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THE IMPACT of DIGITAL TRANSFORMATION in MANUFACTURING SYSTEMS on WORK-STUDY TECHNIQUES

Yıl 2022, DİJİTAL DÖNÜŞÜM VE VERİMLİLİK, 110 - 122, 12.01.2022
https://doi.org/10.51551/verimlilik.987325

Öz

Purpose: With Industry 4.0, a rapid digitalization process has become inevitable for all businesses, including labor-intensive ones. On the other hand, traditional work-study techniques have a critical role in productivity measurement and monitoring. In this study, it is aimed that a research and evaluation study is conducted on the new role of work-study techniques in this digitalization process.

Methodology: In order to make this evaluation, a way of comprehensive literature survey and examination of real-world examples of handled techniques were followed.

Findings: When the response of work study techniques to digitalized business processes is examined, it is possible to say that traditional techniques have largely adapted to this process. It is seen that artificial intelligence techniques, play a critical role in the digitalization of businesses in the industrial revolution, also trigger the digitalization of work measurement techniques. In addition, it is seen that traditional work study techniques adapt to the changing needs of enterprises that arise during the transition to Industry 4.0 by integrating with artificial intelligence and other digital opportunities that come with Industry 4.0.

Originality: Examining the effect of digital transformation processes in production environments on traditional work-study techniques, analyzing the change, and besides this presenting a future projection for the related techniques make this study original.

