Zaim, Selim

Profil fotoğrafı
 E-posta Adresi
selim.zaim@ihu.edu.tr
ORCID Profili
0000-0003-3540-2264
WoS Profili
ABI-8229-2020
Scopus Profili
7004187773
YÖK Araştırmacı Profili
424F081C46C35BAA
Google Akademik Profili
0_S5vswAAAAJ&hl=tr
TR-Dizin Profili
596306
SOBİAD Profili
9095
Web Sitesi
akademik.ihu.edu.tr/tr/selim-zaim

Araştırma projeleri

Organizasyon Birimleri

Organizasyon Birimi Logosu
Organizasyon Birimi
Yönetim Bilimleri Fakültesi, İşletme Bölümü
Küresel rekabete ayak uydurmak ve sürdürülebilir olmak isteyen tüm şirketler ve kurumlar, değişimi doğru bir şekilde yönetmek, teknolojinin gerekli kıldığı zihinsel ve operasyonel dönüşümü kurumlarına hızlı bir şekilde adapte etmek zorundadırlar.

Adı Soyadı

Selim Zaim

İlgi Alanları

Multivariate Data Analysis, Data Analytics, Supply Chain Management, Operations Management

Kurumdaki Durumu

Aktif Personel

Filtreler

2019 - 201912020 - 20241
Filtreleri Sıfırla

Ayarlar

Yazar: Beyca, Ömer Faruk × İndeks: Scopus ×

Arama Sonuçları

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Yayın
    Using machine learning tools for forecasting natural gas consumption in the province of Istanbul
    (Elsevier, 2019) Beyca, Ömer Faruk; Ervural, Beyzanur Çayır; Tatoğlu, Ekrem; Özuyar, Pınar Gökçin; Zaim, Selim; Tatoğlu, Ekrem; Zaim, Selim; Yönetim Bilimleri Fakültesi, İşletme Bölümü
    Commensurate with unprecedented increases in energy demand, awell-constructed forecastingmodel is vital to managing energy policies effectively by providing energy diversity and energy requirements that adapt to the dynamic structure of the country. In this study, we employ three alternative popular machine learning tools for rigorous projection of natural gas consumption in the province of Istanbul, Turkey's largest natural gas-consuming mega-city. These tools include multiple linear regression (MLR), an artificial neural network approach (ANN) and support vector regression (SVR). The results indicate that the SVR is much superior to ANN technique, providing more reliable and accurate results in terms of lower prediction errors for time series forecasting of natural gas consumption. This study could well serve a useful benchmarking study for many emerging countries due to the data structure, consumption frequency, and consumption behavior of consumers in various time-periods.
  • Küçük Resim
    Yayın
    Link between digital technologies adoption and sustainability performance: Supply chain traceability/resilience or circular economy practices
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Duman Altan, Aylin; Beyca, Ömer Faruk; Zaim, Selim; Yönetim Bilimleri Fakültesi, İşletme Bölümü
    Technological progress and digitalization have ushered in significant transformations in business strategies. At present, research is scarcely focused on the influence of the adoption of digital technologies (DTs) on establishing comprehensive relationships within the context of a circular economy (CE), and the supply chain (SC) framework to contribute to the Resource-Based View (RBV) theory. This study utilizes survey data collected from 235 manufacturing practitioners employed by Turkish manufacturing enterprises to explore a model elucidating the relationship between DTs adoption and sustainability performance (SP) through supply chain traceability (SCT), supply chain resilience (SCR), and circular economy practices (CEPs), based on 10R strategies. Through this linkage, this research accentuates that the exclusive integration of CEPs with digital technology solutions is insufficient for industrial enterprises to attain their long-term sustainability goals. It underscores the necessity of ensuring SCT and/or SCR in this context.