Demand Forecasting System for Small Business Optimization

Demand forecasting is a fundamental element in efficient inventory control and supply chain management through the ability of firms to anticipate future demand and devise methods to service that demand accordingly. The present paper explores two major approaches to demand forecasting: the SARIMA model and the Deep AR model. SARIMA is a more traditional statistical approach and exploits autoregressive, integrated, and moving average components along with seasonal adjustments to pick up trends and seasonality in the sales data. DeepAR is the most advanced deep learning-based probabilistic approach in modelling complex time series dynamics and relationships within interconnected time series. This paper details the process of demand forecasting starting from data collection, preparation, and cleaning stages to model building, testing, and validation. SARIMA was able to clearly prove its fit for short- to medium-term forecasting, enabling it to be very efficient in keeping track of inventory stocks and preventing stockouts. However, DeepAR does outperform SARIMA when using large dynamic datasets where non-linear patterns come into play, providing probabilistic forecasts that quantify uncertainty. Our findings have illuminated the strengths and weaknesses of both methods. Given stable, well-structured datasets, SARIMA is a robust choice. However, DeepAR’s advanced capabilities are well-suited for businesses dealing with complex and dynamic conditions. Firms can optimize operations to adapt to market fluctuations and even make data-driven decisions to enhance efficiency and customer satisfaction by using these tools.