Time-Series Demand Forecasting and Supply Chain Optimization Using ARIMA and SARIMA Statistical Models

Abstract: Nowadays, companies are facing many problems because business conditions keep changing very fast. Customer needs can be varying from time to time which can result in change in competition, and market situations. Consequently, companies find it difficult to guess how much demand they will have in the future. If demand is not predicted correctly, it may result in extra stock or stock outs, which causes losses. So, demand forecasting has become an important part of supply chain planning. In this project, past sales data is used to predict future demand. The data is studied based on time to understand how demand changes in a certain period of time. Simply this is known as time- series forecasting. Two models(ARIMA, SARIMA) are used in this work. ARIMA is used when the data does not show seasonal changes, and SARIMA is used when demand repeats in a seasonal manner. The predictions obtained from these models help in planning inventory and purchasing activities. This allows companies to maintain enough stock without spending too much on storage. The method used in this project is easy to apply and is suitable for small and medium businesses. Overall, this project explains how previous sales data can be used in a practical way to support better planning and decision-making. Index Terms: Time-Series Forecasting, Demand Forecasting, Supply Chain Optimization, ARIMA Model, SARIMA Model.