Market Dynamics: Enhancing Supply Chain Efficiency Through Demand Prediction

Abstract

This research gives a state-of-the-art solution for web-based demand forecasting. Machine learning methods are employed to build the approach, which takes use of Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling. Customers may utilize our system to provide product codes combined with the period's start and end dates in order to correctly anticipate future demand patterns. Through the combination of previous sales data with current market circumstances, competitive plans, and other external variables, our system is able to discover complicated consumer patterns. This gives data that may be utilized for inventory management, pricing strategies, and production scheduling. All things considered, the holistic plan optimizes resource usage, eliminates waste, and supports consumer satisfaction. A data-driven solution that takes advantage of AI capabilities is being offered as part of this joint endeavor. This solution is positioned to assist numerous sectors, including manufacturing, retail, and healthcare, and it will boost operational efficiency and sustainable development in a global economic framework. Keywords— Product Demand Forecasting, Inventory Management, Production Planning, Time Series Analysis, Predictive Analytics, Inventory Optimization, Demand Prediction System, Real-time Forecasting, Data-driven Decision Making, Supply Chain Optimization, Price Elasticity Analysis, Demand Volatility Management, Retail Analytics.