Supply Chain Optimization

Abstract

Online surveys and customer feedback have become critical tools for driving strategic decisions in today's dynamic business landscape, particularly in supply chain management. This project introduces a system designed to support enterprises by harnessing the power of artificial intelligence to enhance decision-making across supply chain operations. The system leverages advanced natural language processing (NLP) to conduct sentiment analysis, categorizing customer reviews into positive, negative, or neutral sentiments. Additionally, it incorporates sales analysis to uncover patterns in customer behavior and demand, helping businesses align supply chain activities with market needs. Through comprehensive review analysis, the system identifies recurring themes and concerns, offering insights that can guide product improvements and customer engagement strategies. At the heart of the platform is a user-friendly, welrbased dashboard that visually presents key analytics and insights. This interface empowers executives and decisionmakers to track customer sentiment, monitor sales trends, and evaluate feedback in real time, enabling proactive and informed responses. By focusing on the value and interpretation of customer feedback and sales data, this project empowers businesses to enhance supply chain performance, ensuring t ransparency, responsiveness, and customer satisfaction in a competitive marketplace.