Artificial Intelligence (AI) is most commonly applied in manufacturing to improve overall equipment efficiency (OEE) and first-pass yield in production. Over time, manufacturers can use AI to increase uptime and improve quality and consistency, which allows for better forecasting.
Predictive maintenance to reduce unplanned downtime. Optimize strategies to improve revenue and reduce cost to enhance productivity.
With AI, engineers can find the optimized process recipe for different products. Questions like ‘What conveyor speed or temperature should I input for the highest yield?’ or ‘What machine should I use for this high pitch emerging technology circuit board?’.
manufacturers can define the optimized supply chain solution for all their products. Questions like ‘How many resistors should be ordered for the next quarter?’ or ‘What's the best shipping route for product A’
Collecting and integrating data from sensors and equipment in factories Real-time tracking and monitoring of the shop floor and measuring their performance against set benchmarks Using predictive analytics to identify, predict, and prevent IT service issues as well as to perform accurate capacity planning Using big data analytics to track and improve resource utilization as well as infrastructure performance on the cloud
AI look into energy consumption of a production operation can significantly reduce operations costs. Reduced cost can allocate more funding for process improvement resources which can lead to higher yield and quality.
Uncover issues that drive both dissatisfaction and churn With AI powered Preemptive client engagement. Firms can identify clients at high risk of attrition by learning from examples of clients that have closed or moved accounts in the past.
Predicting yield can better prepare supply chain and inventory management on future component needs. Knowing if yield will be lower than expected can alert production management to increase production time to meet demand needs.
Demand forecasting can further help manufacturers take action to stock up their warehouses in advance and keep up with the customer demand without enormous transportation costs.
AI-driven cybersecurity systems and risk detection mechanisms can help secure production facilities and mitigate threats. Using self-learning AI, manufacturers can spot attacks across cloud services and IoT devices and interrupt them in seconds, with surgical precision.