Inventory Allocation Optimization via Feature Segmentation
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Solution Overview
Problem
The complexity of managing inventory for items with multiple configurations poses challenges in forecasting demand, as various feature combinations complicate determining which features contribute to sales and affect parts supply chains, leading to difficulties in maintaining a balanced, fast-turning, and diverse inventory.
Innovation Solution
An order generation system utilizing a feature selection subsystem that projects sales and inventory based on reported data, performs optimization to determine feature allocations, and recommends configurations to balance inventory mix rates, employing neural networking technology to calculate inventory turn rates and maximize the goodness of configurations, while considering constraints like production and product definition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If items are produced in multiple configurations with many different combinations of features, then product versatility and customer choice are improved, but forecasting accuracy and inventory management complexity worsen
Solution Approach 1:
The patent segments the complex inventory management problem by analyzing individual feature contributions separately. The system breaks down configurations into discrete features and uses statistical analysis to determine the impact of each feature on sales independently, then recombines this information to optimize overall inventory allocation across multiple configurations.
Solution Approach 2:
The system changes the parameters of analysis from whole-configurations to individual feature attributes. By transforming the data representation to focus on feature-level metrics rather than configuration-level metrics, the system simplifies forecasting while maintaining the ability to handle diverse product configurations.
2Adaptability or versatility
If items are produced in multiple configurations with many different combinations of features, then product versatility is improved, but demand forecasting accuracy worsens
Solution Approach 1:
The patent extracts the essential forecasting information by isolating and analyzing individual feature contributions from the complex configuration data. The system separates out which specific features drive sales demand, removing the noise created by configuration variety, and uses these extracted feature-level insights to improve forecasting accuracy.
Solution Approach 2:
Rather than attempting to forecast entire configurations, the system takes a partial approach by forecasting at the feature level. This partial action on the forecasting problem allows the system to capture the essential demand drivers without being overwhelmed by configuration complexity, then uses this partial information to inform overall inventory decisions.
3Adaptability or versatility
If dealer inventory includes diverse item configurations, then customer choice and sales potential are improved, but inventory turnover rate worsens
Solution Approach 1:
The system implements feedback by continuously analyzing actual sales data against predicted sales for different feature configurations. This feedback loop identifies which feature combinations are performing better or worse than expected, allowing the system to dynamically adjust inventory allocations to favor high-turnover configurations while maintaining adequate diversity for customer choice.
Solution Approach 2:
The patent applies dynamics by making inventory allocations flexible and adaptive rather than static. The system continuously updates feature allocation recommendations based on changing sales patterns, allowing the inventory mix to evolve over time to optimize turnover while maintaining the diversity needed for customer preferences.
Data Source
AI summary
A data store includes, for a dealer of interest, a target inventory mix rate for an item having a feature. A computing device is configured to perform an optimization to obtain a recommended feature allocation of the item including the feature by minimizing a difference between a projected inventory mix rate for the feature and the target inventory mix rate for the feature.