Multi-Dimensional Recommended Order System for Configurable Products
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Solution Overview
Problem
Conventional methods struggle to accurately forecast demand for configurable products due to their complex multi-dimensional nature, leading to imbalances in supply and demand, which results in significant revenue losses for manufacturers.
Innovation Solution
A multi-dimensional recommended order system that optimizes imbalances between future and target supplies of product configurations and their dimensions by using a processor and memory to determine recommended orders based on sales metrics, demand data, and constraint analysis, ensuring supply adjustments that maximize profitability while adhering to factory, marketing, and regulatory constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional demand forecasting methods are used for configurable products, then the forecasting process is simple, but the accuracy of demand forecasts deteriorates due to the exponential number of configurations
Solution Approach 1:
The patent segments the complex product configuration space into multiple independent dimensions (e.g., engine type, transmission, body style, color). Instead of forecasting demand for each individual configuration combination, the system forecasts demand at the dimension level and then reconstructs configuration-level forecasts by combining dimensional forecasts. This segmentation reduces the exponential complexity into manageable linear components.
Solution Approach 2:
The patent transforms the forecasting problem from configuration space to dimension space. By changing the dimension of analysis from individual product configurations to independent configuration dimensions, the system reduces the problem from exponential complexity (number of configurations) to linear complexity (number of dimensions). This dimensional transformation enables accurate forecasting without being overwhelmed by configuration complexity.
2Productivity
If manufacturers produce products based on inaccurate forecasted demand, then production planning is simplified, but revenue losses increase due to supply-demand imbalances
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors actual sales data and compares it with forecasted demand. The system uses this feedback to refine and update demand forecasts for each configuration dimension, creating a closed-loop system that learns from past performance. This feedback loop enables manufacturers to adjust production planning dynamically, reducing revenue losses from supply-demand mismatches while maintaining production efficiency.
Solution Approach 2:
The patent changes the parameters used in production planning from single aggregate demand forecasts to multi-dimensional demand forecasts that account for each configuration dimension independently. By changing the parameter structure from simple total demand to dimensional demand breakdowns, the system enables more precise production planning that matches actual market demand across different configuration preferences, thereby reducing revenue losses.
3Adaptability or versatility
If the number of configuration dimensions is increased to capture product variety, then product customization capability is improved, but forecasting difficulty increases exponentially
Solution Approach 1:
The patent segments the forecasting task into independent dimension-level forecasts rather than attempting to forecast the entire configuration space as a whole. Each configuration dimension (e.g., engine type, transmission, color) is forecasted separately using its own demand patterns and historical data. This segmentation maintains the ability to capture full product variety while reducing forecasting difficulty from exponential to linear complexity.
Solution Approach 2:
The patent performs a dimensional transformation by shifting the forecasting analysis from the configuration combination space to the individual dimension space. This allows the system to maintain high product customization capability by tracking all configuration dimensions while simplifying the forecasting process. The dimensional change enables the system to handle increased numbers of configuration dimensions without exponential increase in forecasting difficulty.
Data Source
AI summary
A multi-dimensional recommended order system generates recommended orders for configurable products. The multi-dimensional recommended order system generates the recommended orders based on a multi-dimensional demand and sales metrics analysis. The multi-dimensional recommended order system determines the recommended order for each product configuration with a goal of moving a future supply mix of complex products to an optimized target supply mix. The recommended order can be generated and evaluated based on demand analysis of not only configured products but also based on analysis for demand of particular product dimensions. Thus, the system determines a recommended order for each configurable product by minimizing imbalances between future supplies and target supplies of product configurations and future supplies and target supplies of dimensions of the product configurations.