Endüstri 4.0’ın Uygulanmasında Kritik Başarı Faktörlerinin Belirlenmesi ve Aralık Tip-2 Bulanık TOPSIS Yöntemi ile Yatırım Teşviği Alan Firmaların Durum Değerlendirilmesi

Year 2022, Issue: 41, 36 - 47, 30.11.2022

Damla Çevik Aka

https://doi.org/10.31590/ejosat.961437

An Erratum to this article was published on February 15, 2024. https://dergipark.org.tr/en/pub/ejosat/issue/82065/1435569

Abstract

Endüstri 4.0’ın kritik başarı faktörlerini somutlaştırmasındaki literatür ve ampirik çalışmalardaki eksiklik, bu alandaki çalışmalara olan ihtiyacı ortaya koymaktadır. Çalışmanın birincil amacı, bugünün ve geleceğin üretim alanlarında etkili olan endüstri 4.0 için gereken anahtar başarı faktörlerini belirleyebilmek ve önceliklendirmektir. Ardından belirlenen kriterlerle Endüstri 4.0 kapsamında yatırım teşviği alan farklı sektörlerdeki işletmeler için teşvik sonrası durum değerlendirilmesi yapılmaktadır. Araştırmaya konu olan işletmeler, Kocaeli’nde otomotiv, plastik ve alüminyum sektöründe faaliyet gösteren küçük ölçekli firmalardır. Çalışmada birden fazla karar vericinin, birkaç alternatifin ve çok kriterin yer alması ve sezgisel değerlendirmelerin de hesaba katılması sebebiyle Çok Kriterli Karar Verme Yöntemlerinden Aralık Tip-2 Bulanık TOPSIS kullanılmıştır. Literatürden şekillenen sekiz kriter ve teşvik almış üç firma, yatırım desteği sunan kurumun üç uzman mühendisi tarafından değerlendirilmiştir. Çalışma bulanık TOPSIS yönteminin, işletmelerin mevcut durum değerlendirilmelerinde etkin bir yöntem olarak kullanılabileceğini göstermiştir.

Keywords

Kritik Başarı Faktörleri, Aralık tip-2 Bulanık TOPSIS, Endüstri 4.0, Bulanık TOPSIS, Durum Analizi

Determination of Critical Success Factors in the Implementation of Industry 4.0 and Evaluation of the Situation of Firms Receiving Investment Incentives with the Interval Type-2 Fuzzy TOPSIS Method

An Erratum to this article was published on February 15, 2024. https://dergipark.org.tr/en/pub/ejosat/issue/82065/1435569

Abstract

The lack of literature and empirical studies in concretizing the critical success factors of Industry 4.0 reveals the need for studies in this field. The primary purpose of the study is to identify and prioritize the key success factors required for industry 4.0, which is effective in the production areas of today and the future. Then, a post-incentive situation assessment is made for businesses in different sectors that receive investment incentives within the scope of Industry 4.0 with the determined criterias. The businesses in the research are small-scale firms operating in the automotive, plastic and aluminum sectors in Kocaeli. In the study, Interval Type-2 Fuzzy TOPSIS, one of the Multi-Criteria Decision Making Methods, was used due to the presence of more than one decision maker, several alternatives and multiple criteria, and intuitive evaluations. Eight criterias shaped from the literature and three firms that received incentives were evaluated by three expert engineers of the institution providing investment support. The study has shown that the fuzzy TOPSIS method can be used as an effective method for evaluating the current situation of the firms.

Keywords

Critical Success Factors, Interval Type 2 Fuzzy TOPSIS, Industry 4.0, Fuzzy TOPSIS, Situation Analysis., Critical Success Factors, Interval Type 2 Fuzzy TOPSIS, Industry 4.0, Fuzzy TOPSIS, Situation Analysis.

