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

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–Winte...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2079-8954
2079-8954
DOI:10.3390/systems12060219