Kaynakça

  • Akben, İ. ve Avşar, İ. (2018). “Endüstri 4.0 ve Karanlık Üretim: Genel Bir Bakış”, Türk Sosyal Bilimler Araştırmaları Dergisi, 3(1), 26-37.
  • Ashton, K. (2009). “That ‘İnternet of Things’ Thing”, RFID Journal, 22(7), 97-114.
  • Adizue, U.L., Nwanya, S.C. ve Ozor, P.A. (2020). “Artificial Neural Network Application to a Process Time Planning Problem for Palm Oil Production”, Engineering and Applied Science Research, 47(2), 161-169.
  • Aksoy, S. (2017). “Değişen Teknolojiler ve Endüstri 4.0: Endüstri 4.0’ı Anlamaya Dair Bir Giriş”, SAV Katkı Teknoloji, 34-44.
  • Alizon, F., Shooter, S. B., ve Simpson, T. W. (2009). “Henry Ford and the Model T: Lessons for Product Platforming and Mass Customization”, Design Studies, 30(5), 588-605.
  • Atalay, K. D., Eraslan, E. ve Cinar, M. O. (2015). “A Hybrid Algorithm Based on Fuzzy Linear Regression Analysis by Quadratic Programming for Time Estimation: An Experimental Study in Manufacturing Industry”, Journal of Manufacturing Systems, 36, 182-188.
  • Boyes, H., Hallaq, B., Cunningham, J. ve Watson, T. (2018). “The Industrial Internet of Things (IIoT): An Analysis Framework”, Computers in Industry, 101, 1-12.
  • Busıness Advantage Group (2019). “2018-19 Global CAD Trends”, https://www.business-advantage.com/2018-19%20Global%20CAD%20Trends%20-%20Version%201.0%20DOWNLOAD.pdf, (Erişim Tarihi: 24.08.2021).
  • Calp, M.H., Bahçekapılı, E. ve Berigel, M. (2018). “Endüstri 4.0 Kapsamında Akıllı Fabrikaların İncelenmesi”, The Fifth International Management Information Systems Conference, October 24-26 2018, Ankara.
  • Çakıt, E., Adem, A. ve Dağdeviren, M. (2020). “Endüstri 4.0 Ergonomi için Tehdit mi Fırsat mı?”, Verimlilik Dergisi, 3, 43-57.
  • Dağdeviren, M., Eraslan, E. ve Çelebi, F. V. (2011). “An Alternative Work Measurement Method and Its Application to a Manufacturing Industry”, Journal of Loss Prevention in the Process Industries, 24(5), 563-567.
  • Dombrowsi, U. ve Wagner, T. (2014). “Mental Strain as Field of Action, In the 4th Industrial Revolution Variety Management in Manufacturing”, Proceedia CIRP,17, 100-105.
  • Ekinci, N. (2019). “Klasik, Neoklasik Teori, Sistem ve Durumsallık Yaklaşımları ile Bunların Karşılaştırılması ve Toplam Kalite Yönetimi İçerisindeki Yerlerinin Değerlendirilmesi”, Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 6(11), 16-38.
  • Eraslan, E. (2009). “The Estimation of Product Standard Time by Artificial Neural Networks in the Molding Industry”, Mathematical Problems in Engineering, 1-12.
  • Ghafoorpoor Yazdi, P., Azizi, A. ve Hashemipour, M. (2018). “An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach”, Sustainability, 10(9), 3031.
  • Ghafoorpoor Yazdi, P., Azizi, A. ve Hashemipour, M. (2019). “A Hybrid Methodology for Validation of Optimization Solutions Effects on Manufacturing Sustainability with Time Study and Simulation Approach for SMEs”, Sustainability, 11(5), 1454.
  • Günay, D. (2002), “Sanayi ve Sanayi Tarihi”, Mimar ve Mühendis Dergisi, 31, 8-14.
  • Hemalatha, A., Kumari, P.B., Nawaz, N. ve Gajenderan, V. (2021). “Impact of Artificial Intelligence on Recruitment and Selection of Information Technology Companies”, 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), March 2021, 60-66.
  • Herter J. ve Ovtcharova, J. (2016). “A Model Based Visualization Framework for Cross Discipline Collaboration in Industry 4.0 Scenarios”, Procedia CIRP, 57, 398-403.
  • Hmoud, B. ve Laszlo, V. (2019). “Will Artificial Intelligence Take Over Humanresources Recruitment and Selection?”, Network Intelligence Studies, 7(13), 21-30.
  • Kagermann, H., Wahlster, W. ve Helbig, J. (2013). “Recommendations for Implementing the Strategic Initiative Industrie 4.0: Securing the Future of German Manufacturing Industry”, Final Report of the Industrie 4.0 Working Group, Forschungsunion.
  • Kılıç, S. ve Alkan, R. (2018). “Dördüncü Sanayi Devrimi Endüstri 4.0: Dünya ve Türkiye Değerlendirmeleri”, Girişimcilik İnovasyon ve Pazarlama Araştırmaları Dergisi, 2(3), 29-49.
