Forecasting Retail Sales for Furniture and Furnishing Items through the Employment of Multiple Linear Regression and Holt–Winters Models

• Published in: Systems (Basel) 2024-06, Vol.12 (6), p.219
• Main Authors: Ince, Melike Nur, Tasdemir, Cagatay
• Format: Article
• Language: English
• Subjects: Business metrics; Comparative analysis; Competition; Competitive advantage; Consumption; Decision trees; Demand analysis; demand forecasting; Economic development; Economic growth; Economic models; Forecasting; Furniture industry; GDP; Global economy; Global marketing; Gross Domestic Product; Growth rate; Holt–Winters method; Home furnishings industry; Industry forecasts; Linear models (Statistics); Linear regression models; Marketing; Methods; multiple regression analysis; Neural networks; Operations management; Population; Prediction models; Regression analysis; Retail industry; Revenue management; Sales; Sales forecasting; Supply chains; Support vector machines; Trends; Wavelet transforms
• ISSN: 2079-8954; 2079-8954
• DOI: 10.3390/systems12060219

Global economic growth, marked by rising GDP and population, has spurred demand for essential goods including furniture. This study presents a comprehensive demand forecasting analysis for retail furniture sales in the U.S. for the next 36 months using Multiple Linear Regression (MLR) and Holt–Winters methods. Leveraging retail sales data from 2019 to 2023, alongside key influencing factors such as furniture imports, consumer sentiment, and housing starts, we developed two predictive models. The results indicated that retail furniture sales exhibited strong seasonality and a positive trend, with the lowest forecasted demand in April 2024 (USD 9118 million) and the highest in December 2026 (USD 13,577 million). The average annual demand for 2024, 2025, and 2026 is projected at USD 12,122.5 million, USD 12,522.67 million, and USD 12,922.17 million, respectively, based on MLR, while Holt–Winters results are slightly more conservative. The models were compared using the Mean Absolute Percentage Error (MAPE) metric, with the MLR model yielding a MAPE of 3.47% and the Holt–Winters model achieving a MAPE of 4.21%. The study’s findings align with global market projections and highlight the growing demand trajectory in the U.S. furniture industry, providing valuable insights for strategic decision-making and operations management.