Machine Learning Sales Forecasting with Fixed Cost Adjustment
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Solution Overview
Problem
Retailers face challenges in maximizing sales due to improper pricing, inventory management, and channel distribution, leading to suboptimal sales across various sales channels.
Innovation Solution
The use of trained machine learning processes to predict sales across different channels by generating features from historical sales data, adjusting for fixed cost effects, and applying these models to forecast future sales, thereby recommending optimal sales channels for items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If retailers use traditional sales forecasting methods, then the forecasting process is simple, but the accuracy of sales predictions across various channels is insufficient
Solution Approach 1:
The patent replaces traditional mechanical forecasting methods with machine learning models that automatically learn patterns from historical sales data across multiple channels, significantly improving prediction accuracy while the system handles the complexity of multi-channel analysis
Solution Approach 2:
The patent introduces fixed effect values as intermediary parameters that capture channel-specific characteristics and item-category interactions, serving as mediators between raw sales data and final predictions to improve accuracy without requiring direct complex modeling of all channel dynamics
2Productivity
If retailers allocate inventory based on limited channel analysis, then the inventory management is straightforward, but the profitability across different sales channels is suboptimal
Solution Approach 1:
The patent segments the sales analysis by dividing items into different categories and evaluating each category's performance across multiple channels separately, allowing retailers to identify which specific item categories should be allocated to which channels to maximize profitability
Solution Approach 2:
The patent changes the parameter of channel evaluation from simple sales volume to fixed effect values that capture the true profitability contribution of each channel for different item categories, enabling more accurate inventory allocation decisions
3Productivity
If retailers sell items through a single channel, then the operational complexity is reduced, but the overall sales volume and market reach are limited
Solution Approach 1:
The patent creates a universal forecasting system that handles multiple sales channels simultaneously, allowing retailers to identify the optimal channel mix for each item category and maximize overall sales volume while the system manages the complexity of coordinating across channels
Data Source
AI summary
This application relates to employing trained machine learning processes to predict sales across various sale channels. For example, a computing device may generate features based on historical sales information, and trains the machine learning processes based on the generated features. In some examples, the computing device determines fixed cost effects from selling items across various sales channels, and adjusts the sales information based on the fixed cost effects. The computing device also generates features based on the adjusted sales. The computing device may apply the trained machine learning processes to sales information for one or more items to predict the sales of one or more items across one or more sales channels during a future temporal period. In some examples, the trained machine learning processes generate a ranking of items for a sales channel based on the output generated from the trained machine learning processes.