Smart Store Assortment Planning Using Consumer Intent Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Smart stores face challenges in assortment planning due to reduced space and varying consumer habits across regions and time periods, necessitating effective methods to optimize product combinations for maximum profit.
Innovation Solution
An assortment planning system and method that utilizes tracking and detecting apparatuses to identify consumer interactions, analyze consumption intentions, and estimate optimal product combinations based on consumer behavior data, using a processing apparatus with binding, intention analyzing, and estimating devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If store space is reduced for miniaturization, then store size decreases, but product variety and profit potential worsen
Solution Approach 1:
The patent segments the store into multiple regional zones (e.g., northern region, southern region, eastern region, western region) with different assortments tailored to local consumption habits. This allows the limited store space to serve multiple market segments simultaneously, effectively increasing product variety coverage without expanding physical size.
Solution Approach 2:
The patent implements dynamic assortment planning that adjusts product combinations based on real-time consumer behavior data, time of day, day of week, and regional characteristics. The assortment is not static but dynamically optimized to maximize profit within the constrained space by responding to changing consumer preferences.
2Volume of moving object
If store space is reduced for miniaturization, then store size decreases, but assortment planning complexity increases
Solution Approach 1:
The patent replaces manual or mechanical assortment planning with an automated computing system that uses machine learning models and consumer behavior data to generate optimal assortment recommendations. The system automatically processes consumer track data, interaction data, and sales data to determine the best product combinations for each regional store, reducing human complexity while increasing planning accuracy.
3Measurement precision
If consumer behavior tracking is implemented, then consumer preference accuracy improves, but system complexity increases
Solution Approach 1:
The patent employs a multi-functional tracking system that simultaneously captures multiple types of consumer data (movement tracks, product interactions, purchase behavior) using integrated sensors and cameras. This universal system serves multiple purposes: identifying consumer preferences, analyzing shopping patterns, and generating assortment recommendations, thereby justifying the system complexity through its comprehensive utility.
4Adaptability or versatility
If regional localization is increased, then consumer preference matching improves, but inventory management complexity increases
Solution Approach 1:
The patent uses the assortment planning system to pre-determine optimal product combinations for each regional store based on historical data and consumer behavior patterns. By performing preliminary assortment optimization before the shopping season or promotional period, the system prepares inventory recommendations in advance, reducing the complexity of real-time inventory management while maintaining high regional adaptability.
Data Source
AI summary
An assortment planning method, an assortment planning system and a processing apparatus thereof for a smart store are provided. The assortment planning system includes at least one tracking apparatus, a plurality of detecting apparatuses, and a processing apparatus. The tracking apparatus is used to identify a plurality of consumer tracks. The detecting apparatuses are used to detect a plurality of consumer interactive behaviors of a plurality of products. The processing apparatus includes a binding device, an intention analyzing device and an estimating device. The binding device is used to bind the consumer interactive behaviors with the consumer tracks to obtain a number of interactive behavior time sequence records. The intention analyzing device is used to obtain a plurality of consumption intentions for the products according to the interactive behavior time sequence records. The estimating device is used to estimate a best product combination according to the consumption intentions.