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Designing AI-Powered Frontend Systems for Real Time Decision-Making in Large-Scale Retail and Supply Chain Platforms
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The large-scale retail and supply chain systems have overwhelmed the front end decision support tools capabilities, causing delays in response and inefficiencies in inventory allocation, pricing and customer experience. Although the research that has been conducted so far has mostly concentrated on the optimization of the backends and predictive analytics, little has been done on actually integrating real-time and AI-based decision intelligence into frontend systems. The paper will present a proposal of an AI-powered frontend system (AIFS), which is a hybrid demand forecasting model, a multi agency coordination, and an explainable AI module on a real time streaming architecture. It is implemented as a modular system that comprises of user interaction layer, real time inference engine, continuous feedback loop to provide adaptive learning and interactive visualization layer. In order to test the proposed method, we create the simulation-based experimental setup based on the use of the M5 retail data that is supplemented with the synthetic streaming and IoT data to simulate real-time working conditions. It was experimentally shown that the suggested system is capable of cutting down the decision latency by up to 62 percent and enhancing inventory turnover by 18 percent in contrast to baseline rule-based systems in simulated real-time environments. The findings indicate that frontend-native AI systems are potentially useful in improving real-time operational decision-making processes and form the basis of future studies on human-AI collaborative interfaces in the retail and supply chain management.
Keywords
ai-powered frontend, real-time decision-making, retail supply chain, edge ai, explainable ai, demand forecasting, multi-agent systems
How to cite this paper
@article{1722383,
author = {Sohail Sayed},
title = {Designing AI-Powered Frontend Systems for Real Time Decision-Making in Large-Scale Retail and Supply Chain Platforms},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {1},
pages = {949-961},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722383.pdf},
abstract = {The large-scale retail and supply chain systems have overwhelmed the front end decision support tools capabilities, causing delays in response and inefficiencies in inventory allocation, pricing and customer experience. Although the research that has been conducted so far has mostly concentrated on the optimization of the backends and predictive analytics, little has been done on actually integrating real-time and AI-based decision intelligence into frontend systems. The paper will present a proposal of an AI-powered frontend system (AIFS), which is a hybrid demand forecasting model, a multi agency coordination, and an explainable AI module on a real time streaming architecture. It is implemented as a modular system that comprises of user interaction layer, real time inference engine, continuous feedback loop to provide adaptive learning and interactive visualization layer. In order to test the proposed method, we create the simulation-based experimental setup based on the use of the M5 retail data that is supplemented with the synthetic streaming and IoT data to simulate real-time working conditions. It was experimentally shown that the suggested system is capable of cutting down the decision latency by up to 62 percent and enhancing inventory turnover by 18 percent in contrast to baseline rule-based systems in simulated real-time environments. The findings indicate that frontend-native AI systems are potentially useful in improving real-time operational decision-making processes and form the basis of future studies on human-AI collaborative interfaces in the retail and supply chain management.},
keywords = {ai-powered frontend, real-time decision-making, retail supply chain, edge ai, explainable ai, demand forecasting, multi-agent systems},
month = {July},
}