ENDÜSTRİ 4.0 DÖNÜŞÜMÜNDE İNSAN FAKTÖRLERİ MÜHENDİSLİĞİ: IOT TEMELLİ TEKNOLOJİLER ANALIZİ

Endüstri 4.0 felsefesi ve tamamlanmya çalışılan dönüşüm çerçevesinde insan faktörleri mühendisliği (İFM) süreçlerinde ileri teknolojilerin ve cihazların kullanılması uluslararası arenada yükselen bir trend haline gelmektedir. Beraberinde birçok avantaj getiren geleneksel İFM uygulamalarından bu yeni teknolojiye geçiş, aynı zamandada değişen boyutlarda farklı yönde çelişen çok sayıda yeni kriterin analizini de ifade etmektedir. Bu çalışma bu probleme odaklanmakta ve incelenen problem uzayını bulanık küme teorisi, Delphi yöntemi ve En İyi-En Kötü Yöntemini (Best-Worst Method - BWM) birlikte kullanarak Ergonomi 4.0 dönüşüm sürecini kolaylaştırmak adına araştırmaktadır. Endüstri 4.0 ile ortaya çıkan cihaz temelli ergonomik değerlendirme, iş sağlığı ve güvenliği uygulamaları, ve fiziksel çevre takibi için kullanılabilen yeni teknolojiler ve cihazlar çalışmanın Ergonomi 4.0 literatürüne bir diğer katkısı olarak ele alınmıştır. İncelenen zorlu karar yapılarına uygun olarak önerilen değerlendirme çerçevesi için gerçekleştirilen derinlemesine literatür araştırması sonuçları bulanık Delphi Metodu (FDM) ile analiz edilerek geçerli kılınan kriter kümesi belirlenmiştir. Daha sonra doğrulalan kriterler listesi bir geçiş süreci yol haritası önermek adına için BWM yöntemi ile ele alnışmıştır. Problemin ana ve alt kriterleri karar hiyerarşisine uygun olarak irdelenmiş; yerel ve genel önem seviyeleri ve karar verme sürecinin farklı taraflarına ilişkin çıktılar karşılaştırmalı olarak detayları ile yorumlanmıştır.

HUMAN FACTORS ENGINEERING ON THE EDGE OF INDUSTRY 4.0: ANALYSIS FOR IOT-AIDED TECHNOLOGIES

Using advanced technologies and devices in human factors engineering (HFE) processes is becoming a rising trend in international arena, regarding Industry 4.0 philosophy and transformation consummation. Transition to this new technology from traditional HFE applications offers many advantages but also refers to the analysis of a very complex set of numerous emerging criteria conflicting in varying directions and dimensions. This study focuses on that enigma and investigates the problem space to facilitate Ergonomics 4.0 transformation process with the employment of fuzzy sets theory, Delphi method and Best-Worst Method (BWM). New technologies and devices introduced within Industry 4.0 era for instrument based ergonomic assessment, occupational health and safety applications, and, physical environment monitoring were addressed as another contribution of this study to Ergonomics 4.0 aspect. An evaluation framework apropos of related challenging decision structures was proposed in the wake of in-depth literature analysis, where, the validated criteria set was clarified with fuzzy Delphi Method. The elucidated criteria list was than observed with BWM to propose a transition period charter. Main and sub-criteria of the problem were scrutinized according to decision hierarchy; local and global importance levels of criteria, and, outcomes regarding different parties of the decision making process were interpreted comparatively in details, and suggestions has been made in the light of multi-dimensional benchmarking debates.

Kaynakça

Abdel-Basset, M., Mohamed, M., Chang, V. & Smarandache, F. (2019). IoT and its impact on the electronics market: A powerful decision support system for helping customers in choosing the best product. Symmetry, 11(5) 611, 1-21.

Abdel-Basset, M., Manogaran, G. & Gamal, A. (2019). A group decision making framework based on neutrosophic TOPSIS approach for smart medical device selection. J Med Syst, 43(38), 1-13.

Adem, A., Yılmaz Kaya, B., Çakıt, E. & Dağdeviren, M. (2022). Üretim sistemlerindeki dijital dönüşümün iş etüdü teknikleri üzerindeki etkisi. Verimlilik Dergisi. In press.

