Retail Logical Data Model for Quality Feedback Segmentation
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Solution Overview
Problem
Current data warehouse solutions for retail businesses lack an effective method to capture and analyze quality feedback information, hindering the ability to improve supply chain operations and manage inventory and sales efficiently.
Innovation Solution
A Retail Logical Data Model is developed, incorporating a Quality Feedback subject area within a data warehouse system to organize and store quality feedback data, enabling analysis and improvement of product and service quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quality feedback information is captured and stored in a data warehouse, then the ability to analyze and improve supply chain operations, inventory management, and product sales is enhanced, but the system complexity and data organization requirements increase
Solution Approach 1:
The patent segments quality feedback data into distinct categories (product quality, service quality, supplier quality) and organizes them into a hierarchical data model with separate tables for feedback headers, details, and metadata. This segmentation allows complex quality feedback information to be stored and analyzed systematically without overwhelming system complexity.
Solution Approach 2:
The patent introduces intermediary data structures including feedback classification tables, product hierarchy tables, and supplier relationship tables that act as mediators between raw quality feedback data and analytical applications. These intermediary structures standardize data organization and simplify access for analysis.
2Loss of information
If structured and unstructured feedback data is systematically organized, then actionable insights for supply chain and inventory management are improved, but the data storage and processing requirements increase
Solution Approach 1:
The patent extracts essential quality feedback information into standardized fields (feedback type, severity, product identifier, supplier identifier) while storing detailed unstructured comments separately. This extraction approach captures the most valuable analytical information in a compact, queryable format while preserving complete feedback data for reference.
Solution Approach 2:
The patent transforms unstructured feedback data into structured parameters including numerical ratings, categorical classifications, and temporal metadata. This parameter transformation enables efficient storage, indexing, and analysis of quality feedback while reducing the computational burden of processing unstructured text data.
Data Source
AI summary
A computer implemented method of and system for capturing, storing and organizing quality feedback information associated with products sold by a retail enterprise. The quality feedback information is stored and organized within a relational database in accordance with a logical data model comprising a plurality of entities and relationships defining the manner in which quality feedback information is stored and organized within the relational database. The relational database, populated with quality feedback information, provides the retail enterprise with the means to analyze and improve retail operations, to better manage store inventory, and more efficiently manage product sales and returns.