ML-Based Life-Cycle Classification for Demand Forecasting
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Solution Overview
Problem
Current demand-forecasting processes for retailers fail to accurately classify products as either long-life-cycle (LLC) or short-life-cycle (SLC) products at specific store locations, leading to forecasting inaccuracies due to over-generalization and the inability to keep pace with changing inventories and trends.
Innovation Solution
A machine learning model is trained to classify product-store pairs as LLC or SLC using time-series data and feature-based rules, employing a random forest classifier and a hierarchical set of rules to assign life-cycle classifications, with an 'inconclusive' label for data sets that don't meet classification criteria, and applying majority voting for interim classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If all products are categorized as one product type (e.g., all grocery items as LLC-type), then the forecasting process is simplified and can be applied broadly across all locations, but forecasting accuracy deteriorates for products that are exceptions to the generalization
Solution Approach 1:
The patent segments products into different life-cycle categories (LLC and SLC) based on their specific sales patterns and characteristics rather than applying a single categorization to all products. This segmentation allows the forecasting system to select appropriate forecasting models for each product type, thereby maintaining forecasting accuracy while managing complexity through automated classification.
2Measurement precision
If manual classification of each SKU-store combination as SLC or LLC is performed, then forecasting accuracy for individual products can be improved, but the time and resources required increase significantly
Solution Approach 1:
The patent replaces manual classification processes with an automated machine learning system that analyzes historical sales data and product attributes to classify products as LLC or SLC. This substitution eliminates the time-consuming and resource-intensive manual classification process while maintaining or improving classification accuracy through consistent application of classification criteria across all products.
3Productivity
If broad categorizations are applied to all products at a location, then the forecasting process becomes more efficient and scalable, but forecasting accuracy deteriorates for products with unique sales patterns
Solution Approach 1:
The patent implements a dynamic classification system that automatically assigns products to LLC or SLC categories based on their specific sales patterns and characteristics. This dynamic approach allows the system to adapt to individual product behaviors rather than applying static broad categorizations, thereby maintaining forecasting accuracy for products with unique sales patterns while preserving overall process efficiency through automation.
Data Source
AI summary
Techniques for training a machine learning model to generate life-cycle classifications for product-store pairs are disclosed. A system generates training data sets for training a machine learning model by comparing sets of time-series data to a set of feature-based rules mapped to life-cycle labels. The system trains the machine learning model using the training data sets to classify time-series data associated with product-store pairs. The system applies the trained machine learning model to a particular set of time-series data for a particular product-store pair, such as sales data for a particular product at a particular store. The machine-learning model generates a life-cycle classification for the set of time-series data and the corresponding product-store pair. The system selects a forecasting model to forecast attributes of the product-store pair based on the life-cycle classification.