Inventory Prediction for New Items Using Similarity Segmentation
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Solution Overview
Problem
Current inventory control systems face challenges in predicting and managing inventory for newly added items, as they struggle to accurately determine when and how many new items to include in the product assortment to meet customer demand, leading to inefficiencies and increased costs.
Innovation Solution
A computer-implemented method and system that aggregates sales data from a database, identifies similar items using a multi-stage similarity and classification module with K-Nearest Neighbors algorithm, calculates target metrics, and predicts average inventory for new items by averaging turn predictions, thereby optimizing inventory assortment and maximizing retail sales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inventory control methods are used for new items, then implementation is simple, but prediction accuracy is low
Solution Approach 1:
The system segments the inventory prediction process into multiple stages: data aggregation from multiple sources, similarity identification using K-Nearest Neighbors, turn rate calculation, and average inventory prediction. This segmentation allows complex prediction tasks to be broken down into manageable components, improving accuracy while maintaining systematic control over complexity
Solution Approach 2:
The patent introduces turn rate as an intermediary metric between sales data and average inventory prediction. By calculating turn rates from similar items and using them as intermediate predictions, the system bridges the gap between historical data and new item inventory forecasts, enhancing prediction accuracy through a mediating computational layer
2Adaptability or versatility
If more new items are added to meet customer demand, then customer satisfaction increases, but inventory costs increase
Solution Approach 1:
The system performs preliminary inventory prediction and assortment selection for new items before they are fully integrated into the product catalog. By predicting average inventory and turn rates in advance, the system enables proactive inventory planning that allows flexible assortment expansion while controlling costs through data-driven decision making
Solution Approach 2:
The patent dynamically adjusts inventory parameters (average inventory levels, turn rates) based on predicted metrics from similar items. This parameter optimization allows the system to adapt assortment flexibility to actual demand patterns, expanding product variety only where predicted inventory turnover justifies the investment, thereby balancing assortment versatility against inventory costs
3Reliability
If simple similarity matching is used, then processing speed is fast, but prediction reliability is low
Solution Approach 1:
The similarity identification process is segmented into multiple computational stages including data preprocessing, feature extraction, K-Nearest Neighbors matching, and turn rate calculation. This segmentation improves prediction reliability by systematically processing data through multiple validation layers while maintaining processing efficiency through modular computation
Solution Approach 2:
The system maintains continuous processing of sales data, similarity metrics, and turn rate calculations as an ongoing operational workflow. By continuously aggregating data and updating predictions rather than performing batch processing, the system sustains high prediction reliability through constant data refinement while maintaining processing speed through streamlined continuous computation
Data Source
AI summary
An example method for predicting average inventory with newly added items can include: aggregating sales data of a plurality of items, the items comprising training items and new items; identifying, using a set of predefined rules, a data set of similar items on the training items for each of the new items, the set of predefined rules comprising a first stage similarity module, a second stage similarity module, and a second stage classification module; obtaining target metrics for each of the new items, the target metrics being turn predictions from the data set of the similar items; calculating mean errors of the turn predictions to identify a set of turn predictions with mean errors lower than a dynamic threshold; obtaining an ultimate turn prediction for each new item by averaging the set of turn predictions; and predicting an average inventory for each new item based on the ultimate turn prediction.