TÜRKİYE’DE AĞIR SANAYİ ENDÜSTRİLERİNİN SİBER-FİZİKSEL ÜRETİM SİSTEMLERİNE GEÇİŞ POTANSİYELLERİNİN YENİ BİR BONFERRONİ FONKSİYONU TEMELLİ KARAR VERME YAKLAŞIMI İLE DEĞERLENDİRİLMESİ

Öz

Amaç: Bu çalışma ağır sanayi alt sektörlerinin üretim sistemlerinin siber-fiziksel sistemler yardımıyla dönüştürebilme potansiyellerini incelemektedir. Birçok ülkede kamu ve özel sektör kaynakları, bütün ağır sanayi endüstrilerinin eş zamanlı olarak geliştirilmesi ve dönüştürülmesi için yeterli olamayabilmektedir. Bu nedenle politika yapıcılar dengeli ve sürdürülebilir bir gelişim ve kalkınma yaratabilmek için öncelikli sektörler belirleyebilirler.

Yöntem: Mevcut çalışmada önerilen yaklaşım, öncelikli sektörlerin belirlenmesi için LMAW (Logarithm Methodology of Additive Weights) tekniğinden yararlanmaktadır. LMAW tekniği hem nicel hem de nitel kriterlerin birlikte değerlendirilmesine imkân tanıyan aynı zamanda karar vericilerin öznel değerlendirmelerinin grup kararına dönüştürülmesinde Bonferroni fonksiyonunu temel alan çok kriterli karar verme (ÇKKV) yaklaşımlarından birisidir.

Bulgular: LMAW tekniğinin uygulanması sonucunda çalışmada en etkili değerlendirme kriterinin genel ekipman verimliliği olduğu ve ilk sırada Havacılık ve Uzay Sanayi Endüstrisinin yer aldığı gözlemlenmiştir.

Özgünlük: Bu çalışma ağır sanayi alt sektörlerinin siber fiziksel sistemlere geçiş sürecini yeni bir ÇKKV yaklaşımı kullanılarak incelemektedir.

Anahtar Kelimeler

• Ağır Sanayi
• LMAW
• Bonferroni Fonksiyonu
• Çok Kriterli Grup Karar Verme
• Siber-Fiziksel Sistemler

EVALUATION OF THE TRANSITIONS POTENTIAL TO CYBER-PHYSICAL PRODUCTION SYSTEM OF HEAVY INDUSTRIES IN TURKEY WITH A NOVEL DECISION-MAKING APPROACH BASED ON BONFERRONI FUNCTION

Abstract

Purpose: This study examines the potential of production systems of the heavy industry branches with the help of cyber-physical systems. Sources of public and private sectors may not be sufficient to transform and develop all heavy industry branches simultaneously. Because of that, policymakers can determine priority industries for development and growth, which are sustainable and balanced in a country.

Methodology: In current study, the proposed approach uses the LMAW (Logarithm Methodology of Additive Weights) technique to identify priority sectors. The LMAW is a novel MCDM (Multi-Criteria Decision Making) technique providing an opportunity to evaluate both objective and subjective criteria; in addition, it uses the Bonferroni functions to transform the subjective evaluations of decision-makers to the group decision.

Findings: It has been observed that the most significant criterion is overall equipment effectiveness (OEE), and the most prior branch of heavy industry is the aerospace industry.

Originality: This paper examines the transformation process of the heavy industry branches to the cyber-physical systems by using a new MCDM approach.

Keywords

• Heavy Industries
• LMAW
• Bonferroni Function
• Multicriteria Group Decision-Making
• Cyber-Physical Systems.

Kaynakça

1. Ali, N. and Hong, J-E. (2018). “Failure Detection and Prevention for Cyber-Physical Systems Using Ontology-Based Knowledge Base”, Computers, 7(4), 68, 1-16.
2. CFI (2021). https://corporatefinanceinstitute.com, (Access Date: 20.07.2021).
3. Dafflon, B., Moalla, N. and Ouzrout, Y. (2021). “The Challenges, Approaches, and used Techniques of CPS for Manufacturing in Industry 4.0: a Literature Review”, The International Journal of Advanced Manufacturing Technology, 113, 2395-2412.
4. Deveci, M.; Pamucar, D. and Gokasar, I. (2021). “Fuzzy Power Heronian Function Based CoCoSo Method for the Advantage Prioritization of Autonomous Vehicles in Real-time Traffic Management”, Sustainable Cities and Society, 69, 102846.
5. Gaggioli, A. (2018). "The Disappearing Smartphone", Cyberpsychology, Behavior and Social Networking, 21(9), 530-531.
6. Gigović, L., Pamučar, D., Bajić, Z. and Milićević, M. (2016). “The Combination of Expert Judgment and GISMAIRCA Analysis for The Selection of Sites for Ammunition Depots”, Sustainability, 8(4), 372.
7. Görçün, O. F., Senthil, S. and Küçükönder, H. (2021). “Evaluation of Tanker Vehicle Selection Using a Novel Hybrid Fuzzy MCDM Technique”, Decision Making: Applications in Management and Engineering, 4(2), 140-162.
8. Hayhoe, T., Podhorska, I., Siekelova, A. and Stehel, V. (2019). “Sustainable manufacturing in Industry 4.0: Cross-Sector Networks of Multiple Supply Chains, Cyber-Physical Production Systems, and AI-driven Decision-Making”, Journal of Self-Governance and Management Economics, 7(2), 31-36.

