Araştırma Makalesi
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EFFECTS OF THE COVID-19 PANDEMIC ON TURKISH NATURAL STONE INDUSTRY: A GREY FORECASTING MODEL

Yıl 2022, Cilt: 10 Sayı: 2, 520 - 531, 30.06.2022
https://doi.org/10.21923/jesd.989253

Öz

When the indicators in recent years are examined in the developing and renewed economic environment in Turkey, it is seen that the momentum of the natural stone industry and its share in total mining exports have increased steadily. However, the Covid-19 pandemic in 2020, which affected the whole world, also affected the Turkish natural industry. Within the scope of this study, the export values of the Turkish natural stone industry on a monthly and yearly basis were evaluated both before the pandemic and during the pandemic. Export figures for 2020 and 2021 were tried to be estimated using the Gray Forecast model. With the effect of the Covid-19 pandemic, natural stone export figures for 2020 fell behind 2019 in February, March, April and May. With the normalization process in June, July and Months, normalization started in export figures and exceeded the export values of 2019 in September, October, November and December. In 2020, which was entered with great hopes, it was not possible to reach the targeted figures this year due to the pandemic. In addition, Also, export values for 2020 and 2021 were predicted using a GM (1,1) grey forecasting model, which is a method frequently used in uncertainty cases. 2020 and 2021 export values were estimated by using the GM (1,1) gray forecasting model, which is a method frequently used in uncertainty situations. It has been seen that the model can be used reliably to predict natural stone export figures. In the following years, some assessments and recommendations have been made that may make the Turkish natural stone industry stronger in the following years on issues such as health management of crises and adaptation to the current situation if such outbreaks are replicated in the global world economy.

Kaynakça

  • Adgüzel, M., Şengüler, M., 2019. Investigation of Turkish Marble Sector and its Competitive Power. Third Sector Social Economic Review, 54 (3), 1530-1546.
  • Akay, D., Atak, M., 2007. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32 (9), 1670-1675.
  • Aydemir, E., Bedir, F., Ozdemir, G., 2013. The Grey System Approaches for Demand Forecasting. Journal of Trends in Development of Machinery and Associated Technology, 17 (1), 105-108.
  • Başol, K., Durman, M., Çelik, M.Y., 2005. Leading of Development Process; Natural Process. Journal of Social Sciences and Humanities Researches, 14, 61-71.
  • Carmona-Benítez, R.B., Nieto, M.R., 2020. SARIMA damp trend grey forecasting model for airline industry. Journal of Air Transport Management, 82, 101736.
  • Chatfield, C., 1988. Apples, oranges and mean squared error. International Journal of Forecasting, 4, 515–518.
  • Deng, J., 1989. Introduction to grey system theory. Journal of Grey System, 1, 1-24.
  • Ding, S., Hipel, K.W., Dang, Y.G., 2018. Forecasting China's electricity consumption using a new grey prediction model. Energy, 149, 314-328.
  • Ekincioğlu, G., Akbay, D., 2021. Değerlendirme: 2020 Yılı Türkiye Doğal Taş Sektörü. Türkiye 11. Uluslararası Mermer ve Doğal Taş Kongresi ve Sergisi, 10-11 Aralık 2021, s. 127-136, Diyarbakır.
  • Goodwin, P., Lawton, R., 1999. On the asymmetry of the symmetric MAPE. International Journal of Forecasting, 15 (4), 405-408.
  • Hamzaçebi, C., Es, H.A., 2014. Forecasting the annual electricity consumption of Turkey using an optimized grey model. Energy, 70, 165-171.
  • Hsu, C.C., Chen, C.Y., 2003. Applications of improved grey prediction model for power demand forecasting. Energy Conversion and management, 44 (14), 2241-2249.
  • IMIB (Istanbul Mineral Exporters Association), 2021, Mineral Export Reports by Product Groups or Countries on a Monthly Basis, available at: https://www.IMIB.org.tr/tr/raporlar/ihracat-istatistikleri (accessed 01 March 2021)
  • Kayacan, E., Ulutaş, B., Kaynak, O., 2010. Grey system theory-based models in time series prediction. Expert systems with applications, 37 (2), 1784-1789.
  • Kocaman, F., 2006. Natural Stone Sector and Marketing Strategies, Master Thesis, Dumlupınar University, Kütahya, Turkey.
  • Lewis, C.D., 1982. Industrial and business forecasting methods, London: Butterworths.
  • Liu, S., Lin, Y., 2006. Grey Information, London: Springer-Verlag.
  • Liu, S., Yang, Y., Forrest, J., 2017. Grey Data Analysis: Methods, Models and Applications, Singapore: Springer.
  • Liu, S., Yang, Y., Xie, N.F., 2016). New progress of grey system theory in the new millennium. Grey Systems: Theory and Application, 6 (1), 2-31.
  • Liu, X., Xie, N., 2019. A nonlinear grey forecasting model with double shape parameters and its application. Applied Mathematics and Computation, 360, 203-212.
  • Makridakis, S., 1993. Accuracy measures: theoretical and practical concerns. International Journal of Forecasting, 9, 527–529.
  • O’Connor, M., Remus, W., Griggs, K., 1997. Going up-going down: how good are people at forecasting trends and changes in trends? Journal of Forecasting, 16, 165–176.
  • TCKB (TR Ministry of Development), 2018. Eleventh Development Plan (2019-2023) Mining Policies Specialization Commission Report, Ankara, Turkey, Kb: 3041 - JMC: 822.
  • Wei, B.L., Xie, N.M., Yang, Y.J., 2019. Data-based structure selection for unified discrete grey prediction model. Expert Systems with Applications, 136, 264-275.
  • Yang, X., Zou, J., Kong, D., Jiang, G., 2018. The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China. Medicine, 97 (34), e11787.
  • Zhu, X., Dang, Y., Ding, S., 2021. Forecasting air quality in China using novel self-adaptive seasonal grey forecasting models. Grey Systems: Theory and Application, 11 (4), 596-618.

