Retail Sales Forecasting Clustering System
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Solution Overview
Problem
Conventional retail sales forecasting relies heavily on managerial expertise and is unpredictable in accounting for unplanned events, such as weather events, leading to inefficiencies in preparing for sale surges.
Innovation Solution
A system that includes a sales history database, a network adapter, and a control circuit configured to provide a retail task user interface, cluster store locations based on shared characteristics, determine local and group sales forecast values using a forecast model, and provide an adjusted sales forecast.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sales forecasting relies on managerial expertise and institutional knowledge, then the forecasting process is simple to implement, but the accuracy and reliability of sales forecasts deteriorate due to unpredictability in accounting for unplanned events
Solution Approach 1:
The patent introduces an automated forecasting system that acts as an intermediary between sales data and forecast outputs. This system uses machine learning models and clustering algorithms to objectively analyze sales histories and generate forecasts, eliminating reliance on subjective managerial expertise while improving reliability through data-driven insights that account for various factors including unplanned events
Solution Approach 2:
The patent replaces the manual, expertise-based forecasting mechanism with an automated computational system. The mechanical process of managerial judgment is substituted with algorithmic processing that consistently applies forecasting models to sales data, thereby improving reliability through systematic analysis rather than human subjectivity
2Device complexity
If sales forecasting uses a single local store's sales history, then the forecasting model is simple to compute, but the accuracy deteriorates due to lack of broader contextual patterns
Solution Approach 1:
The patent merges sales history data from multiple store locations by clustering stores with similar characteristics. This combination allows the forecasting model to leverage broader contextual patterns and trends from comparable stores, improving forecast accuracy for individual locations while maintaining computational feasibility through the clustering approach
3Device complexity
If preparations for sale surges are managed as institutional knowledge dependent on manager expertise, then the system requires minimal technology infrastructure, but the adaptability to unplanned events such as weather events deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the forecasting system continuously analyzes sales data and adjusts predictions based on actual performance and emerging patterns. This feedback loop enables the system to adapt to unplanned events such as weather changes by detecting deviations from expected patterns and adjusting forecasts accordingly, providing versatility without requiring complex manual intervention
Data Source
AI summary
A system for retail forecasting and task management. The system includes a sales history database storing sales histories associated with a plurality of store locations; a network adapter; and a control circuit. The control circuit is configured to: provide, via the network adapter, a retail task user interface on a user device at a store location; cluster a plurality of store locations based on shared characteristics; determine a local sales forecast value on a future date for the store location based on a sales history of the store location using a first forecast model; determine a group sales forecast value on the future date based on sales histories of other store locations using the first forecast model; determine an adjusted sales forecast for the store location based on the local sales forecast value and the group sales forecast value; and provide the adjusted sales forecast.