9. Iansiti, M. and Lakhani, K.R. (2014). “Digital Ubiquity: How Connections, Sensors, and Data are Revolutionizing Business (Digest Summary)”, Harvard Business Review, 92(11). 91-99.
10. Jamwal, A. Agrawal, R. Sharma, M. Kumar, V. and Kumar, S. (2021). “Developing A Sustainability Framework for Industry 4.0”, Procedia CIRP, 98, 430-435.
11. Jiang J-R. (2018). “An Improved Cyber-Physical Systems Architecture for Industry 4.0 Smart Factories”, Advances in Mechanical Engineering, 10(6), 1-15.
12. Karthikeyan, R., Venkatesan, K.G.S. and Chandrasekar, A. (2016). “A Comparison of Strengths and Weaknesses for Analytical Hierarchy Process”, Journal of Chemical and Pharmaceutical Sciences, 9(3), 12-16.
13. Keyser, W.D. and Peeters, P. (1996). “A Note on the use of PROMETHEE Multicriteria Methods”, European Journal of Operational Research, 89(3), 457-461.
14. Lin, B. and Liu, K. (2016). “How Efficient Is China's Heavy Industry? A Perspective of Input-Output Analysis”, Emerging Market Finance and Trade, 52(11), 2546-2564.
15. Lopes Y. G., Almeıda A. T. (2015). “Assessment of Synergies for Selecting a Project Portfolio in the Petroleum Industry Based on a Multi-Attribute Utility Function”, Journal of Petroleum Science and Engineering, 126, 131-140.
16. Lu, Y., Xu, X., Wang, L. (2020). “Smart Manufacturing Process and System Automation – A Critical Review of the Standards and Envisioned Scenarios”, Journal of Manufacturing Systems, 56, 312-325.
17. Machado, C. G. Winroth, M. P. and Ribeiro da Silva, E. H. D. (2020). “Sustainable Manufacturing in Industry 4.0: An Emerging Research Agenda”, International Journal of Production Research, 58(5), 1462-1484.
18. Makris, D. Hansen, Z.N.L. and Khan, O. (2019). “Adapting to Supply Chain 4.0: An Explorative Study of Multinational Companies”, Supply Chain Forum: An International Journal, 20(2), 116-131.
19. Mbuli, J.W. (2019). “A Multi-Agent System for The Reactive Fleet Maintenance Support Planning of a Fleet of Mobile Cyber-Physical Systems: Application to The Rail Transport Industry”, Automatic Control Engineering, Université Polytechnique Hauts-de-France, 1-177.
20. Mufazzal, S. and Muzakkir, S.M. (2018). “A New Multi-Criterion Decision Making (MCDM) Method Based on Proximity Indexed Value for Minimizing Rank Reversals”, Computers & Industrial Engineering, 119, 427-438.
21. Nujoom, R., Mohammed, A. and Wang, Q. (2019). “Drafting a Cost-Effective Approach Towards a Sustainable Manufacturing System Design”, Computers & Industrial Engineering, 133, 317-330.
22. Oliveira J, Carvalho G, Cabral B. and Bernardino J. (2020). “Failure Mode and Effect Analysis for Cyber-Physical Systems”, Future Internet, 12(11:205), 1-18.
23. Pamučar, D. and Ćirović, G. (2015). “The Selection of Transport and Handling Resources in Logistics Centers Using Multi-Attributive Border Approximation Area Comparison (MABAC)”, Expert Systems with Applications, 42(6), 3016-3028.
24. Pamucar, D., Žižović, M., Biswas, S. and Božanić, D. (2021). “A New Logarithm Methodology of Additive Weights (LMAW) for Multi-Criteria Decision-Making: Application in Logistics”, Facta Universitatis Series: Mechanical Engineering, DOI: https://doi.org/10.22190/FUME210214031P.
25. Pascual, D.G., Pasquale D. and Uday, K. (2021). “The Industry 4.0 Architecture and Cyber-Physical Systems”. Handbook of Industry 4.0 and SMART Systems, Editors: Diego Galar Pascual, Pasquale Daponte and Uday Kumar, Routledge Handbooks Online.
26. Ribeiro, L. (2017). “Cyber-Physical Production Systems' Design Challenges”, IEEE 26th International Symposium on Industrial Electronics (ISIE), 1189-1194.
27. Shahi, C. and Sinha, M. (2021). “Digital Transformation: Challenges Faced by Organizations and Their Potential Solutions”, International Journal of Innovation Science, 13(1), 17-33.
28. Si, S.-L. You, X.-Y. Liu, H.-C. and Zhang, P. (2018). “DEMATEL Technique: A Systematic Review of The State-Of-The-Art Literature on Methodologies and Applications”, Mathematical Problems in Engineering, 2018, 1-33.
29. Silva, E. M. and Jardim-Goncalves, R. (2021). “Cyber-Physical Systems: A Multi-Criteria Assessment for Internet-Of-Things (IoT) Systems”, Enterprise Information Systems, 15(3), 332-351.
30. Stanković, M. Stević, Ž. Das, D. K. Subotić, M. and Pamučar, D. (2020). “A New Fuzzy MARCOS Method for Road Traffic Risk Analysis”. Mathematics, 8(3), 457.
31. Stević, Ž., Pamučar, D., