Daily Archives: May 27, 2025

Authors: Pratichi Dhar

Abstract: This study explores the effect of AI-powered technologies on productivity, cost reduction, and decision-making within the “Supply Chain Management (SCM)” of “Fast-Moving Consumer Goods (FMCG)”. It aims to explore the ways in which AI enhances operational performance and sustainability. Academic research identifies inadequate infrastructure, particularly in underprivileged regions, high implementation costs, and data privacy concerns as significant challenges. This study reached conclusions by utilizing both primary and secondary data through a combination of research methods. AI solutions enhance logistics, inventory management, and resource allocation, minimizing waste and errors while boosting cost efficiency. The use of AI in predictive analytics and real-time decision-making enhances strategic planning and improves supply chain agility. The advantages of AI surpass its disadvantages, including integration with legacy systems and significant upfront expenses. The results show that AI enhances the resilience and sustainability of FMCG supply chains. There is a need for research on data security, implementation methods, and scalability to fully realize its potential. AI has the ability to revolutionize supply chains entirely, making it crucial for organizations to stay competitive in the ever-evolving global market.

 

 

Published by: vikaspatanker

Authors: Yashas D R, Vinutha H N, Merlin B, Soundarya R, Chethan V

Abstract: Identification of an existent's blood group is pivotal in exigency situations, for identity authentication, and in population analysis. It would else involve drawing a blood sample and assaying it in a laboratory, which is painful, tedious and requires trained labor force and installations. Herein, we suggest a way to prognosticate blood groups without blood through the use of point images and a Convolutional Neural Network (CNN). Since fingerprints are distinct in each existent, we suppose they could have patterns associated with natural characteristics similar as blood type. We gathered point images with eight colorful blood groups marked and used them to train a CNN model to classify them. We estimated the performance of the trained model using criteria similar as delicacy, perfection, recall, and F1- score upon testing. Our findings were encouraging, indicating that fingerprints may be potentially employed to cast blood groups using deep literacy. In the future, we will expand our dataset with fresh samples, try out bettered CNN models, and work on securing individualities' data. This system has the implicit to offer an invasive-free, hastily, and easier system for blood group vaticination, particularly in locales with no lab setup.

 

 

Published by: vikaspatanker