TR
Avrasya Ekonometri �statistik ve Ampirik Ekonomi DergisiYl:2019 Say: 14 Alan: Ekonometri

Kutluk Kaan SMER
ENDSTR 4.0 AISINDAN STATSTK MAKNE RENMES VE BYK VERNN FRMALARIN REKABET GCNE ETKSNN NCELENMES
 
Endstri 4.0'n nihai amac, makinelere, bileenlere ve devam eden almalara yerletirilmi her zaman bal sensrlerin a tabanl bilgi teknolojisi sistemlerine gerek zamanl veri iletmesidir. Bunlar da, bu byk verilerden analiz etmek ve bilgi edinmek ve sreleri gerektii gibi otomatik olarak ayarlamak iin makine renimi ve yapay zek algoritmalarn kullanlr. statistiki makine renmesi teknikleri, mevcut verilerden bilgi elde etmek iin tasarlanmtr. statistiki makine renmesi nemli bir lde istatistiki optimizasyon ve tahmin tekniklerinden temel ala gelmektedir. amzda zellikle istatistiki tekniklerle toplanan byk verinin istatistiki makine renme yntemleriyle analizi sonucunda bu yeni teknik ve yntemleri kullanan gerek imalat gerekse hizmet sektrndeki firmalar bu yeni tekniklere adapte olamayan firmalara gre yksek rekabet gc elde etmektedir. Bu almada Endstri 4.0 asndan istatistiki makine renmesi ve Byk Verinin Firmalarn Rekabet Gcne Etkisi incelenilmeye allmtr.

Anahtar Kelimeler: Endstri 4.0, istatistiki makine renmesi, Byk Veri, Rekabet, inovasyon, innovatif zmler


STATISTICAL MACHINE LEARNING IN TERMS OF INDUSTRY 4.0 AND INVESTIGATION OF THE IMPACT OF BIG DATA ON THE COMPETITIVENESS OF FIRMS
 
The ultimate goal of Industry 4.0 is to deliver real-time data to network-based information technology systems, which are always connected to machines, components and ongoing work. They use machine learning and artificial intelligence algorithms to analyze and obtain information from these big data and adjust processes automatically as needed. Statistical machine learning techniques are designed to extract information from existing data. Statistical machine learning is largely based on statistical optimization and forecasting techniques. As a result of the analysis of big data gathered by statistical techniques with statistical machine learning methods, both manufacturers and service sector companies using these new techniques and methods have higher competitive power compared to companies that cannot adapt to these new techniques. In this study, statistical machine learning in terms of Industry 4.0 and the effect of big data on the competitiveness of firms have been investigated.

Keywords: Industry 4.0, statistical machine learning, Big Data, Competition, innovation, innovative solutions


Detay

ÇERK