Hierarchical Neural Network for Granular Out-of-Stock Prediction
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Solution Overview
Problem
Existing methods for predicting out-of-stock products in retail stores are inaccurate due to the aggregation of disparate datasets with different data hierarchies, leading to the loss of valuable granular information and ineffective OOS event predictions.
Innovation Solution
A hierarchical neural network system that processes low-level, categorical, and time-series data without aggregation, using CNN, embedding, and LSTM layers to generate an out-of-stock probability for specific products within a predefined time frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If disparate datasets with different data hierarchies are aggregated for prediction, then the system can process multiple data sources, but valuable granular information is lost and prediction accuracy deteriorates
Solution Approach 1:
The system segments the prediction task into multiple hierarchical levels (store level, product level, category level) and processes each level separately with appropriate data aggregation only at that level, rather than aggregating all data uniformly. This preserves granular information at each hierarchy while still leveraging multiple data sources.
Solution Approach 2:
The system introduces a hierarchical dimension to the data processing architecture, organizing data and predictions across multiple levels (store→product→category). This dimensional structure allows simultaneous processing of aggregated and granular data without loss of information, resolving the contradiction between versatility and precision.
2Loss of time
If sales representatives visit stores frequently to check OOS events, then OOS detection timeliness improves, but the cost and complexity of manual monitoring increases
Solution Approach 1:
The system enables self-service OOS detection by automatically monitoring inventory levels, sales data, and product availability without requiring manual store visits. The automated system continuously detects OOS events and generates predictions, eliminating the need for frequent physical inspections while reducing operational complexity.
Solution Approach 2:
The system replaces the mechanical manual monitoring process (sales representatives physically visiting stores) with an automated information processing system that uses neural networks and data analysis. This substitution dramatically reduces detection time and eliminates the complexity of coordinating human resources for frequent store visits.
3Measurement precision
If a hierarchical neural network processes data without aggregation, then granular information is preserved and prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The computational complexity is managed by segmenting the neural network into hierarchical layers that process different levels of data granularity separately. Each layer handles specific types of data at appropriate aggregation levels, making the complex computation more manageable and efficient while preserving prediction accuracy.
Solution Approach 2:
The system uses hierarchical dimensionality to organize computational processing across multiple levels (store, product, category). This dimensional structure allows the complex neural network to process granular information efficiently by distributing computation across hierarchical layers rather than processing all data uniformly at one level.
Data Source
AI summary
A hierarchical neural network for predicting out of stock products comprises an input layer that receives data from data sources that store disparate datasets having different levels of attribute detail pertaining to products for sale in stores of a retailer. A first level of neural networks processes the data from the data sources into respective learned intermediate vector representations. A second level comprises a concatenate layer that concatenates the learned intermediate vector representations from the second level into a combined vector representation. A third level comprises a feed forward network that receives the combined vector representation and outputs to the retailer an out of stock probability indicating which store and product combinations are likely to have out of stock products over a predetermined timeframe.