How Manufacturing Industry is Improving Productivity with AI in 2020?
Trust me, almost all the industry today is becoming artificially intelligent. That’s what she said 😜😜😜. (purely pun intended)
Airlines, banking, education, eCommerce, logistics, and now it is manufacturing. This sector has tremendously been using cyber-physical systems to make production more effective.
Why they are heavily investing in AI?
The reason is pretty simple. AI can enable systems to minimize downtime, optimize asset utilization, and predict failures faster. So more and more offshore product engineering companies are helping the manufacturing industry to grow every day, by driving their journey towards building a smarter factory/workshop.
So, how things changed with AI in Manufacturing Industry?
The modern manufacturing hub may look much similar as it has been for ages. However, if you take a close look, you will observe the quiet revolution which is underway. The manufacturing industry is digitally transforming — big or small — they have started to use sensors to gather data and information at every stage of their manufacturing processes in the pants or workshops.
This has made the manufacturing hub owners gather massive data. Humans cannot analyze such a huge amount of data and therefore artificial intelligence has a huge role to play. The machine learning techniques can recognize various data patterns from the structured and unstructured data sets which are complex for the human to identify.
As per a global report from McKinsey, smart factories now have the potential to generate around $3.7 trillion by 2025.
With a smart factory with AI or machine learning stuff, make the production move at an exceptional speed, reducing costs and enhancing customer experience. With the use of digital transformation, enterprises can stop the machine downtime predicting failures, manage inventory keeping a track of the stocks, predicts the delivery time, and work towards delivery of the highest quality products with the help of artificial intelligence.
Let’s dig deep into how offshore product engineering companies are helping the manufacturing industries to achieve their dream.
#1. Keeping a Tab on Quality Using Digital Vision
With the use of a computer or digital vision helps to observe the manufacturing process and identify errors such as cracks in manufacturing equipment, improper machine movement, or any microscopic defects that might arise during the production.
No doubt this super helpful, like those 3D printers using high-resolution cameras to record the printing process layer by layer. Even keep a track of pits, streaks, divots, and other patterns that are not visible to the human eye. Comes with proper alignment, dimensions, and measurement details, to make sure that the product is of the proper dimensions. AI can learn from the videos about the printing processes and detect the flaws in the production line.
#2. Takes Care of Generative Designs
AI is playing a pivotal role in manufacturing companies design process, especially the generative designs. This is no doubt an iterative process that involves feeding detailed design information as input towards the AI algorithm. Such information might cover several parameters like production methods, material types, time, and budget constraints. By taking all these into considerations, the algorithm will be able to explore every possible combination of a solution and deliver a set of the most suitable solutions.
The product designer can also set a minimum and maximum limit to make sure that the algorithm generates value. The output will provide the proposed solutions which can be further tested with the use of machine learning to gain proper insights on design to meet various expectations in a manufacturing process.
#3. Integration & Optimization in the Assembly Line
When it comes to the manufacturing industry, you’ll notice a number of equipment that sends a wide array of data to the cloud. These different types of data do not work cohesively in the cloud but help to derive business insights. One might need a dozen of dashboards and a team of SMEs to get a whole picture of the manufacturing operations. The integrated applications can push data from IoT based tools to the ecosystem of your manufacturing unit to ensure that you get a bird-eye view of all the processes.
Implementing AI and IoT in your ecosystem on top of data insights can help to automate the assembly line. Say when one equipment in the assembly line is improper and the supervisor gets notified. In such a scene, the system prepares a contingency plan and reorganizes the activities.
#4. Looking After Predictive Maintenance
The use of reactive maintenance strategy has been huge in the manufacturing industry from repairing machines to keep a tab on the flaws. The industry has moved to the employ of preventive maintenance where the machine is prepared as the schedule while considering the previous failures. With the advent of AI, manufacturers are now able to eliminate machine failures using predictive maintenance.
You can also be fed the asset utilization data to the machine learning stuff to predict the time of potential failure. This approach helps to fix the problems before it occurs creating an uninterrupted production line. This approach works better that reactive and preventive maintenance as it helps to maximize asset life and utilization
#5. Use of Digital Twin Technology
The manufacturing companies can use the digital twin technology to create a virtual representation that is able to replicate the physical attributes of the factory plant, products, or machine components. By leveraging cameras, sensors, or used any techniques to collect data, the digital twin can reflect the real-time information that pertains to the real world.
As per the latest findings in Gartner, “By 2021, half of the large industrial companies will use digital twins, resulting in those organizations gaining a 10% improvement in its effectiveness.”
Combining the digital and physical world allows us to monitor factory plants and analyze data proactively to manage problems. With the use of digital twins help in decision-making to allow manufacturers to test various scenarios to improve asset performance and predict machine failures.
With the help of a digital twin, an organization can understand their products by visualizing how a product performs in its factory environment when used by the workers in real-time.
Wrap Up
As a part of machine learning services at Blue Copper Technology, they offered customized solutions for manufacturers to automate the factories and make the operations more efficient to keep the cost low.
If your manufacturing unit wants a digital transformation, get in touch with us, or hire our certified product engineers.
