Machine Learning Inventory Forecasting Across Geographic Granularities
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Solution Overview
Problem
Conventional inventory management approaches face challenges such as overprovisioning and low forecast accuracy due to the inability to handle sporadic demand and lack of demand history, especially across multiple geographic locations.
Innovation Solution
The use of machine learning techniques to generate composite inventory-related forecasts by processing historical data across multiple geographic granularities, combining statistical forecasts with calculated weights, and performing automated actions based on these forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional inventory management uses the same forecast across multiple locations, then inventory planning is simplified, but forecast accuracy deteriorates and overprovisioning increases
Solution Approach 1:
The patent segments the inventory forecasting system by geographic granularity, creating separate forecasts for different locations rather than using a single unified forecast. The machine learning model processes data at multiple geographic levels (enterprise, region, location) to generate location-specific forecasts, thereby improving forecast accuracy while maintaining manageable complexity through automated processing.
2Device complexity
If conventional inventory management uses single forecast approach, then processing is simpler, but ability to handle sporadic demand and limited demand history deteriorates
Solution Approach 1:
The patent changes the parameters used in forecasting by incorporating multiple geographic granularity levels (enterprise-level, region-level, and location-level data) and using machine learning models that can adapt to sporadic demand patterns. This allows the system to reliably predict demand even when historical data is limited, as the model learns from patterns across different geographic scales.
3Measurement precision
If machine learning model processes data at multiple geographic granularities, then forecast accuracy improves, but computational complexity increases
Solution Approach 1:
The computational process is segmented into multiple processing stages corresponding to different geographic granularities. The machine learning model processes data at enterprise, region, and location levels in a structured manner, breaking down the complex computational task into manageable segments that can be processed efficiently while maintaining high forecast accuracy.
4Measurement precision
If machine learning model combines multiple statistical forecasts with calculated weights, then forecast precision improves, but system complexity increases
Solution Approach 1:
The machine learning model uses feedback mechanisms to calculate optimal weights for combining multiple statistical forecasts. The model processes historical forecast data and demand outcomes to learn the relative reliability of different forecast methods, automatically adjusting weights to maximize forecast precision while the automated nature of this process keeps system complexity manageable.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automatically generating inventory-related information forecasts using machine learning techniques are provided herein. An example computer-implemented method includes training a machine learning model for calculating weights for multiple statistical forecasts for inventory-related data by processing historical data pertaining to at least a portion of the multiple statistical forecasts; generating two or more statistical forecasts for inventory-related data associated with an enterprise by processing data pertaining to multiple system parts across multiple geographic granularities associated with the enterprise; calculating weights for the generated statistical forecasts by processing data associated with at least a portion of the generated statistical forecasts using the trained machine learning model; generating at least one composite inventory-related forecast by combining the generated statistical forecasts in accordance with the calculated weights; and performing automated actions based on the at least one composite inventory-related forecast.