Shipment Prediction System Using Change Ratio Scale
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Solution Overview
Problem
Current methods for estimating shipment standard volume at the end of the month lack a scientific measurement and monitoring mechanism, leading to inaccurate predictions, which affects raw material purchasing, manufacturing capacity, and inventory turnover efficiency, resulting in increased inventory costs and reduced profitability.
Innovation Solution
A method and electronic apparatus for predictive value decision that calculates shipment predictive values based on historical data using various estimation methods, including weight estimation, ratio estimation, and moving scale methods, providing predictive performance information to stabilize shipment predictions and adjust production and shipment progress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shipment standard volume is estimated based on experience and historical data without scientific measurement, then the estimation process is simple and quick, but the accuracy and reliability of the prediction is low
Solution Approach 1:
The system performs preliminary calculations by establishing multiple prediction models (first, second, and third prediction values) before the actual shipment date. These models use historical data, moving averages, and trend analysis to predict shipment volumes in advance, allowing businesses to prepare accordingly rather than waiting for actual shipment data
Solution Approach 2:
The system incorporates feedback mechanisms by comparing actual shipment data with predicted values, calculating prediction accuracy, and using this feedback to continuously improve future predictions. The accuracy calculation module provides feedback on model performance, enabling iterative optimization of the prediction system
2Reliability
If high inventory quantity is maintained to reduce the risk of losing sales opportunities, then the risk of lost sales is reduced, but the inventory cost increases and inventory turnover efficiency decreases
Solution Approach 1:
The system enables preliminary action by providing accurate shipment predictions before the actual shipment occurs. This allows businesses to determine optimal inventory levels in advance based on predicted demand, rather than maintaining high inventory as a precaution. The prediction models calculate expected shipment volumes, enabling precise inventory planning that reduces both stockouts and excess inventory
Solution Approach 2:
The system dynamically adjusts inventory parameters based on prediction results. Instead of using fixed high inventory levels, the system varies inventory quantities according to predicted shipment demands, transforming the inventory management approach from static and conservative to dynamic and demand-driven
3Productivity
If shipment standard volume is determined without real-time monitoring mechanism, then the determination process is simple, but the ability to control and adjust production and shipment progress is weak
Solution Approach 1:
The system implements real-time monitoring through feedback mechanisms that track actual shipment progress against predicted values. The comparison module continuously monitors whether actual shipments meet prediction targets, and the accuracy calculation provides ongoing feedback on model performance, enabling timely adjustments to production and shipment plans
Solution Approach 2:
The system enables preliminary control actions by providing prediction results before shipment occurs. This allows businesses to proactively adjust production schedules, allocate resources, and plan logistics based on predicted shipment volumes, rather than reacting to actual performance after the fact
Data Source
AI summary
A method and an electronic apparatus for predictive value decision and a computer-readable recording medium thereof are provided. First, a model operation interface is activated, and in response to receiving an operation through the model operation interface, the following steps are executed. A shipment predictive value at a target time point is calculated based on historical shipment data. Next, a change ratio scale corresponding to the target time point is calculated using the shipment predictive value corresponding to the target time point and multiple previous shipment predictive values at multiple time points before the target time point. Moreover, an average value of past change ratio scales corresponding to the target time point is calculated based on the historical shipment data. Finally, predictive performance information is provided based on the average value of the past change ratio scales and the change ratio scale corresponding to the target time point.