  • Kitana, A. (2016). “Overview of the Managerial Thoughts and Theories from the History: Classical Management Theory to Modern Management Theory”, Indian Journal of Management Science, 6(1), 16-19. Kurt, M. ve Dağdeviren, M. (2003). “İş Etüdü”, Gazi Kitabevi, Ankara.
  • Muhlhäuser, M. (2007). “Smart Products: An Introduction”, AMI 2007 Workshops European Conference on Ambient Intelligence, November, Springer-Verlag Berlin Heidelberg, 158-164.
  • Mahmood, Z. ve Basharad, M. (2012). “Review of Classical Management Theories”, International Journal of Social Sciences and Education, 2(1), 514-517.
  • Meyers, F.E. ve Stewart, J.R. (2002). “Motion and Time Study for Lean Manufacturing, Upper Saddle River”, Prentice Hall, New Jersey.
  • Nunes, M.L., Pereira, A.C. ve Alves, A.C. (2017). “Smart Products Development Approaches for Industry 4.0”, Procedia Manufacturing, 13, 1215-1222.
  • Pamuk, N. ve Soysal, M. (2018). “Yeni Sanayi Devrimi Endüstri 4.0 Üzerine Bir İnceleme”, Verimlilik Dergisi, 1, 41-66.
  • Prokopenko, J. (2001). “Verimlilik Yönetimi: Uygulamalı El Kitabı”, MPM Yayınları, Ankara.
  • Oesterreich, T.D. ve Teuteberg, F. (2016). “Understanding the Implications of Digitisation and Automation in the Context of Industry 4.0: A Triangulation Approach and Elements of a Research Agenda for the Con-Struction Industry”, Computers in Industry, 83, 121-139.
  • Rijsdijk, S.A. ve Hultink, E.J. (2009). “How Today's Consumers Perceive Tomorrow's Smart Products”, Journal of Product Innovation Management, 26(1), 24-42.
  • Redclift, M. (2005). “Sustainable Development (1987-2005): An Oxymoron Comes of Age”, Sustainable Development, 13(4), 212-227.
  • Sarıkulak, Ö. (2018). “Endüstri Devrimlerinin Performans Göstergelerine Etkilerinin İncelenmesi ile Endüstri 4.0 Analizi”, Yüksek Lisans Tezi, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Sayar, S. (2019). “Dijitalleşme ile Yeni Oluşan Kavramlar Endüstri 4.0, IOT ve Blockchain Uygulamaları”, Yüksek Lisans Tezi, Maltepe Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
  • Schneider, S. (2016). “The Industrial Internet of Things (IIoT)”, Internet of Things and Data Analytics Handbook, Editör: Geng, H., Wiley, 41-81.
  • Shao, Y., Ji, X., Zheng, M. ve Chen, C. (2021). “Prediction of Standard Time of the Sewing Process Using a Support Vector Machine with Particle Swarm Optimization”, AUTEX Research Journal, DOI: 10.2478/aut-2021-0037. Sinan, A. (2016). “Üretim İçin Yeni Bir İzlek: Sanayi 4.0”, Journal of Life Economics, 8, 19-30.
  • Susanto, S., Tanaya, P.I. ve Soembagijo, A.S. (2012). “Formulating Standard Product Lead Time at a Textile Factory Using Artificial Neural Networks”, 2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering, 99-104.
  • Thames, L. ve Schaefer, D. (2016). “Software-Defined Cloud Manufacturing for Industry 4.0”, Procedia CIRP, 52, 12-17.
  • Tunzelmann, N.V. (2003). “Historical Coevolution of Governance and Technology in the Industrial Revolutions”, Structural Change and Economic Dynamics, 14(4), 365-384.
  • TÜSİAD (1994). “Türkiye’de ve Dünyada Yükseköğretim Bilim ve Teknoloji”, Yayın No. 94.6-167, İstanbul.
  • Westbrook, J.I. ve Ampt, A. (2009). “Design, Application and Testing of the Work Observation Method by Activity Timing (WOMBAT) to Measure Clinicians’ Patterns of Work and Communication”, International Journal of Medical Informatics, 78, 25-33.
  • Wierschem, D.C., Jimenez, J.A. ve Mediavilla, F.A.M. (2020). “A Motion Capture System for the Study of Human Manufacturing Repetitive Motions”, The International Journal of Advanced Manufacturing Technology, 110(3), 813-827.
  • Yıldız, A. (2018). “Endüstri 4.0 ve Akıllı Fabrikalar”, Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(2), 546-556.
  • Yılmaz Kaya, B. ve Dağdeviren, M. (2019a). “Strategy Selection for Smoothing the Transition Period of Industry 4.0 Applications Implementation”, 10th International Symposium on Intelligent Manufacturing and Service Systems, Sakarya, Türkiye, 728-737.
  • Yılmaz Kaya, B. ve Dağdeviren, M. (2019b). “A Guiding Analysis to Accomplish the Challenges for Implementation of Industry 4.0”, 10th International Symposium on Intelligent Manufacturing and Service Systems, Sakarya, Türkiye, 738-746.

ÜRETİM SİSTEMLERİNDEKİ DİJİTAL DÖNÜŞÜMÜN İŞ ETÜDÜ TEKNİKLERİ ÜZERİNDEKİ ETKİSİ

Yıl 2022, DİJİTAL DÖNÜŞÜM VE VERİMLİLİK, 110 - 122, 12.01.2022
https://doi.org/10.51551/verimlilik.987325

Öz

Amaç: Endüstri 4.0 ile hızlı bir dijitalleşme süreci emek yoğun işletmeler le birlikte tüm işletmeler için kaçınılmaz olmuştur. Öte yandan geleneksel iş etüdü teknikleri verimlilik ölçüm ve izlemede oldukça kritik bir role sahiptir. Bu çalışmada iş etüdü tekniklerinin dijitalleşme sürecindeki rolü ve değişimine yönelik bir araştırma ve değerlendirme yapılması amaçlanmıştır.

Yöntem: İlgili değerlendirmeyi yapmak için ele alınan tekniklere ilişkin kapsamlı literatür araştırması ve gerçek dünya örneklerinin incelenmesi şeklinde bir yol izlenmiştir.

Bulgular: Dijitalleşen iş süreçleri karşısında iş etüdü tekniklerinin gösterdiği tepki incelendiğinde, geleneksel tekniklerinin bu dönüşüm sürecine büyük oranda adapte olduğu söylenebilir. Endüstri devriminde işletmelerin dijitalleşmesinde kritik rol oynayan yapay zekâ tekniklerinin iş ölçümü tekniklerindeki dijitalleşmeyi de tetiklediği görülmektedir. Dahası, geleneksel iş etüdü tekniklerinin, işletmelerin Endüstri 4.0’a geçiş süreçlerinde ortaya çıkan ihtiyaçlarına yapay zekâ ve Endüstri 4.0 ile birlikte gelen diğer dijital teknikler ile bütünleşerek uyum sağladığı görülmektedir.

Özgünlük: Üretim ortamlarındaki dijital dönüşüm süreçlerinin geleneksel iş etüdü teknikleri üzerindeki etkisinin incelenmesi, değişimin analiz edilmesi, bununla birlikte ilgili tekniklere yönelik bir gelecek projeksiyonunun sunulması bu çalışmayı özgün kılmaktadır.

Kaynakça

  • Akben, İ. ve Avşar, İ. (2018). “Endüstri 4.0 ve Karanlık Üretim: Genel Bir Bakış”, Türk Sosyal Bilimler Araştırmaları Dergisi, 3(1), 26-37.
  • Ashton, K. (2009). “That ‘İnternet of Things’ Thing”, RFID Journal, 22(7), 97-114.
  • Adizue, U.L., Nwanya, S.C. ve Ozor, P.A. (2020). “Artificial Neural Network Application to a Process Time Planning Problem for Palm Oil Production”, Engineering and Applied Science Research, 47(2), 161-169.
  • Aksoy, S. (2017). “Değişen Teknolojiler ve Endüstri 4.0: Endüstri 4.0’ı Anlamaya Dair Bir Giriş”, SAV Katkı Teknoloji, 34-44.
  • Alizon, F., Shooter, S. B., ve Simpson, T. W. (2009). “Henry Ford and the Model T: Lessons for Product Platforming and Mass Customization”, Design Studies, 30(5), 588-605.
  • Atalay, K. D., Eraslan, E. ve Cinar, M. O. (2015). “A Hybrid Algorithm Based on Fuzzy Linear Regression Analysis by Quadratic Programming for Time Estimation: An Experimental Study in Manufacturing Industry”, Journal of Manufacturing Systems, 36, 182-188.
  • Boyes, H., Hallaq, B., Cunningham, J. ve Watson, T. (2018). “The Industrial Internet of Things (IIoT): An Analysis Framework”, Computers in Industry, 101, 1-12.
  • Busıness Advantage Group (2019). “2018-19 Global CAD Trends”, https://www.business-advantage.com/2018-19%20Global%20CAD%20Trends%20-%20Version%201.0%20DOWNLOAD.pdf, (Erişim Tarihi: 24.08.2021).
  • Calp, M.H., Bahçekapılı, E. ve Berigel, M. (2018). “Endüstri 4.0 Kapsamında Akıllı Fabrikaların İncelenmesi”, The Fifth International Management Information Systems Conference, October 24-26 2018, Ankara.
  • Çakıt, E., Adem, A. ve Dağdeviren, M. (2020). “Endüstri 4.0 Ergonomi için Tehdit mi Fırsat mı?”, Verimlilik Dergisi, 3, 43-57.
  • Dağdeviren, M., Eraslan, E. ve Çelebi, F. V. (2011). “An Alternative Work Measurement Method and Its Application to a Manufacturing Industry”, Journal of Loss Prevention in the Process Industries, 24(5), 563-567.
  • Dombrowsi, U. ve Wagner, T. (2014). “Mental Strain as Field of Action, In the 4th Industrial Revolution Variety Management in Manufacturing”, Proceedia CIRP,17, 100-105.
  • Ekinci, N. (2019). “Klasik, Neoklasik Teori, Sistem ve Durumsallık Yaklaşımları ile Bunların Karşılaştırılması ve Toplam Kalite Yönetimi İçerisindeki Yerlerinin Değerlendirilmesi”, Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 6(11), 16-38.
  • Eraslan, E. (2009). “The Estimation of Product Standard Time by Artificial Neural Networks in the Molding Industry”, Mathematical Problems in Engineering, 1-12.
  • Ghafoorpoor Yazdi, P., Azizi, A. ve Hashemipour, M. (2018). “An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach”, Sustainability, 10(9), 3031.
  • Ghafoorpoor Yazdi, P., Azizi, A. ve Hashemipour, M. (2019). “A Hybrid Methodology for Validation of Optimization Solutions Effects on Manufacturing Sustainability with Time Study and Simulation Approach for SMEs”, Sustainability, 11(5), 1454.
  • Günay, D. (2002), “Sanayi ve Sanayi Tarihi”, Mimar ve Mühendis Dergisi, 31, 8-14.
  • Hemalatha, A., Kumari, P.B., Nawaz, N. ve Gajenderan, V. (2021). “Impact of Artificial Intelligence on Recruitment and Selection of Information Technology Companies”, 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), March 2021, 60-66.
  • Herter J. ve Ovtcharova, J. (2016). “A Model Based Visualization Framework for Cross Discipline Collaboration in Industry 4.0 Scenarios”, Procedia CIRP, 57, 398-403.
  • Hmoud, B. ve Laszlo, V. (2019). “Will Artificial Intelligence Take Over Humanresources Recruitment and Selection?”, Network Intelligence Studies, 7(13), 21-30.
  • Kagermann, H., Wahlster, W. ve Helbig, J. (2013). “Recommendations for Implementing the Strategic Initiative Industrie 4.0: Securing the Future of German Manufacturing Industry”, Final Report of the Industrie 4.0 Working Group, Forschungsunion.
  • Kılıç, S. ve Alkan, R. (2018). “Dördüncü Sanayi Devrimi Endüstri 4.0: Dünya ve Türkiye Değerlendirmeleri”, Girişimcilik İnovasyon ve Pazarlama Araştırmaları Dergisi, 2(3), 29-49.
  • Kitana, A. (2016). “Overview of the Managerial Thoughts and Theories from the History: Classical Management Theory to Modern Management Theory”, Indian Journal of Management Science, 6(1), 16-19. Kurt, M. ve Dağdeviren, M. (2003). “İş Etüdü”, Gazi Kitabevi, Ankara.
  • Muhlhäuser, M. (2007). “Smart Products: An Introduction”, AMI 2007 Workshops European Conference on Ambient Intelligence, November, Springer-Verlag Berlin Heidelberg, 158-164.
