Demand Forecasting with Synthetic Data for Sparse Item Histories
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Solution Overview
Problem
Existing demand forecasting systems face challenges such as scarce data availability, flawed training data, difficulty in identifying appropriate models for items, and challenges in forecasting demand across different channels and item catalogs, especially for enterprises with numerous items and dynamic inventory conditions.
Innovation Solution
A demand forecasting system utilizing computer models that incorporate synthetic data generation, feature management, hyperparameter tuning, and model deployment systems to improve forecasting accuracy and adapt to changing item catalogs and channels, including real-time inventory adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demand forecasting models are used with limited historical data, then model training is simpler, but forecasting accuracy deteriorates due to data scarcity
Solution Approach 1:
The patent introduces synthetic data as an intermediary between limited historical data and model training requirements. The system generates synthetic training data that mimics real demand patterns, allowing models to be trained adequately even when actual historical data is scarce. This intermediary synthetic data bridge enables accurate forecasting without requiring large volumes of real historical data.
Solution Approach 2:
The system changes the parameter of data quantity by generating synthetic data samples that expand the training dataset. By transforming the state of available data from limited to sufficient through synthetic generation, the model can achieve accurate forecasting performance even when original historical data is insufficient.
2Reliability
If multiple forecasting models are evaluated for different items, then model appropriateness improves, but system complexity increases
Solution Approach 1:
The system implements self-service through automated model selection and evaluation. The framework automatically evaluates multiple forecasting models against different items and their characteristics, then selects the most appropriate model without requiring manual intervention. This automation maintains high model appropriateness while reducing the operational complexity of managing multiple models.
Solution Approach 2:
The system dynamically adapts model selection based on item characteristics and data availability. Rather than using a static model for all items, the framework flexibly evaluates and selects different models suited to different item types, making the system adaptable and reliable while managing complexity through dynamic rather than rigid structures.
3Adaptability or versatility
If forecasting is performed across hundreds or thousands of items, then enterprise coverage improves, but computational challenges increase
Solution Approach 1:
The patent segments the enterprise-wide forecasting problem into item-level independent forecasting tasks. By dividing the large-scale problem of forecasting across hundreds or thousands of items into smaller, manageable unit-level forecasts, the system can process each item independently using appropriate models and synthetic data, reducing overall computational complexity while maintaining comprehensive enterprise coverage.
Solution Approach 2:
The system implements a universal forecasting framework that handles diverse item types across the entire enterprise through a common architecture. The synthetic data generation and model selection mechanisms work universally across all items, providing enterprise-wide coverage while avoiding the need for separate complex systems for different item categories.
4Quantity of substance
If synthetic data is generated to supplement training data, then data availability improves, but data quality control becomes more challenging
Solution Approach 1:
The system implements feedback mechanisms where the quality and characteristics of generated synthetic data are continuously evaluated and adjusted. The synthetic data generation process incorporates feedback from actual demand patterns and model performance, ensuring that generated data maintains high quality and accurately reflects real demand characteristics while supplementing the training dataset.
Data Source
AI summary
Aspects of the present disclosure relate to a demand forecasting system. The demand forecasting system may include components for developing forecasting models, generating demand forecasts, and handling outputs of demand forecasting models. In some embodiments, the demand forecasting system may include a model training system and one or more components that can be used by the model training system to improve model performance.