a lot by profoundly examining the manufactured products and helping companies to reach the market at all. It has been estimated that as compared to human inspections, machine learning technological inspections are given 90% more accurate results.
The second way is to use machine learning to increase the quality of the manufacturing
process. Businesses can use machine learning and IoT devices to analyze the performance and availability of all the equipment used in the manufacturing process of products. This helps predict the time when the maintenance is needed so that the equipment can be fixed at appropriate times to extend its life and avoid any costly downtimes.
General Electric is one of the most prominent investors in the predictive maintenance of equipment and procedures in the quality control department. This company has already worked in the machine learning sector and has created and deployed many tools and equipment throughout its business units and customers. The main branches include the aerospace, transportation industries, and power generation. Its leading working includes detecting early warning signs of damage in the manufacturing lines and providing predictions of long term behavior and the equipment’s lifetime.
The security of mobile apps, devices, and data is increasing day by day. The reason is that all the machine learning solutions rely on operating systems, applications, cloud, networks, and on-premise platforms. The manufacturers pay special attention to it because of this. Fortunately, this problem is solved by the Zero Trust Security (ZTS) framework used frequently by machine learning. With this technology’s help, the user’s access to data and information is heavily regulated and controlled.
Thus, with the help of machine learning, users can easily be monitored in how they access certain information, what applications they are using, and how they are connecting to it. Machine learning can easily delimit access to certain information, keep an eye on who uses what information, and detect disturbances that can trigger warnings.
Despite its extensive functionality, the manufacturing industry does not use Zero Trust architecture and frameworks as a standard. According to a recent survey, only 60% of the manufacturers planned to introduce Zero Trust architecture and framework in their digital landscapes.
Most of the manufacturers are joining their forces in machine learning to approach robots’ smart technology. Robots created under artificial intelligence can be used to do all the dangerous and life-risking tasks humans can’t. The new robots use their machine learning capabilities to tackle more complex and complicated processes than before, and they quickly surpass the assembly lines that they were once relegated to.
A Chinese-owned German manufacturing company, Keller Und Knappich Augsburg (KUKA for short), is planning on achieving precisely this with its industrial robots. Its main goal is the creation of robots that can work alongside humans and achieve collaboration. For this, the company is launching the ‘LBR iiwa,’ a highly sensitive HCR-compatible robot. The intelligent robot can perform highly complicated tasks with the help of its high-performance sensitive sensors alongside humans. It is also able to learn with trial and error, thus increasing its productivity.
KUKA is highly popular for using robots in its manufacturing factories. Similar to this, much other large manufacturing also uses robots. Bavarian Motor Works (BMW) is a famous auto brand and one of KUKA’s biggest customers. It is renowned for being one of the businesses that always relies on machine learning and artificial intelligence to reduce human-related errors, enhance productivity and efficiency, and add value to the entire manufacturing chain.
Some Closing Thoughts
It is evident from the above discussion that the manufacturing industry is a highly technologically advanced sector. Manufacturers have always been highly adaptable to all kinds of technologies. Manufacturers have always been the first to implement such solutions, from automation to robotics and sophisticated digital solutions. Knowing that manufacturers are already investing in machine learning solutions for all their problems is not a surprise.
The highly anticipated results are already in front of us. Some of the favorably perceived uses of machine learning in manufacturing include reduced equipment failures, increased productivity, better introduction and distribution of advanced products. And while we are still far from adopting machine learning in our technologies, many companies have already cleared our path. They are leading the way for us to use smarter manufacturing methods.