Asadi, F. & Arjmand, N. (2020). Marker-less versus marker-based driven musculoskeletal models of the spine during static load-handling activities. Journal of Biomechanics, 112

Balog, A., Băjenaru, L. & Cristescu, I. (2019). Analyzing the factors affecting the quality of IoT-based smart wearable devices using the DANP method. Studies in Informatics and Control, 28(4), 431-442.

Bharathi, S. V. (2019). Forewarned is forearmed: Assessment of IoT information security risks using analytic hierarchy process. Benchmarking: An International Journal, 26(8), 2443-2467.

Büyüközkan, G. & Göçer, F. (2019) Smart medical device selection based on intuitionistic fuzzy Choquet integral. Soft Computing, 23, 10085–10103.

Büyüközkan, M. & Güler, M. (2020) Smart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique. Measurement, 153, 107353.

Cerqueira, S. M., Ferreira da Silva, A. & Santos, C. P. (2019). Instrument-based ergonomic assessment: A perspective on the current state of art and future trends. IEEE 6th Portuguese Meeting on Bioengineering, 1-4.

Chaari, M. Z., Abdelfatah, M., Loreno, C., & Al-Rahimi, R. (2021). Development of air conditioner robot prototype that follows humans in outdoor applications. Electronics, 10(14).

Chang, D.Y. (1996). Application of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655.

Chebel, E., & Tunc, B. (2021). Deep neural network approach for estimating the three-dimensional human center of mass using joint angles. Journal of Biomechanics, 126

Dağdeviren, M. (2007). Bulanik Analitik Hiyerarşi Prosesi ile personel seçimi ve bir uygulama. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 22 (4)

Dağdeviren, M. & Yüksel, İ. (2008). Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management. Information sciences, 178 (6), 1717-1733

Eldar, R. & Fisher-Gewirtzman, D. (2020). E-worker postural comfort in the third-workplace: An ergonomic design assessment. Work, 66(3), 519-538.

Gao, Y., Li, H. & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723.

Giles, M. (2019) “Volkswagen Is Accelerating One Of The World’s Biggest Smart-Factory Projects”, Forbes, https://www.forbes.com/sites/martingiles/2019/12/02/vw-cio-drives-digital-manufacturing/.

González-Cañete, F. J. & Casilari, E. (2021). A feasibility study of the use of smartwatches in wearable fall detection systems. Sensors, 21(6).

He, Y. (2021). Fast job recognition and sorting based on image processing. Traitement Du Signal, 38(2), 421-429.

Hinduja, A. & Pandey, M. (2020). An ANP-GRA-based evaluation model for security features of IoT systems. Advances in Intelligent Systems and Computing, 989, 243-253.

Hsiao, K. L. & Chen, C. C. (2018). What drives smartwatch purchase intention? Perspectives from hardware, software, design, and value. Telematics and Informatics, 35(1), 103-113.

Ishikawa, A., Amagasa, M., Shiga, T,, Tomizawa, G., Tatsuta, R. & Mieno, H. (1993). The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets Syst. 55 (3), 241–253.

Ivaschenko, A., Sitnikov, P. & Krivosheev, A. (2018). AR guide for a robot design kit. 2nd Annual Science Fiction Prototyping Conference, 41-45.

Jeong, S. C., Kim, S. H., Park, J., Choi, B. (2017). Domain-specific innovativeness and new product adoption: A case of wearable devices Telematics and Informatics, 34(5), 399-412.

Kılıç Delice, E. (2016). A fuzzy multicriteria model for airline companies selection. Journal of the Faculty of Engineering and Architecture of Gazi University, 31.

Kılıç Delice, E. & Can, G. F. (2020) A new approach for ergonomic risk assessment integrating KEMIRA, Best-worst and MCDM methods. Soft Computing, 14(313), 15093-15111.

Lee, A.H.I., Wang, W. & Lin, T. (2010). An evaluation framework for technology transfer of new equipment in high technology industry. Technological Forecasting and Social Change, 77(1), 135–150.