  • Mahmood, Z. ve Basharad, M. (2012). “Review of Classical Management Theories”, International Journal of Social Sciences and Education, 2(1), 514-517.
  • Meyers, F.E. ve Stewart, J.R. (2002). “Motion and Time Study for Lean Manufacturing, Upper Saddle River”, Prentice Hall, New Jersey.
  • Nunes, M.L., Pereira, A.C. ve Alves, A.C. (2017). “Smart Products Development Approaches for Industry 4.0”, Procedia Manufacturing, 13, 1215-1222.
  • Pamuk, N. ve Soysal, M. (2018). “Yeni Sanayi Devrimi Endüstri 4.0 Üzerine Bir İnceleme”, Verimlilik Dergisi, 1, 41-66.
  • Prokopenko, J. (2001). “Verimlilik Yönetimi: Uygulamalı El Kitabı”, MPM Yayınları, Ankara.
  • Oesterreich, T.D. ve Teuteberg, F. (2016). “Understanding the Implications of Digitisation and Automation in the Context of Industry 4.0: A Triangulation Approach and Elements of a Research Agenda for the Con-Struction Industry”, Computers in Industry, 83, 121-139.
  • Rijsdijk, S.A. ve Hultink, E.J. (2009). “How Today's Consumers Perceive Tomorrow's Smart Products”, Journal of Product Innovation Management, 26(1), 24-42.
  • Redclift, M. (2005). “Sustainable Development (1987-2005): An Oxymoron Comes of Age”, Sustainable Development, 13(4), 212-227.
  • Sarıkulak, Ö. (2018). “Endüstri Devrimlerinin Performans Göstergelerine Etkilerinin İncelenmesi ile Endüstri 4.0 Analizi”, Yüksek Lisans Tezi, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Sayar, S. (2019). “Dijitalleşme ile Yeni Oluşan Kavramlar Endüstri 4.0, IOT ve Blockchain Uygulamaları”, Yüksek Lisans Tezi, Maltepe Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
  • Schneider, S. (2016). “The Industrial Internet of Things (IIoT)”, Internet of Things and Data Analytics Handbook, Editör: Geng, H., Wiley, 41-81.
  • Shao, Y., Ji, X., Zheng, M. ve Chen, C. (2021). “Prediction of Standard Time of the Sewing Process Using a Support Vector Machine with Particle Swarm Optimization”, AUTEX Research Journal, DOI: 10.2478/aut-2021-0037. Sinan, A. (2016). “Üretim İçin Yeni Bir İzlek: Sanayi 4.0”, Journal of Life Economics, 8, 19-30.
  • Susanto, S., Tanaya, P.I. ve Soembagijo, A.S. (2012). “Formulating Standard Product Lead Time at a Textile Factory Using Artificial Neural Networks”, 2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering, 99-104.
  • Thames, L. ve Schaefer, D. (2016). “Software-Defined Cloud Manufacturing for Industry 4.0”, Procedia CIRP, 52, 12-17.
  • Tunzelmann, N.V. (2003). “Historical Coevolution of Governance and Technology in the Industrial Revolutions”, Structural Change and Economic Dynamics, 14(4), 365-384.
  • TÜSİAD (1994). “Türkiye’de ve Dünyada Yükseköğretim Bilim ve Teknoloji”, Yayın No. 94.6-167, İstanbul.
  • Westbrook, J.I. ve Ampt, A. (2009). “Design, Application and Testing of the Work Observation Method by Activity Timing (WOMBAT) to Measure Clinicians’ Patterns of Work and Communication”, International Journal of Medical Informatics, 78, 25-33.
  • Wierschem, D.C., Jimenez, J.A. ve Mediavilla, F.A.M. (2020). “A Motion Capture System for the Study of Human Manufacturing Repetitive Motions”, The International Journal of Advanced Manufacturing Technology, 110(3), 813-827.
  • Yıldız, A. (2018). “Endüstri 4.0 ve Akıllı Fabrikalar”, Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(2), 546-556.
  • Yılmaz Kaya, B. ve Dağdeviren, M. (2019a). “Strategy Selection for Smoothing the Transition Period of Industry 4.0 Applications Implementation”, 10th International Symposium on Intelligent Manufacturing and Service Systems, Sakarya, Türkiye, 728-737.
  • Yılmaz Kaya, B. ve Dağdeviren, M. (2019b). “A Guiding Analysis to Accomplish the Challenges for Implementation of Industry 4.0”, 10th International Symposium on Intelligent Manufacturing and Service Systems, Sakarya, Türkiye, 738-746.
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Aylin Adem 0000-0003-4820-6684

Burcu Yılmaz Kaya 0000-0002-5088-5842

Erman Çakıt 0000-0003-0974-5941

Metin Dağdeviren 0000-0003-2121-5978

Yayımlanma Tarihi 12 Ocak 2022
Gönderilme Tarihi 26 Ağustos 2021
Yayımlandığı Sayı Yıl 2022 DİJİTAL DÖNÜŞÜM VE VERİMLİLİK

Kaynak Göster

APA Adem, A., Yılmaz Kaya, B., Çakıt, E., Dağdeviren, M. (2022). ÜRETİM SİSTEMLERİNDEKİ DİJİTAL DÖNÜŞÜMÜN İŞ ETÜDÜ TEKNİKLERİ ÜZERİNDEKİ ETKİSİ. Verimlilik Dergisi110-122. https://doi.org/10.51551/verimlilik.987325

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