Inventory Control System Using Synthetic Demand Matrices
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Solution Overview
Problem
Inventory control for items with highly intermittent demand poses challenges due to limited historical data points, leading to unreliable statistical analysis and potential stock shortages or overstocking, especially in industries like aircraft component manufacturing, where high costs and customer inconvenience are associated with inventory unavailability.
Innovation Solution
An inventory control system that generates an initial demand matrix and synthetic demand matrices using historical data, identifies sparse demand vectors, and modifies these matrices to produce filtered synthetic demand matrices, enabling the estimation of demand for target periods and efficient acquisition of inventory items through parallel processing and demand signal generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis of historical demand data is used to forecast demand, then demand estimation can be performed for items with sufficient historical data, but reliable analysis cannot be performed for items with highly intermittent demand due to insufficient data points
Solution Approach 1:
The patent creates synthetic demand matrices that are copies of the initial demand matrix, randomly permuted to generate alternative historical scenarios. This allows the system to perform statistical analysis on multiple synthetic datasets, effectively copying and transforming limited historical data into multiple analytical opportunities, thereby improving demand estimation accuracy for intermittent demand items.
Solution Approach 2:
The patent performs preliminary actions by generating synthetic demand matrices before final demand estimation. These synthetic matrices are created in advance through random permutation of historical data, allowing the system to pre-compute multiple possible demand scenarios and select the most reliable estimates, thus preparing accurate demand forecasts before actual inventory decisions are made.
2Reliability
If inventory is maintained for expensive items with intermittent demand, then customer service level is improved, but manufacturing or purchasing expenses increase significantly
Solution Approach 1:
The patent changes the parameter of demand estimation by using synthetic demand matrices with randomly permuted values, allowing the system to identify sparse demand patterns that traditional methods miss. This parameter change in the analytical approach enables more accurate identification of when inventory is truly needed, optimizing the balance between availability and holding costs.
Solution Approach 2:
The patent replaces traditional mechanical statistical analysis with a computational approach using synthetic data generation and random permutation. This substitution allows the system to handle intermittent demand patterns that traditional statistical methods cannot reliably analyze, enabling better inventory decisions for expensive items with sparse demand history.
3Measurement precision
If demand forecasting is performed for each inventory item individually, then accurate item-level estimates can be achieved, but processing time and resource utilization increase
Solution Approach 1:
The patent merges the processing of multiple inventory items by generating synthetic demand matrices that can be applied across entire item catalogs simultaneously. Instead of processing each item individually, the system creates synthetic scenarios that cover all items, then applies sparse demand identification across the entire dataset, significantly improving processing efficiency while maintaining item-level accuracy.
Solution Approach 2:
The patent creates a universal synthetic demand matrix generation process that serves multiple inventory items simultaneously. The same synthetic matrix generation and sparse demand identification methodology is applied universally across all items, allowing the system to maintain consistent demand estimation accuracy for each item while achieving efficient batch processing through the multi-functional synthetic analysis approach.
Data Source
AI summary
An inventory control system is configured to perform operations including generating an initial demand matrix and generating a plurality of synthetic demand matrices. The operations also include identifying sparse demand vectors in the synthetic demand matrices, where each sparse demand vector represents synthetic demand that satisfies a sparse demand criteria. The operations also include modifying synthetic demand values based on the sparse demand vectors to generate filtered synthetic demand matrices. The operations include generating estimated demand for a target period for each inventory item based on the filtered synthetic demand matrices and the initial demand matrix and comparing the estimated demand to inventory data to determine whether one or more inventory items should be acquired. The operations include generating and sending a demand signal to cause the one or more inventory items to be acquired.