Logistics Management System using Big Data Analytics
Dr. Sumaira Shaikh 1,a *,Shubham Pardeshi 1, b, Kartik Kabra1, c, Vedant Khode1,d and Sourav Sharma1,e
1 Vishwakarma University, Pune, Maharashtra, India
Abstract:
Nowadays, the e-commerce sector has emerged as a significant player in the current financial landscape. It offers a wide range of advantages for global customers, and given the increasing demand for various services and products, there is a pressing need for the development of efficient Web applications.
This e-commerce application serves as the bridge between small businesses and their clientele. The application harnesses the capabilities of various technologies, including MongoDB for data storage, Node.js and Express.js for a fast and efficient backend, and HTML CSS for constructing a user-friendly interface for clients.
This thesis underscores the proficiency of the (MongoDB, Express.js, HTML, CSS, Node.js) technology stack in the creation of a comprehensive e-commerce application with robust scalability options. The study delves into a meticulous development process, spanning from the backend to the frontend. The results unequivocally underscore the indispensable role in crafting a customizable Web application. We have also investigated the realm of big data analytics and its applications in logistics and supply chain management. It delves into innovative methods, practices, and prospects within this field. The articles within this collection scrutinize multiple avenues for enhancing big data analytics and its applications in logistics and supply chain management. These include the exploration of technology-centric tracking approaches, the examination of the correlation between financial performance and data-driven supply chains, as well as the assessment of implementation challenges and the maturity of supply chain capabilities in the context of big data.
Keywords:
E-commerce, Web application, MongoDB, Node.js, Express.js, HTML, CSS, Logistics management, Data analytics, Big data, Supply chain, Predictive analytics, User authentication, Order management, Inventory management, Route optimization, Real-time tracking, Reporting and analytics, Literature survey, Internet of Things (IoT), High-level design, Operational efficiency, Customer satisfaction, Resource optimization, Competitive advantage, Environmental impact reduction, Compliance, Accuracy, Conclusion.
