Artificial Intelligence Scheduling Functions
Trial Calculation of Remaining Production Capacity
IMPACTs will consider the currently unused capacity and existing inventory materials and generate a suggested combination of production and sales products.
- Consume surplus capacity and materials, and convert them into revenue.
- Increase extra production throughput and product output with minimal cost.
Capacity Balance Optimization
According to demand, the AI module will calculate whether resources need to be adjusted, increasing equipment with insufficient capacity, reducing overcapacity, and simulating the utilization rate under different product portfolio scenarios.
- Maximize the capacity utilization of the production line according to the product mix
- Fully utilize the bottleneck equipment’s capacity, and the non-bottleneck resources follow the production pace.
Production Period Optimization
According to the production line's capacity, try to calculate the production cycle time of different releasing times and quantities. Further, recommend the MO releasing time and quantity according to the order due date.
- According to the order due date, the Ai model will recommend MO release time and quantity.
- The waiting time of work-in-process (WIP) on the production line is reduced effectively.
Resource Failure Period Prediction
Use historical equipment production data to train the machine learning model and use the AI model to estimate the equipment's remaining lifetime. AI model will schedule maintenance orders before failure occurs.
- Equipment failure period prediction
- Reduce the risk of equipment capacity impact due to unexpected downtime.
Maintenance Impact Analysis
Analyze the factors affecting maintenance and repair events, and find out the main reasons for frequent maintenance. For instance, the AI model will schedule regular production before scheduling maintenance if a specific process is prone to damage to the equipment.
- Cause analysis of equipment failure.
According to the machines' historical failure data, the AI model will predict the latest maintenance time limit and generate the maintenance schedule before reaching the time limit.
- The maintenance schedule will have the least impact on the production line.
- Optimized maintenance schedule with minimal impact on productivity.