Contextual Data-Driven AI-Based Retail Decision Support and Optimization System

Abstract

This invention relates to an AI-based decision support system developed for businesses operating in the retail sector. It utilizes sales data, product information, and external contextual data sources to provide inventory management, demand forecasting, product positioning, and profitability optimization. Existing systems are limited to data collection and reporting, failing to offer proactive and actionable recommendations. This invention integrates multiple data sources, performs analyses through AI algorithms, and provides businesses with directly actionable recommendations. The system increases inventory efficiency, reduces sales losses, and accelerates decision-making processes.

Description

Contextual Data-Driven AI-Based Retail Decision Support and Optimization System Technical Area This invention is used in retail businesses for inventory management and demand forecasting. and barcode (EAN / GTIN) based data processing and artificial intelligence for profit optimization. generating decision support mechanisms using supported analysis and contextual data integration. It is related to computer-aided systems. The invention is particularly relevant to product-based consumption in small, medium, and large-scale retail businesses. analyzing their habits and generating operational recommendations based on these analyses. It is located in the field. State of the Art The current systems used in the retail sector are generally point of sale (POS) and enterprise systems. It consists of energy resource planning (ERP) and business intelligence (BI) systems. These systems process data. It performs collection, storage, and reporting functions. However, the current systems: • It lacks the ability to make proactive decisions. • Cannot effectively use contextual data (weather, events, special occasions, etc.) • It cannot offer directly applicable action suggestions to the user, • It cannot analyze consumption behaviors at the micro level. • It cannot effectively establish semantic relationships between products. Therefore, existing solutions remain reactive and fail to adapt to the dynamic market conditions of businesses. It limits their ability to adapt. Purpose of the Invention The purpose of this invention is; • An automated learning system that uses barcode (EAN / GTIN) based product data. to create, • Products are developed using both code-based and AI-powered semantic methods. to classify, • Analyzing sales data along with time, location, and contextual data, • Generating meaningful recommendations on a daily, weekly, and monthly basis. • To provide businesses with actionable outcomes, 1 • The goal is to maximize profit through inventory optimization. The invention also allows for product placement and inventory levels to be adjusted by anticipating changes in demand due to external factors. It aims to provide guiding suggestions on sales strategies. Explanation of References 1. Data collection module 2. Barcode processing module 3. Product classification and matching module 4. Artificial intelligence analysis engine 5. Contextual data integration module 6. Proposal generation module 7. Reporting and user interface Disclosure of the Invention The invention enables retail businesses to process information from multiple data sources. It is an integrated system that produces decision support outputs. The system consists of the following stages: a) Data Collection Product sales data is collected via barcode (EAN / GTIN) and includes sales time, quantity and transaction details. The information is transferred to the system. b) Product Identification and Matching Products are identified via barcodes. Product names may not be displayed if barcode data is missing. AI-assisted semantic mapping is performed through it. c) Data Analysis Sales data is analyzed over time, relationships between products are identified, and the data is analyzed. It is associated with contextual factors. d) Contextual Data Integration External data sources such as weather conditions, events, public holidays, and the like are integrated into the system. The effects on sales are modeled. e) Decision Engine 2 All data layers are combined and analyzed through artificial intelligence and rule-based algorithms. This is done and action recommendations are generated. f) Proposal Generation By the system; • Increasing or decreasing inventory, • Product placement arrangement, • Campaign suggestions, • Demand increase warnings is created. g) Interpretation Layer The generated suggestions are presented to the user with explanations, along with the rationale behind the suggestions and what is expected. The effects are stated. System Architecture The system consists of the following modules: • Data Collection Module • Product Identification and Category Mapping Module • Time Series Analysis Module • Product Relationship Analysis Module • Contextual Data Integration Module • Decision Engine • Proposal Generation Layer • Reporting and User Interface Technical Impact 3 Thanks to this invention: • Sales losses are reduced, • Inventory efficiency is increased, • Decision-making processes are accelerated, • The need for manual analysis is eliminated. The system offers technical and functional advantages over existing data reporting solutions. It provides. Application Method in Industry The invention is a cloud-based or integrated system that will work with POS systems in retail businesses. This can be implemented with local server infrastructure. System: • Can be integrated with existing sales software, • It can process real-time data, • Adaptable to businesses of different sizes. This allows businesses to reduce inventory costs, improve sales performance, and It can make its operational decisions data-driven. 4

Claims

1. Inventory management, sales forecasting, and profit in food retail businesses. In order to ensure optimization, barcode data and / or equivalent data of the products Collecting product identifiers, mapping this data to the product database, Analyzing sales, inventory, and consumption data, based on historical data. Demand forecasting, inventory, order and product based on the data obtained Generating layout optimization recommendations, from external data sources The analysis of current event, environmental and behavioral data obtained, this Based on the analyses, product-based sales increase and demand change predictions the creation of all this data and its processing by an AI-powered system This involves interpreting and explaining the information and presenting it to the user as a suggestion. Intelligent inventory and profit optimization system.

2. The system, according to Claim 1, uses EAN, GTIN, or other methods for identifying products. It is characterized by the use of equivalent barcode standards.

3. System that exists according to Claim 1 but lacks, is incomplete or has errors in barcode data. AI-based text processing, semantic analysis, and category matching of products. It is characterized by its classification using algorithms.

4. The system, according to Claim 1, provides daily, weekly, and monthly sales and inventory data. by analyzing in periods and generating recommendations at different time scales It is characterized.

5. According to Claim 1, the system provides daily analyses for informational purposes, and weekly and The monthly analyses are aimed at generating optimization and action plan recommendations. It is characterized by its structure.

6. According to claim 1, the system uses data obtained from external data sources for sports purposes. events, public holidays, special occasions, weather, economic indicators and It is characterized by including contextual parameters such as social trends.

7. According to Claim 1, the system identifies specific product groups based on external data analysis results. By creating a forecast of periodic and / or instantaneous increase or decrease in demand for 35. It is characterized.

8. According to claim 1, the system provides the user with information based on the predictions made. product placement, shelf arrangement, stock increase or decrease, and order planning. It is characterized by the presentation of 40 proposals.

9. According to claim 1, the system provides suggestions to the user that are supported by artificial intelligence. expressed in the form of natural language outputs, along with the rationale and expected effects. It is characterized by its performance.

10. The system, according to claim 1, provides tiered services for different user segments. by offering different levels of analysis depth and recommendation within the framework of the model. It is characterized.

11. According to Claim 1, the system aims to improve the accuracy of the recommendations. feedback from users, sales results, and system performance. a learning mechanism that constantly updates itself by processing its data It is characterized by having.

12. According to claim 1, the system monitors stock levels, sales velocity, product turnover rate, and comparing consumption trends to determine the risk of stock depletion and the risk of excess inventory It is characterized by its detection.

13. According to claim 1, the system provides the user with a graphical representation of the analysis results obtained. by presenting it as an interface, report, notification, or integrated system output It is characterized.

14. According to Claim 1, the system is modular and adaptable to different types of businesses. It is characterized by having a structure.

15. According to Claim 1, the system analyzes the co-selling relationships between products. It is characterized by cross-selling and the creation of alternative product recommendations.

16. According to claim 1, the system is capable of analyzing specific events based on contextual data analysis. It is characterized by the creation of proactive recommendations in advance of or during certain periods.

17. According to Claim 1, the system is a recommendation engine that uses company-specific sales data and Personalized results over time based on user behavior. It is characterized by its production. 6