Product Representation Learning for New Product Demand Forecasting
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Solution Overview
Problem
Demand forecasting for new products is challenging due to the lack of historical data, and manual selection of surrogate products is cumbersome and often inaccurate, as similar-looking products may not have similar demands.
Innovation Solution
A system that uses a deep neural network to learn a non-linear mapping from product characteristics to a numerical space where demand similarities are inferred, optimizing surrogate recommendations based on cosine similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of surrogate products is used, then domain expertise can be applied, but the process is cumbersome and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of expert selection with an automated computational system. A deep neural network learns non-linear mappings from product characteristics to demand similarity scores, automatically identifying surrogate products without human intervention. This substitution maintains high accuracy while eliminating time losses associated with manual evaluation.
Solution Approach 2:
The system enables self-service by allowing the algorithm to autonomously perform surrogate product selection. The deep learning model independently processes product characteristics, computes demand similarities, and ranks surrogate candidates without requiring domain experts to manually evaluate each product pair, thereby resolving the contradiction between accuracy and time consumption.
2Ease of operation
If name-wise or category-wise similar products are selected as surrogates, then selection is simplified, but demand similarity is not guaranteed
Solution Approach 1:
The patent transforms the selection criteria from simple name-wise or category-wise matching to a comprehensive parameter-based approach. The deep neural network analyzes multiple product characteristics simultaneously and learns non-linear relationships between these parameters and demand patterns. This parameter transformation enables the system to identify products with truly similar demand profiles rather than just superficial similarities.
Solution Approach 2:
The system replaces simplistic filtering mechanisms with a sophisticated deep learning-based evaluation system. Instead of relying on basic categorical matching, the neural network computes continuous demand similarity scores based on learned representations of product characteristics, thereby achieving both ease of operation and high measurement precision.
3Measurement precision
If deep neural network with non-linear mapping is used, then demand similarity accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer - the deep neural network with non-linear mapping functions - that bridges product characteristics and demand similarity assessment. This intermediary learns complex relationships between input features and demand patterns, enabling high measurement precision. The complexity is justified and managed by the intermediary's ability to capture non-linear dependencies that simpler systems cannot model.
Data Source
AI summary
A deep learning model that learns a non-linear mathematical function that maps vector representations of products obtained from their characteristics known before launch, to a new numerical space where pair-wise product demand similarities can be inferred from their newly learned dense vector representations. The non-linear mapping between different numerical spaces can be optimized such that two products that have a small demand difference, also have a small cosine distance between their respective vector representations, even if their characteristics are not all that similar. The inferred demand similarities can be used to provide surrogate product recommendations that may be used to predict initial demand volume within a new product's launch period.