Shipment Prediction Method Selection Under Compute and Time Constraints
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Solution Overview
Problem
Existing logistics prediction systems face challenges in efficiently predicting shipment volumes of a large number of products within limited calculation resources and time periods, and they often fail to accurately determine product characteristics specific to shipment prediction.
Innovation Solution
A logistics prediction system that integrates multiple prediction methods, selects appropriate methods based on product characteristics and resource constraints, and integrates results using an integrator to achieve high accuracy in shipment volume prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple prediction methods are used to predict shipment volumes of a large number of products, then prediction accuracy is improved, but calculation time and resource consumption increase significantly
Solution Approach 1:
The patent segments the large set of products into multiple groups based on their characteristics (e.g., product category, shipment patterns, seasonal variations). Different prediction methods are assigned to different product groups, allowing the system to use computationally intensive methods only where necessary while using simpler methods for other products, thus reducing overall calculation time while maintaining accuracy for critical products.
Solution Approach 2:
The patent applies different prediction methods to different products based on their specific characteristics rather than using a uniform approach. For example, products with high variability or strategic importance receive more sophisticated prediction methods, while stable, low-value products use simpler methods. This localized approach optimizes the balance between accuracy and computational resources.
2Measurement precision
If multiple prediction methods are used to predict shipment volumes of a large number of products, then prediction accuracy is improved, but calculation resource consumption increases
Solution Approach 1:
The system segments products into groups and assigns prediction methods based on resource constraints. High-value or high-variability product groups receive resource-intensive prediction methods, while other groups use lighter methods, optimizing the distribution of computational resources to achieve overall accuracy without exhausting resources.
Solution Approach 2:
The patent dynamically adjusts prediction parameters such as the number of historical data points to analyze, the complexity of statistical models, and the level of detail in forecasting based on available calculation resources. When resources are limited, the system reduces parameter complexity; when resources are abundant, it increases detail and model sophistication.
3Measurement precision
If product characteristics specific to shipment prediction are determined, then prediction accuracy for logistics is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary analysis to automatically determine product characteristics such as shipment seasonality, product category, and historical patterns before selecting prediction methods. By pre-processing and categorizing products based on their characteristics, the system reduces the complexity of the main prediction process while maintaining high accuracy through characteristic-based method selection.
Data Source
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AI summary
A logistics prediction system includes an arithmetic device that executes a predetermined process, a storage device connected to the arithmetic device, a shipment information input section to which past shipment information is input, a constraint input section to which a calculation resource and a calculation time period are input as constraints for prediction, an integration prediction database holding shipment trends of products, a prediction method selector that selects a prediction method to be used to predict a shipment volume for each of the products from among multiple prediction methods so that the constraints input to the constraint input section are satisfied, multiple prediction method sections that predict shipment volumes using the prediction methods, an integrator that integrates the results of the prediction by the prediction method sections for each of the products, and a shipment prediction output section that outputs the result of the integration by the integrator.