Autonomous Product Mix Optimization Using Virtual Shelf Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods fail to effectively utilize sales tracking data to identify similar products across locations, leading to unattainable strategic learnings, impossible pricing strategy impact calculations, and impractical identification of business improvement opportunities due to complex data analysis and costly physical changes in product placement.
Innovation Solution
A computer-implemented method using algorithmic autonomous learning to determine optimal business metrics by defining a boundary-constrained shelf space, ranking products based on sales, and applying business rules to simulate and forecast sales, allowing for virtual testing of 'what-if' scenarios and visualization of product placement impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical product mix changes are made to test pricing strategies, then sales performance can be measured, but costs and time increase significantly
Solution Approach 1:
The patent creates a virtual copy of the physical shelf space and product mix, allowing pricing strategies to be tested in a simulated environment. The system models the shelf space, products, and customer behavior to predict sales outcomes without requiring physical reconfiguration, thus eliminating time losses while maintaining measurement capability through virtual experimentation
Solution Approach 2:
The system performs preliminary virtual testing of pricing strategies and product mix configurations before implementing physical changes. By simulating various scenarios in advance and predicting their impact on sales performance, the system allows decision-makers to select optimal strategies without undergoing costly and time-consuming physical trial-and-error processes
2Loss of information
If vast amounts of sales and marketing data are collected from multiple locations, then more information is available, but identifying actionable insights becomes impractical
Solution Approach 1:
The system employs autonomous learning algorithms that automatically analyze sales and marketing data from multiple locations, identify patterns, and generate actionable insights without requiring extensive human intervention. The system serves itself by continuously learning from incoming data, adapting its models, and providing recommendations, thereby managing the complexity of vast data sets while maximizing information utilization
Solution Approach 2:
The patent replaces manual data analysis methods with automated computational algorithms and machine learning models. Instead of relying on human analysts to manually process vast amounts of data, the system uses computational power to automatically detect patterns, correlations, and insights, significantly reducing the complexity burden while maintaining comprehensive data utilization
3Manufacturing precision
If expert personnel are used to setup and optimize product displays, then placement quality improves, but costs and reprogramming requirements increase
Solution Approach 1:
The system incorporates autonomous learning capabilities that automatically optimize product placement and display configurations without requiring expert personnel intervention. The algorithms continuously analyze sales data and customer behavior to self-adjust product mix and placement strategies, maintaining high placement quality while eliminating the need for expensive expert setup and ongoing reprogramming
Solution Approach 2:
The system implements dynamic, adaptive optimization where product placement and mix recommendations automatically adjust based on real-time and historical data. Rather than requiring periodic manual reconfiguration by experts, the system continuously learns and adapts its recommendations, maintaining optimal placement quality while reducing the complexity and cost associated with expert personnel involvement
Data Source
AI summary
The present invention relates to a computer implemented method of determining optimal business metrics from a product mix constrained by at least physical shelf space and selectively by at least one business rule. The computer implemented method comprising the steps of defining a boundary constrained shelf space, placing, physically, a product mix within the boundary constrained shelf space, and creating a product mix ranking based, in part, on prior sales of each of the product type. The computer implemented method continues by using a data processing device to develop, through algorithmic autonomous learning, achievable business metric performance of the boundary constrained shelf space. In this regard, a group of similar product mix/ranking is optimized to create an ideal product mix/ranking which is then use to inform changes to make to the product mix to achieve the desired OPTIMAL BUSINESS METRIC.