IoT & AI are building a new world of opportunities for the manufacturing companies to keep growing during these tough economic times and optimize business operations. Internet of things (IoT) and Artificial Intelligence (AI) is transforming the traditional manufacturing industry with JDE to change the way how products are made and making companies more efficient.
More Safety for Human Operators
IoT can predict dangerous operating conditions and improve the safety of the operators. The IoT sensors can analyze sound frequencies, vibrations, and temperature of a machine to check if it is working in the right conditions. It collects and analyzes data that allows knowing where the issue with a device is and predict if a machine is going to break down before it happens. Check out data science course in Bangalore for further information
It also tells when a machine is going to need maintenance before it is taking out of service. The results are outstanding: less machine downtime which improves the factory’s capacity saves money, creates a safer environment for workers.
This prevents any dangerous conditions and warns the workers in case of an emergency or evacuation and it can increase the factory’s efficiency because when condition monitoring is done by humans, it can be more time-consuming.
Real-Time Product Quality Inspection
AI-based image recognition software completes product quality inspection thanks to image recognition systems that click on images of incoming inventory, the AI system has a collection of thousands of images of the parts and compares them with the actual images of the incoming stock.
In addition, the network is able to find defective ones and separates them from the rest, those parts are discarded or sent for correction. This system can be tailored for specific products and materials.
The AI product quality inspection system improves the speed of inspection workflow compared to the manual one. In a matter of hours, the company can know the quality of their incoming parts. When these results are uploaded in real-time.
It can significantly reduce the risk for potential fraud because of the insights that are shared on the performance dashboard were other brands, retailers, suppliers, and factories involved in share production strategies. Companies will manage end-product quality better due to the increased accuracy of inspection.
Location Tracking
Manual location is time-consuming, but IoT is drastically reducing that time thanks to location tracking, which can tell the workers where an item is located so they can easily reach them. It works by using a location sensing device, which is a hardware device built only for the purpose of providing a relative location; this device can broadcast the location to a remote system. It offers real-time tracking and historic sites for all the devices.
There is no longer a need to make excel sheets or manual databases to manage supplies, which eliminates the risk of human errors. Operators will see what it is going in real-time and ensure the quick dispatch and arrival of products and materials.
Machine Learning-based Demand Planning and Forecasting
This system is trained to use data from historical sales and third party data such as social media. It makes predictions for future consumer demands, this system has proven to be highly accurate, and it can be adjusted on a data science workflow.
This workflow allows any machine learning forecasting solution can be implemented quickly and analyzed, the best part is that all of the available data such as prices and discounts, distribution networks, social media.
Even weather information can be matched to the SKU-level forecasting at the point of sale or distribution. The huge variety of data arrives at high speed, improving the accuracy of forecasting.
Machine Learning to Monitor an Assembly Line Process
The manual monitoring of assembly processes is an exhausting work that takes a lot of time, and of course, there is a risk of human errors. Machine learning allows algorithms to visually inspect the assembly line and identify flaws quicker.
If the defects are identified earlier in the process, this can lead to less product waste and save the company money. If there are any inconsistencies in the process, the machine will alert the operators so they can correct the issue before it causes a bigger problem. This will improve the factory’s efficiency and help the manufacturers increase the quality of their products. Check out data science courses in Hyderabad to learn more about it.
Infographic provided by PACK’R, a food filling machine company
