XGBoost Demand Forecasting for Inventory Accuracy
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Solution Overview
Problem
Existing inventory forecasting technologies are limited in utilizing all data variables, leading to inaccurate predictions, and face challenges in systematic inheritance and scalability across different regions due to reliance on time series methods and human experience-based formulas.
Innovation Solution
A demand forecasting method utilizing an XGBoost model that includes a linear model, classifier, and regression model to process historical demand data, converting data formats to consider multiple features and periods, and deconstructing the model to attribute feature influences, eliminating artificial errors and enabling accurate predictions across regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If time series method with limited variables is used for demand forecasting, then the forecasting system is simple to implement, but the prediction accuracy is insufficient due to inability to fully utilize data variables
Solution Approach 1:
The patent transforms the forecasting approach by changing from traditional time series parameters to machine learning parameters. It converts historical demand data into feature matrices with multiple variables including time features, product features, and external factors, enabling the model to fully utilize data variables for improved prediction accuracy
Solution Approach 2:
The patent replaces manual formula-based forecasting with automated machine learning models. It substitutes human experience-based formulas with trained ML models that can systematically process and inherit knowledge from historical data, eliminating the need for manual formula development when personnel change
2Reliability
If human experience-based formulas are used for demand forecasting, then the system can be implemented with available data, but systematic inheritance and transfer become difficult when personnel leave or are replaced
Solution Approach 1:
The patent creates a digital copy of human expertise by training machine learning models on historical data. The models learn and encode forecasting knowledge from past performance, creating a reusable digital asset that can be systematically transferred and inherited without depending on specific personnel. This allows organizational knowledge to be captured and replicated across different regions and teams
Solution Approach 2:
The patent develops universal forecasting models that can be applied across multiple regions and product lines. The same ML framework and trained models can serve different business units and geographic areas, enabling systematic inheritance of forecasting capabilities throughout the organization rather than requiring region-specific manual formulas
3Measurement precision
If region-specific forecasting teams are established for different regions, then localized predictions can be made for each region, but the system cannot be quickly expanded to different regions and requires multiple teams for experience accumulation
Solution Approach 1:
The patent creates a universal forecasting platform using machine learning models that can be deployed across multiple regions simultaneously. The same model framework processes local data with region-specific features, enabling consistent high-quality predictions across Europe, United States, Asia and other regions without requiring separate teams for each location
Solution Approach 2:
The patent enables the forecasting system to automatically adapt to different regions by training models on local historical data. The ML models self-learn regional demand patterns and characteristics from available data, eliminating the need for manual formula development and experience accumulation by human teams in each region
Data Source
AI summary
A demand forecasting method and a demand forecasting apparatus are provided. A preliminary prediction amount corresponding to a part number is obtained based on historical demand data. A demand probability of the part number is calculated based on the preliminary prediction amount. A prediction demand amount corresponding to the part number is obtained based on the historical demand data, the preliminary prediction amount and the demand probability.