COVİD-19 PANDEMİSİNİN TÜRK DOĞAL TAŞ SEKTÖRÜNE ETKİLERİ: BİR GRİ TAHMİN MODELİ

Yıl 2022, Cilt: 10 Sayı: 2, 520 - 531, 30.06.2022
https://doi.org/10.21923/jesd.989253

Öz

Türkiye'de gelişen ve yenilenen ekonomik ortamda son yıllardaki göstergeler incelendiğinde, doğal taş sektörünün ivmesinin ve toplam madencilik ihracatı içindeki payının istikrarlı bir şekilde arttığı görülmektedir. Ancak 2020 yılında tüm dünyayı etkisi altına alan Covid-19 salgını, Türkiye doğal taş endüstrisini de etkilemiştir. Bu çalışma kapsamında hem pandemi öncesi hem de pandemi döneminde Türkiye doğal taş sektörünün aylık ve yıllık olarak ihracat değerleri değerlendirilmiştir. 2020 ve 2021 yılına ait ihracat rakamları Gri Tahmin modeli kullanılarak tahmin edilmeye çalışılmıştır. Türkiye maden ihracatının %50’lik kısmını Doğal Taş ihracatı oluşturmaktadır. Covid-19 pandemisinin etkisiyle 2020 yılı doğal taş ihracat rakamları Şubat, Mart, Nisan ve Mayıs aylarında 2019 yılının gerisinde kalmıştır. Haziran, Temmuz ve Ağustos aylarında normalleşme süreci ile birlikte ihracat rakamlarında normalleşme başlamış ve Eylül, Ekim, Kasım ve Aralık aylarında 2019 yılı ihracat değerlerinin aşmıştır. Büyük umutlarla girilen 2020 yılında pandemi nedeniyle bu yıl hedeflenen rakamlara ulaşmak mümkün olmamıştır. Ayrıca belirsizlik durumlarında sıklıkla kullanılan bir yöntem olan GM (1,1) gri tahmin modeli kullanılarak 2020 ve 2021 ihracat değerleri tahmin edilmiştir. Modelin doğal taş ihracat rakamlarını tahminde güvenilir olarak kullanılabileceği görülmüştür. İlerleyen yıllarda krizlerin sağlıklı yönetimi ve bu tür salgınların küresel dünya ekonomisinde tekrarlanması halinde mevcut duruma uyum gibi konularda önümüzdeki yıllarda Türkiye doğal taş sektörünü daha güçlü kılabilecek bazı değerlendirmeler ve önerilerde bulunulmuştur.