Lennefer, T., Reis, D., Lopper, E. & Hoppe, A. (2020). A step away from impaired well-being: A latent growth curve analysis of an intervention with activity trackers among employees. European Journal of Work and Organizational Psychology, 29(5), 664-677.

Ly, P. T. M., Lai, W. H., Hsu, C. W. & Shih, F. Y. (2018). Fuzzy AHP analysis of Internet of Things (IoT) in enterprises. Technological Forecasting and Social Change, 136(C), 1-13.

Ma, L., Wu, R., Miao, H., Fan, X., Kong, L., Patil, A. & Wang, J. (2021). All-in-one fibrous capacitive humidity sensor for human breath monitoring. Textile Research Journal, 91(3-4), 398-405.

Mashal, I & Alsaryrah, O. (2019). Fuzzy analytic hierarchy process model for multi-criteria analysis of internet of things. Kybernetes, 0368-492X, 1-12.

Mashal, I., Alsaryrah, O., Chung, T.Y. & Yuan, F.C. (2020). A multi-criteria analysis for an internet of things application recommendation system. Technology in Society, 60(101216), 1-8.

Mudiyanselage, S. E., Nguyen, P. H. D., Rajabi, M. S. & Akhavian, R. (2021). Automated workers’ ergonomic risk assessment in manual material handling using sEMG wearable sensors and machine learning. Electronics, 10(20)

Murray, T. J., Pipino, L. L., Gigch, V. & John P. (1985). A pilot study of fuzzy set modification of Delphi. Hum. Syst. Management 5, 76–80.

Özgüner Kılıç, H. (2017). Giyilebilir Teknoloji Ürünleri Pazarı ve Kullanım Alanları. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(4), 99-112.

Padilla-Rivera, A., Telles do Carmo, B. B., Arcese, G. & Merveille, N. (2021). Social circular economy indicators: Selection through fuzzy Delphi method. Sustainable Production and Consumption, 26, 101-110.

Park, K. C. & Shin, D. H. (2017). Security assessment framework for IoT service, Telecomm. Systems, 64(1), 193-209.

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.

Rezaei, J. (2016). Best-worst multi-criteria decision-making method: some properties and a linear model. Omega, 64, 126-30.

Rijsdijk, S.A. & Hultink, E.J. (2009), How Today's Consumers Perceive Tomorrow's Smart Products. Journal of Product Innovation Management, 26, 24-42.

Sanchez, J. & Gomez, A. T. (2003). Applications of fuzzy regression in actuarial analysis. The Journal of Risk and Insurance, 70(4), 665-699.

Sotoudeh-Anvari, A., Sadjadi, S. J., Molana, S. M. H. & Sadi-Nezhad, S. (2018) A new MCDM-based approach using BWM and SAW for optimal search model. Decision Science Letters, 7, 395-404.

Takahashi, K. & Sakata, O. (2021). Development of nose detection and an infrared image matching system for mental fatigue evaluation. IEEE 3rd Global Conference on Life Sciences and Technologies, 220-221.

Wang, M., Zhang, S., Lv, Y. & Lu, H. (2018). Anxiety level detection using BCI of miner’s smart helmet. Mobile Networks and Applications, 23(2), 336-343.

Yang, H., Yu, J., Zo, H. & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256-269.

Yang, X., Yu, Y., Shirowzhan, S., Sepasgozer, S. & Li, H. (2020). Automated PPE-tool pair check system for construction safety using smart IoT. Journal of Building Engineering, 32

Ye, Q. & Gao, S. (2014). AHP-based evaluation of IoT-aided stadium information system. The Open Cybernetics&Systemics Journal, 8, 594-600.

Yılmaz Kaya, B. & Dağdeviren, M. (2016). Selecting Occupational Safety Equipment by MCDM Approach Considering Universal Design Principles. Human Factors and Ergonomics in Manufacturing & Service Industries, 26, 224-242.

Yılmaz Kaya, B. & Dağdeviren, M. (2019). Strategy Selection for Smoothing the Transition Period of Industry 4.0 Applications Implementation. 10th International Symposium on Intelligent Manufacturing and Service Systems, 728–737.

