Demand Forecasting For Furniture Industry With Multi-Variable Time Series Models

Demand forecasting is important for businesses in order to be prepared for any adverse situations that may arise in the future. Time series models are a useful and reliable tool for demand forecasting. These models enable businesses to improve planning and decision-making by providing more accurate and reliable demand forecasts. This study aims to forecast the monthly door sales quantities of a business in the furniture industry using various multi-variable Time Series methods. The variable set includes dollar exchange rate, inflation, and interest rates. Models considered and compared are ARIMA-SARIMAX, Multiple Linear Regression and Holt-Winters. Analysis are conducted using real-life sales data obtained for years 2005 to 2019. Required data on the dollar exchange rate, inflation, and interest rates are obtained from the Central Bank of Türkiye. By incorporating these variables, the study aims to enhance the accuracy and reliability of the predictions, providing valuable insights for the industry. Results show that ARIMA-SARIMAX produce more accurate forecasts compared to (Holt-Winters and Multiple Linear Regression. It is observed that the time series model that takes into account the seasonality and trend factors provide better forecast results in the furniture industry. The findings highlight the importance of advanced forecasting methods in maintaining competitive advantage. This approach not only supports strategic planning but also helps in optimizing inventory levels.