AI Case Studies
Supply Chain Management
Profet AI assists supply chain management departments in predicting demand fluctuations, optimizing supply chain plans, and reducing inventory costs. By analyzing supply chain data, it optimizes supplier selection and inventory management, enhancing supply chain efficiency.
Application Scenarios
Demand Forecasting, Price Forecasting
Last Time Buy Spare Parts Quantity Forecast
When suppliers no longer provide raw materials or spare parts (End of Life, EOL), procurement personnel must seize the last purchase opportunity by estimating future demand for the final purchase.
Pain Points Analysis:
- Difficult Inventory Management: Excessive procurement increases inventory costs, while insufficient stock can lead to material shortages and affect reputation.
- Lack of Predictive Model: Currently, the final purchase quantity for each spare part is estimated based on a fixed ratio, often leading to inaccurate stock levels.
- Inaccurate Universal Model Predictions: RMA quantity forecasts for different products and parts vary, making it difficult to use a single model for different products and parts.
Outcome Benefits:
- Improved Forecast Accuracy: Model prediction accuracy for the final purchase quantity improves by 20%, enhancing inventory management efficiency.
- Data-Driven Objective Analysis: Moves away from experience-based demand analysis to achieve data-driven objective analysis and experience transfer.
Raw Material Price Forecasting
Raw material prices significantly impact production costs, and price fluctuations directly affect profit margins. For instance, when international crude oil prices rise, petrochemical product prices also rise, increasing production costs in downstream industries such as chemicals and textiles.
Pain Points Analysis:
- Multiple Influencing Factors: Raw material prices are influenced by various factors, including supply and demand relationships, economic policies, and international situations, leading to significant price fluctuations.
- Information Acquisition Difficulty: Companies often find it challenging to obtain timely raw material price information, resulting in delayed procurement decisions.
- High Forecast Difficulty: Price fluctuations often exhibit irregularities that traditional forecasting methods struggle to accurately capture.
Outcome Benefits:
- Advance Price Trend Prediction: Helps companies adjust procurement strategies in real-time, reducing procurement costs by 12%.
- Increased Efficiency: Enhances the efficiency of procurement decisions, improving overall operational efficiency.