Industry 4.0 Score Prediction of Turkish SMEs via Data Classification - DÜNDAR, Uğurcan; Gergin, Zeynep; ILHAN, DOGAN AYBARS; Güneş Gençyılmaz, Mehmet; Çavdarlı, Ali İhsan; YÜKSEKTEPE, FADİME ÜNEY (My University, 2019)
Todays most important industrial concept for companies is to be able to incorporate new technologies into their planning and monitoring systems. These technologies include sensor systems, cloud technologies, automation, predictive/pre-emptive maintenance, 3D printing, smart warehouses, etc. In order to survive in today’s competitive business environment, this revolution is very important for Small and Medium Sized Enterprises (SMEs) in Turkey. Hence, in order to analyze the relationship between new technologies and Industry 4.0 score of Turkish SMEs, a data mining study is performed in this research. A survey is performed to gather information on the awareness, readiness and interests of SMEs in new technologies in addition to their Industry 4.0 scores. Aim of this study is to predict whether Industry 4.0 scores of SMEs is low or high by using their technology utilizations. As this is a typical data classification problem, many different data classification methods are applied to determine the best alternative by using WEKA software. Among them, the highest prediction accuracy is 69.11%, obtained by Support Vector Machines. Thus, a Turkish SME’s Industry 4.0 score level could be predicted by just investigation of its new technology usage. Therefore, Turkish government could use this approach to determine the current situation of a SME. Moreover, government could determine their supporting programs based the technology usage levels of the SMEs.
Keywords: Industry 4.0; Data Mining; Data Classification; Support Vector Machines; Turkish Industry; Endüstri 4.0; Veri Madenciliği; Veri Sınıflandırması; Vektör Makineleri Desteklemek; Türk Endüstrisi