AI Case Studies
Customer Services
Profet AI AutoML assists customer service departments in conducting data-driven objective analyses to improve customer satisfaction and reduce economic losses.
Application Scenarios
Demand Forecasting, Anomaly Detection
Spare Parts Demand Prediction
In electronic device sales, RMA (Return Merchandise Authorization) repair and replacement services are usually provided. When a product is returned and found to require repair or part replacement, predicting the demand for spare parts becomes inevitable and challenging.
Pain Points Analysis:
- Inventory Management Issues: Excess stock increases inventory costs, while insufficient stock leads to additional costs and customer complaints.
- No Predictive Model: Current methods use fixed ratios to estimate spare parts quantities, often leading to inaccurate predictions.
- Inaccurate General Models: Different products and parts have varying RMA quantities, making a single predictive model ineffective.
Outcome Benefits:
- Increased Accuracy: Predictive model accuracy for RMA quantities improves by 20%, enhancing inventory management efficiency.
- Objective Analysis: Helps companies use AI to predict RMA quantities, moving away from experience-based analysis to data-driven, objective analysis and knowledge transfer.
Machine Anomaly Detection
When a machine malfunctions, equipment providers must send technicians for on-site inspection and repair. However, machine failures often occur without warning, making it difficult to schedule maintenance in advance.
Pain Points Analysis:
- No Advance Scheduling: Unexpected machine failures make it challenging to plan maintenance schedules and personnel, leading to inefficient use of maintenance resources.
- Production Interruptions: Machine failures cause production line stoppages, affecting production schedules and potentially delaying customer orders, resulting in economic losses.
Outcome Benefits:
- Efficient Resource Planning: Using predictive models to better plan technicians’ work schedules, improving human resource utilization by 15%.
- Reduced Downtime: Predictive maintenance reduces unexpected downtime, enhancing continuous production line operation.