References

• Abidin, Z.Z., Selamet, S.R., & Anawar, S. (2019). Multi- Layered based Digital Forensic Investigation for Internet-of-Things: Systematic Literature Review. International Journal of Computer and Science and Network Security, 19(9), 156-175.
• Akdil, K. Y., Ustundag, A., & Cevikcan, E. (2018). Maturity and Readiness Model for Industry 4.0 Strategy. In Industry 4.0: Managing the Digital Transformation, 61-94, Springer, Cham.
• Akbas, H., & Bilgen, B. (2017). An integrated fuzzy QFD and TOPSIS methodology for choosing the ideal gas fuel at WWTPs, Energy, (125), 484-49.
• Al Zubayer, A.; Ali, S.M.; & Kabir, G. (2019). Analysis of supply chain risk in the ceramic industry using the TOPSIS method under a fuzzy environment. J. Model. Manag., (14), 792–815.
• Alçın, S. (2016). Üretim için yeni bir izlek: Endüstri 4.0. Journal of Life Economics, (8), 19-30.
• Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: a survey. Computer Networks, 54(15), 2787–2805. doi:10.1016/j.comnet.2010.05.010.
• Brynjolfsson, E., & Mcafee, A. (2014). The Second Machine Age. İstanbul: Türk Havayolları Yayınları.
• Calabrese, A., Dora, M., Levialdi Ghiron, N., & Tiburzi, L. (2020). Industry’s 4.0 Transformation Process: How to Start, Where to Aim, What to Be Aware of. Production Planning & Control, (32), 1–21.
• Chauhan, R., Singh, T., Tiwari, A., Patnaik, A., & Thakur, N.S. (2017). Hybrid Entropy–TOPSIS Approach for Energy Performance Prioritization in A Rectangular Channel Employing Impinging Air Jets, Energy, (134), 360-368.
• Chen, C. T. (2000). Extensions of The TOPSIS for group decision- makingunder fuzzy environment. Fuzzy Sets and Systems, (114), 1-9.
• Chen- T. C., Lin, C.T., & Huang, S. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289-301.
• Cheng, C-Y. (2018). A noveel approach of information visualization for machine operations states in industrial 4.0. Computers & Industrial Engineering, (125), 563-573.
• Conti, M., Dehghantanha, A., Franke, K., & Watson S. (2017). Internet of things security and foren-sics: challenges and opportunities. Future Generation Computer Systems, (78), 1–3.
• Çalık, A. (2019). Yüklenici Değerlendirme sürecinde aralıklı tip-2 bulanık TOPSIS yöntemi uygulaması: küçük ve orta ölçekli işletmelerde (KOBİ’ler) bir örnek olay çalışması. Iğdır Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (18), 481-501.
• Denizhan, B., Yalçıner A., & Berber, Ş. (2017). Analitik Hiyerarşi Proses ve Bulanık Analitik Hiyerarşi Proses Yöntemleri Kullanılarak Yeşil Tedarikçi Seçimi Uygulaması. Nevşehir Bilim ve Teknoloji Dergisi, 6(1), 63-68.
• Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, (89), 23-34.
• Ecer, F. (2006). Bulanık ortamlarda grup kararı vermeye yardımcı bir yöntem: fuzzy TOPSIS ve bir uygulama. İşletme Fakültesi Dergisi, 7(2), 77 -96.
• Esmer, Y., ve Bağcı, H. (2017). Katılım Bankalarında Finansal Performans Analizi: Türkiye Örneği, Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 8(15), 17-30. Fırat S. U., & Fırat, O. Z. (2017a). Sanayi 4.0 devrimi üzerine karşılaştırmalı bir inceleme: kavramlar, küresel gelişmeler ve Türkiye. Toprak İşveren Dergisi, 114.
• Fırat, O. Z., & Fırat, S. U. (2017b). Endüstri 4.0 Yolculuğunda Trendler ve Robotlar. Istanbul University Journal of the School of Business, 46(2), 211-223. doi: 10.5152/iujsb.2017.005.
• Foidl, H., & Felderer, M. (2015). Research challenges of Industry 4.0 for quality management. International Conference on Enterprise Resource Planning Systems, Hagenberg, Austria: Springer, C. LNBIP 245, 121-137.
• Ghanzanfari, M., Rouhani, S., Jafari, M. (2014). A fuzzy TOPSIS model to evulate the Business Intelligence competencies of Port Community Systems. Polish Maritime Researh, 21(2), 279-285.
• Gilchrist, A. (2016). Industry 4.0: The Industrial Internet of Things, Springer, Heidelberg.
• Han, H., & Trimi, S. (2018). A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms, Expert Systems Wıth Applİcatİons, (103), ‏133-145.
• Heidari, S.S.; Khanbabaei, M.; & Sabzehparvar, M. (2018). A model for supply chain risk management in the automotive industry using fuzzy analytic hierarchy process and fuzzy TOPSIS. Benchmarking-An Internatıonal Journal, 25(9), 3831-3857.
• Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for ındustrie 4.0 scenarios. 49th Hawaii International Conference on System Science, Koloa, USA. doi: 10.1109/HICSS.2016.488.
• Hoyer, C., Gunawan, I., & Reaiche, C.H. (2020). The Implementation of Industry 4.0–a Systematic Literature Review of the Key Factors. Systems Research and Behavioral Science, 37 (4), 557–578. doi:10.1002/sres.2701.
• Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., & Ivanova, M. (2016). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory Industry 4.0. International Journal of Production Research, 54(2), 386-402.
• Jain, V., Sangaiah, A. K., Sakhuja, S., Thoduka, N., & Aggarwal, R. (2018). Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Computing and Applications, 29(7), 555-564.
• Kagermann, H., Wahlster, W., & Helbig, J. (April, 2013). Recomendations for ımplementing the strategic initiative industrie 4.0. Acatech Natıonal Academy of Scienec and Engineering Report.
• Kekilli, E, Cebeci, U. & Sılay, L. (2021). Selection of VFQ Consultant by Using Integrated Fuzzy AHP and Fuzzy TOPSIS. European Journal of Science and Technology, (24), 262-267.
• Khoshi, A., Gooshki, H. S., & Mahmoudi, N. (2018). The data on the effective qualifications of teachers in medical sciences: An application of combined fuzzy AHP and fuzzy TOPSIS methods. DATA IN BRIEF, (21), ‏2689-2693.
• Kiraz, A., Canpolat, O., Erkan, E.F., & Albayrak, F. (2018). Evaluating R&D Projects Using Two Phases Fuzzy AHP and Fuzzy TOPSIS Methods, European Journal of Science and Technology, (14), 49-53.
• Koçak, A., & Diyadin, A. (2017). Sanayi 4.0 geçiş süreçlerinde kritik başarı faktörlerinin DEMATEL yöntemi ile değerlendirilmesi. Ege Akademik Bakış, 18(1), 107-120.
• Lichtblau, K., Stich, V., Bertenrath, R., Blum, M., Bleider, M., Millack, A., Schmitt, K., Schmitz, E., & Schröter, M. (October, 2015). Industrie 4.0 Readiness. VDMA’s IMPULS-Stiftung Report.
• Liu, C., & Xu, X. (2017). Cyber-physical machine tool – the era of machine tool 4.0. The 50th Cırp Conference on Manufacturing Systems - Procedia CIRP, (63), 70–75.
• Majd, M.M., Ftemi, A., & Soltanpanah, H. (2014). The risk analysis of oil projects using fuzzy TOPSIS technique (Case Study:18-inch pieline repair Project from Chesme Khosh to Ahwaz, Int. J. Basic Sci. Appl. Res., 5(3), 281-285.
• Maghsoodi, A.I.; & Khalilzadeh, M. (2017). Identification and Evaluation of Construction Projects’ Critical Success Factors Employing Fuzzy-TOPSIS Approach. KSCE J. Civ. Eng., (22), 1593–1605.
• Memari, A.; Dargi, A.; Jokar, M.R.A.; Ahmad, R.; & Rahim, A.R.A. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. J. Manuf. Syst., (50), 9–24.
• Miç, P., & Antmen, Z.F. (2019). A Healthcare Facility Location Selection Problem with Fuzzy TOPSIS Method for a Regional Hospital, European Journal of Science and Technology, (16), 750-757.
• Mohsin, M.; Zhang, J.; Saidur, R., Sun, H., & Sait, S.M. 2019). Economic assessment and ranking of wind power potential using fuzzy-TOPSIS approach. Environmental Science and Pollution Research, 26(22), 22494-22511.
• Moeuf, A., Lamouri, S., Pellerin, R., Tamayo-Giraldo, S., Tobon-Valencia, E., & Eburdy, R. (2020). Identification of Critical Success Factors, Risks and Opportunities of Industry 4.0 in SMEs. International Journal of Production Research, 58,(5), 1384–1400. doi:10.1080/00207543.2019.1636323.
• Nwaiwu, F., Duduci, M., Chromjakova, F., & Otekhile, C.A.F. (2020). Industry 4.0 Concepts within the Czech SME Manufacturing Sector: An Empirical Assessment of Critical Success Factors. Business: Theory and Practice, 21(1), 58–70.
• Özdemir, M. (2018). TOPSIS; Operasyonel, Yönetsel ve Stratejik Problemlerin Çözümünde Çok Kriterli Karar Verme Yöntemleri, Edt. Yıldırım, B.F., & Önder, E, Dora Yayıncılık, Bursa.
• Park, C-J., Kim, S-Y., & Nguyen, M.V. (2021). Fuzzy TOPSIS Application to Rank Determinants of Employee Retention in Construction Companies: South Korean Case, Sustainability, 13, 5787.
