A digital twin is a virtual representation of a real-world product or asset. Thanks to digital twins, manufacturers can improve their understanding of the product and allow businesses to experiment in future actions that may enhance asset performance.
Logistics hubs usually conduct manual quality inspections to inspect containers or packages for any kind of damage during transit.
Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product.
A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended.
Digital twins allow manufacturers to gain a clear view of the materials used and provide the opportunity to automate the replenishment process
Manufacturers leverage AI technology to identify potential downtime and accidents by analyzing the sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs.
Generative design uses machine learning algorithms to mimic an engineer’s approach to design. Designers or engineers enter parameters of design (such as materials, size, weight, strength, manufacturing methods, and cost constraints) into generative design software and the software provides all the possible outcomes that can be created with those parameters.
Industrial robots, also referred to as manufacturing robots, automate repetitive tasks, prevent or reduce human error to negligible rate, and shift human workers’ focus to more productive areas of the operation. Applications of robots in plants vary.
Edge analytics provides fast and decentralized insights from data sets collected from sensors on machines. Manufacturers collect and analyze data on edge to reduce time to insight.
Quality assurance is the maintenance of a desired level of quality in a service or product. Assembly lines are data-driven, interconnected and autonomous networks. These assembly lines work based on a set of parameters and algorithms that provide guidelines to produce the best possible end-products
AI powered software can help organizations optimize processes to achieve sustainable production levels. Manufacturers can prefer AI-powered process mining tools to identify and eliminate bottlenecks in the organization’s processes.
For decades, companies have been “digitizing” their plants with distributed and supervisory control systems and, in some cases, advanced process controls.
In response to strong market demand, a cement company had embarked on a throughput upgrade at the beginning of 2016.
The automated manufacturing machines use AI solutions to identify faults in the manufacturing process and notify the production team to eliminate product quality issues.
Over the past few years, venture capital firms and global companies like GVFL Ltd., Boschand Hitachi High-Tech Solutions Corporationhave invested in Indian AI start-ups serving the manufacturing industry.