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

Burak Güngör; Emine Tokgöz; Ali Sevinç; Eren Kamber; Mehmet Gümüş

Makale detayları

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

Yazarlar:

Burak Güngör | Emine Tokgöz | Ali Sevinç | Eren Kamber | Mehmet Gümüş

Yayıncı:

Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar:

Endüstri Mühendisliği

Dergi:

ALKÜ Fen Bilimleri Dergisi Vol.7 Issue 2, pp.111-128

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351434

pISSN:

2667-7814

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 İşletmelerin gelecekte oluşacak herhangi bir olumsuz duruma hazırlıklı olmaları için talep tahmini çalışmalarına önem vermeleri gerekir. Zaman serileri modeli, mobilya sektöründe talep tahmini için kullanışlı ve güvenilir bir araçtır. Zaman serileri modeli, işletmelere daha doğru ve güvenilir talep tahminleri sağlayarak, daha iyi planlama ve karar verme imkanı sunmaktadır. Bu çalışmada Zaman Serileri ile talep tahmin yöntemi kullanılarak işletmenin aylık kapı satış miktarlarının tahmini yapılması amaçlanmaktadır. Zaman serileri modelinin ARIMA, SARIMAX, diğer talep tahmin yöntemlerine Holt-Winters, Çoklu Doğrusal Regresyon kıyasla daha doğru tahminler ürettiği görülmüştür. Özellikle, mevsimsellik ve trend gibi faktörleri de dikkate alan zaman serileri modeli, mobilya sektöründeki talepleri daha iyi modellemeyi başarmıştır. Bu bağlamda, Alanyada bir fabrikanın 2005-2019 yılları arasındaki kapı satış miktarı verileri tahminleme yapmak için kullanılmıştır. Dolar kuru, enflasyon ve faiz oranı verileri Merkez Bankasından alınmıştır.

Makale detayları

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

Yazarlar:

Burak Güngör | Emine Tokgöz | Ali Sevinç | Eren Kamber | Mehmet Gümüş

Yayıncı:

Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar:

Endüstri Mühendisliği

Dergi:

ALKÜ Fen Bilimleri Dergisi Vol.7 Issue 2, pp.111-128

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351434

pISSN:

2667-7814

Furniture Sector

Demand Forecasting

Holt-Winters

ARIMA-SARIMAX

Multiple Linear RegressionMobilya Sektörü

Talep Tahmini

Holt-Winters

ARIMA-SARIMAX

Çoklu Doğrusal Regresyon

Sayı

Makale detayları

Yazarlar: Burak Güngör | Emine Tokgöz | Ali Sevinç | Eren Kamber | Mehmet Gümüş

Yayıncı: Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar: Endüstri Mühendisliği

Dergi: ALKÜ Fen Bilimleri Dergisi Vol.7 Issue 2, pp.111-128

Belge Tipi: Makale

Yayın Tarihi: 8/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001351434

pISSN: 2667-7814

Sayı

Yazarlar:

Burak Güngör | Emine Tokgöz | Ali Sevinç | Eren Kamber | Mehmet Gümüş

Yayıncı:

Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar:

Endüstri Mühendisliği

Dergi:

ALKÜ Fen Bilimleri Dergisi Vol.7 Issue 2, pp.111-128

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351434

pISSN:

2667-7814