• Platform Industrie 4.0. (2014). Umsetyungsstrategie Industrie 4.0. Erişim https://www.plattform-i40.de/umsetzungsstrategie-industyrie-40-0 (Erişim Tarihi: 22.05.2019). Pozzi, R., Rossi, T., & Secchi, R. (2020). Industry 4.0 technologies: critical success factors for implementation and improvements in manufacturing companies, Production Planning&Control, https://doi.org/10.1080/09537287.2021.1891481.
• Qin, J., Liu, Y., & Grosvenora, R. (2016). A categorical framework of manufacturing for industry 4.0 and beyond changeable, agile. Reconfigurable & Virtual ProductioN. Procedia CIRP, 52, 173–178.
• Reddy, A. S; Kumar, P. R; & Raj, P. A. (2019). Entropy-based fuzzy TOPSIS framework for selection of a sustainable building material. Internatıonal Journal of Constructİon Management, doi.org/10.1080/15623599.2019.1683695.
• Roblek, V., Meško, M., & Krapež, A. (2016). A complex view of Industry 4.0, SAGE Open, 6(2), 1-11.
• Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group Report, 1-14.
• Sağır, H., & Doğanalp. B. (2016). Bulanık Çok Kriterli Karar Verme Perspektifinden Türkiye İçin Enerji Kaynakları Değerlendirmesi. Kastamonu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 11(1), 243-254.
• Sarvari, P.A., Ustundag, A., Cevikcan, E., Kaya, I., & Cebi, S. (2018). Technology roadmap for Industry 4.0, in Ustundag, A. and Cevikcan, E. (Eds), Industry 4.0: Managing the Digital Transformation. Springer, Heidelberg, 95-103.
• Schumacher, A., Erol, S., & Sihn, W. (2016). A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP, (52), 161-166.
• Solangi, Y.A.; Tan, Q., & Mirjat, N.H.; et al. (2019). Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach. Journal of Cleaner Production, 236. Article Number: UNSP 117655.
• Sony, M. (2018). Industry 4.0 and lean management: a proposed integration model and research propositions. Production & Manufacturing Research, 6(1), 416-432.
• Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in Industry 4.0. Procedia CIRP, (40), 536–541.
• Thames, L., &Schaefer, D. (2016). Softwaredefined cloud manufacturing for industry 4.0. Procedia CIRP, (52), 12-17.
• TÜBİTAK. (2016). Yeni Endüstri devrimi: Akıllı üretim sistemleri teknoloji yol haritası. Erişim: http://www.tubitak.gov.tr/ (Erişim Tarihi: 17.05.2019).
• Yacan, İ. (2016). Eğitim Kalitesinin Belirlenmesinde Etkili Olan Faktörlerin Bulanık AHP ve Bulanık TOPSIS Yöntemi ile Değerlendirilmesi. Pamukkale Üniversitesi Sosyal Bilimleri Enstitüsü, Yüksek Lisans Tezi, Denizli, 90s.
• Yao, X., Zhou, J., Zhang, J., & Boër, C.R. (2017). From intelligent manufacturing to smart manufacturing for industry 4.0 driven by next generation artificial intelligence and further. 2017 5th International Conference on Enterprise Systems, 22–24, IEEE, Beijing, China.
• Yıldız, A., Karakoyun, F., & Parlak, I.E. (2018). The Most Suıtable Mobıle RFıD Reader Selectıon By Usİng Interval Type-2 Fuzzy Topsıs Method. Sıgma Journal of Engıneerıng And Natural Scıences-Sıgma Muhendıslık ve Fen Bılımlerı Dergisi, 36(3),‏ 717-729.
• Yurdakul, M., & İç Y.S. (2018). Development of a multi-level performance measurement model for manufacturing companies using a modified version of the fuzzy TOPSIS approach. Soft Cumputing, 22 (22), 7491-7503.
• Wanke, P., Barros, C. P., & Chen, Z. (2015). An analysis of Asian airlines efficiency with two-stage TOPSIS and MCMC generalized linear mixed models. International Journal of Production Economics, (169), 110-126.
• Wang, S., Wan, J., Li, D., & Zhang, C. (2016). Implementing smart factory of Industrie 4.0: an outlook. International Journal of Distributed Sensor Networks, 12(1), 1-10.
• Wang, G., Gunasekaran, A., Ngai, E.W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: certain investigations for research and application. International Journal of Production Economics, 176(1), 98-110.
• Wood, D.A. (2016). Supplier selection for development of petroleum industry facilities, applying multi-criteria decision making techniques inclding fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting. Journal of Natural Gas Science and Eng. (28), 594-612.