Kaynakça

  • Adgüzel, M., Şengüler, M., 2019. Investigation of Turkish Marble Sector and its Competitive Power. Third Sector Social Economic Review, 54 (3), 1530-1546.
  • Akay, D., Atak, M., 2007. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32 (9), 1670-1675.
  • Aydemir, E., Bedir, F., Ozdemir, G., 2013. The Grey System Approaches for Demand Forecasting. Journal of Trends in Development of Machinery and Associated Technology, 17 (1), 105-108.
  • Başol, K., Durman, M., Çelik, M.Y., 2005. Leading of Development Process; Natural Process. Journal of Social Sciences and Humanities Researches, 14, 61-71.
  • Carmona-Benítez, R.B., Nieto, M.R., 2020. SARIMA damp trend grey forecasting model for airline industry. Journal of Air Transport Management, 82, 101736.
  • Chatfield, C., 1988. Apples, oranges and mean squared error. International Journal of Forecasting, 4, 515–518.
  • Deng, J., 1989. Introduction to grey system theory. Journal of Grey System, 1, 1-24.
  • Ding, S., Hipel, K.W., Dang, Y.G., 2018. Forecasting China's electricity consumption using a new grey prediction model. Energy, 149, 314-328.
  • Ekincioğlu, G., Akbay, D., 2021. Değerlendirme: 2020 Yılı Türkiye Doğal Taş Sektörü. Türkiye 11. Uluslararası Mermer ve Doğal Taş Kongresi ve Sergisi, 10-11 Aralık 2021, s. 127-136, Diyarbakır.
  • Goodwin, P., Lawton, R., 1999. On the asymmetry of the symmetric MAPE. International Journal of Forecasting, 15 (4), 405-408.
  • Hamzaçebi, C., Es, H.A., 2014. Forecasting the annual electricity consumption of Turkey using an optimized grey model. Energy, 70, 165-171.
  • Hsu, C.C., Chen, C.Y., 2003. Applications of improved grey prediction model for power demand forecasting. Energy Conversion and management, 44 (14), 2241-2249.
  • IMIB (Istanbul Mineral Exporters Association), 2021, Mineral Export Reports by Product Groups or Countries on a Monthly Basis, available at: https://www.IMIB.org.tr/tr/raporlar/ihracat-istatistikleri (accessed 01 March 2021)
  • Kayacan, E., Ulutaş, B., Kaynak, O., 2010. Grey system theory-based models in time series prediction. Expert systems with applications, 37 (2), 1784-1789.
  • Kocaman, F., 2006. Natural Stone Sector and Marketing Strategies, Master Thesis, Dumlupınar University, Kütahya, Turkey.
  • Lewis, C.D., 1982. Industrial and business forecasting methods, London: Butterworths.
  • Liu, S., Lin, Y., 2006. Grey Information, London: Springer-Verlag.
  • Liu, S., Yang, Y., Forrest, J., 2017. Grey Data Analysis: Methods, Models and Applications, Singapore: Springer.
  • Liu, S., Yang, Y., Xie, N.F., 2016). New progress of grey system theory in the new millennium. Grey Systems: Theory and Application, 6 (1), 2-31.
  • Liu, X., Xie, N., 2019. A nonlinear grey forecasting model with double shape parameters and its application. Applied Mathematics and Computation, 360, 203-212.
  • Makridakis, S., 1993. Accuracy measures: theoretical and practical concerns. International Journal of Forecasting, 9, 527–529.
  • O’Connor, M., Remus, W., Griggs, K., 1997. Going up-going down: how good are people at forecasting trends and changes in trends? Journal of Forecasting, 16, 165–176.
  • TCKB (TR Ministry of Development), 2018. Eleventh Development Plan (2019-2023) Mining Policies Specialization Commission Report, Ankara, Turkey, Kb: 3041 - JMC: 822.
  • Wei, B.L., Xie, N.M., Yang, Y.J., 2019. Data-based structure selection for unified discrete grey prediction model. Expert Systems with Applications, 136, 264-275.
  • Yang, X., Zou, J., Kong, D., Jiang, G., 2018. The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China. Medicine, 97 (34), e11787.
  • Zhu, X., Dang, Y., Ding, S., 2021. Forecasting air quality in China using novel self-adaptive seasonal grey forecasting models. Grey Systems: Theory and Application, 11 (4), 596-618.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yer Bilimleri ve Jeoloji Mühendisliği (Diğer)
Bölüm Araştırma Makaleleri \ Research Articles
Yazarlar

Gökhan Ekincioğlu 0000-0001-9377-6817

Deniz Akbay 0000-0002-7794-5278

Erdal Aydemir 0000-0003-4834-725X

Yayımlanma Tarihi 30 Haziran 2022
Gönderilme Tarihi 1 Eylül 2021
Kabul Tarihi 20 Aralık 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 10 Sayı: 2

Kaynak Göster

APA Ekincioğlu, G., Akbay, D., & Aydemir, E. (2022). EFFECTS OF THE COVID-19 PANDEMIC ON TURKISH NATURAL STONE INDUSTRY: A GREY FORECASTING MODEL. Mühendislik Bilimleri Ve Tasarım Dergisi, 10(2), 520-531. https://doi.org/10.21923/jesd.989253