Yılmaz Kaya, B., Adem, A. & Dağdeviren, M. (2021a) Multi-criteria Approach to Usability Research for Digital Platforms in Fuzzy Environment. Intelligent and Fuzzy Systems Conference, 417-425.

Yılmaz Kaya, B., Adem, A. & Dağdeviren, M. (2021b). Multi-crıteria assessment framework for freight villages based on operational efficiency criteria. 19th International Logistics and Supply Chain Congress, In press.

Zadeh, L.A. (1965), Fuzzy sets. Inf. Control, 8, 338–353

Zimmermann, H. J. (1990). Fuzzy Set Theory and Its Application, 35–85. Kluwer Academic Publishers, Boston.

Kaynak Göster

APA Yılmaz Kaya, B. (2022). HUMAN FACTORS ENGINEERING ON THE EDGE OF INDUSTRY 4.0: ANALYSIS FOR IOT-AIDED TECHNOLOGIES . Endüstri Mühendisliği , 33 (1) , 1-21 .
Endüstri Mühendisliği
  • ISSN: 1300-3410
  • Yayın Aralığı: Yılda 3 Sayı
  • Yayıncı: TMMOB Makina Mühendisleri Odası

7.8b4.7b

Sayıdaki Diğer Makaleler

ENDÜSTRİ 4.0 DÖNÜŞÜMÜNDE İNSAN FAKTÖRLERİ MÜHENDİSLİĞİ: IOT TEMELLİ TEKNOLOJİLER ANALIZİ

Burcu YILMAZ KAYA

DIŞ TİCARET FİRMALARINDA TKY ALGISI VE TKY UYGULAMALARININ PERFORMANSA OLAN ETKİSİ

Kadriye Kübra SARIGÜL, Fikret Korhan TURAN

VAKIF ÜNİVERSİTELERİNİN AHP, COPRAS, SAW, TOPSIS YÖNTEMLERİYLE DEĞERLENDİRİLMESİ VE BORDA SAYIM YÖNTEMİ İLE BÜTÜNLEŞİK BİR SIRA ELDE EDİLMESİ

Meryem ULUSKAN, Gizem AKPOLAT, Dilek ŞİMŞEK

ÖĞRENME VE BOZULMA ETKİLERİ ALTINDAKİ ORTAK TESLİM TARİHLİ TEK MAKİNE ERKEN/GEÇ TAMAMLANMA ÇİZELGELEME PROBLEMİNİN OPTİMAL ÇİZELGESİNİN V ŞEKLİNDEKİ ÖZELLİĞİ

Oğuzhan Ahmet ARIK

BÜYÜK ÖLÇEKLİ BİR METAL SANAYİ İŞLETMESİNİN OFİSLERİNDE İŞYERİ KOŞULLARININ DEĞERLENDİRİLMESİ

Emin KAHYA, Sanem ÇAKIR, Selinay TETİK

DÜNYA KLASINDA ÜRETİM ODAKLANMIŞ İYİLEŞTİRME SÜTUNU İÇİN METODOLOJİ ÖNERİSİ VE BİR UYGULAMA

İhsan EROZAN, Zeynep SAYAN, Meltem BEŞKARDEŞLER

COVID-19 AŞISI ÇİZELGELEME PROBLEMİ: TERMAL NAKLİYE KONTEYNERLERİ UYGULAMASI

Elvin ÇOBAN GÖKTÜRK

ANALİTİK HİYERARŞİ PROSESİ VE MATEMATİKSEL PROGRAMLAMA ENTEGRASYONU İLE MEYVE NEKTARI ÜRÜN KARMASI OPTİMİZASYONU

Adem ERİK, Burcu DÜNDAR, Durmuş Can ACER

DEPO YÖNETİMİNDE ENDÜSTRİ 4.0 UYGULAMASI: BİR İŞLETME İÇİN RFID TEKNOLOJİ SEÇİMİ

Ebru TAŞKIN, Nisanur GEZİK, Rabia YUMUŞAK, Tamer EREN

Minimum Dinamik İletim Hat Derecelendirmesi Kullanarak Güç Sistemin Güvenilirliğini Artırma

Masood JABARNEJAD