Manufacturers who continue to invest in analytics and AI-driven pilots see the pandemics’ hard reset as an opportunity to become stronger, more resilient, more resourceful. They’re developing new methods to differentiate themselves while driving down costs and safeguarding margins by combining human experience, insight, and AI techniques. And they’re all up for the task of maintaining growth in difficult times. They aren’t the only ones who have accepted the challenge.
Manufacturing’s mission is to produce consistently high quality at the lowest possible cost and with the quickest possible turnaround time. As a result, the most challenging problems are delivering consistently high-quality goods while keeping prices down and producing at a quick pace. Here are some examples of how AI in manufacturing may assist:
Manufacturing’s Real-Time Future Requires AI
Many advantages of real-time monitoring include fixing production bottlenecks, checking scrap rates, fulfilling client delivery deadlines, and more. It’s a fantastic source of contextually relevant data for machine learning models to learn from. Both supervised and unsupervised, machine learning algorithms can understand real-time data from numerous production shifts in seconds and uncover previously undiscovered processes, products, and workflow patterns.
Strengthening Human Capabilities
Artificial intelligence’s ultimate objective is to make processes more efficient — not by replacing humans but by filling the gaps in their abilities. People and industrial robots can work together to make work less laborious, boring, and repetitive while improving accuracy and efficiency. To assess the integrity of each item and its internal structure, the program uses industrial radiography (X-ray) and photographs to inspect manufacturing components. The examination procedure can be exceedingly laborious and error-prone when just a specialized specialist is present. Assisted Defect Recognition, on the other hand, uses computer vision and machine learning to analyze images of inspected parts, identify potential defects (including those that the human eye might miss), and learn and improve the technology’s accuracy based on human acceptance or corrections of the results.
Preventative Maintenance should be enabled.
According to a Capgemini report, maintenance accounts for over 30% of AI use cases in manufacturing. This makes sense, given that the biggest value from AI may be achieved in manufacturing by employing it for predictive maintenance (estimated to be worth $0.5 trillion to $0.7 trillion globally).
Predictive maintenance examines machine performance data in the past to predict when they are likely to break, reduce the amount of time they are out of service, and pinpoint the core cause of the problem. Yield-energy-throughput (YET) analytics may be utilized to guarantee that those specific machines are as efficient as possible while in operation, allowing them to enhance yields and throughput while consuming less energy.
How AI asset optimizers helped a cement business
A cement manufacturer began a throughput improvement at the beginning of 2016 in response to increasing market demand. Hardware improvements increased fee rates by 8% while implementing an off-the-shelf sophisticated process-control solution from an equipment vendor increased fee rates by another 2%. The customer, on the other hand, desired to shift the needle even more.