Assortment Planning System Simulating Product Interactions
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Solution Overview
Problem
Retailers face difficulties in determining how the addition or deletion of products affects overall and individual product performance metrics such as sales volume and profits due to complex interactions within product assortments.
Innovation Solution
An assortment planning system that simulates the impact of adding or deleting products by using a graphical user interface, interaction unit, and transfer demand engine to calculate and represent the effects on sales volume and profits, allowing retailers to optimize their product offerings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If retailers expand their product assortment to meet customer preferences, then customer satisfaction and sales volume increase, but difficulty in determining performance metric impact increases
Solution Approach 1:
The system segments the overall performance metric into individual product-level metrics. It calculates and displays performance metrics for each product separately, allowing retailers to understand the specific impact of each product on overall sales volume and profits, rather than dealing with aggregated data alone.
Solution Approach 2:
The system introduces an intermediary simulation engine that models the complex interactions between products. This simulation technology acts as a mediator between the retailer's assortment decisions and the actual performance outcomes, providing predictive insights into how product additions or deletions will affect overall performance.
2Loss of energy
If retailers delete unpopular products to optimize assortment, then profits may improve, but uncertainty about overall performance impact increases
Solution Approach 1:
The system performs preliminary simulation and analysis before retailers make deletion decisions. It predicts the potential impact of removing unpopular products on overall profits and sales volume, allowing retailers to make informed decisions about which products to delete based on simulated outcomes rather than intuition.
Solution Approach 2:
The system provides feedback loops that show the relationship between product deletions and performance metric changes. It displays how deleting specific products will affect individual product metrics and overall assortment performance, enabling retailers to adjust their decisions based on this feedback information.
3Measurement precision
If retailers analyze individual product performance to optimize assortment, then product-level insights improve, but system complexity increases
Solution Approach 1:
The system provides a universal platform that handles multiple functions: it calculates individual product metrics, simulates product interactions, predicts performance outcomes, and generates actionable insights. This multi-functional approach consolidates what would otherwise require multiple separate analysis tools into a single integrated system.
Solution Approach 2:
The system automatically calculates and updates performance metrics for individual products based on simulation results. It self-generates the detailed product-level insights without requiring manual analysis, reducing the complexity burden on retailers while providing precise measurement capabilities.
Data Source
AI summary
The system includes a comparison unit that identifies products in an initial assortment of products, a desired assortment of products and the performance metric for each product. The comparison unit compares the initial assortment of products and the desired assortment of products to determine kept products, added product and deleted products. The system includes an interaction unit simulating the interaction of the kept products, the added products and the deleted products based on the performance metric. The system generates simulation results identifying the interactions between the kept products, the added products and the deleted products.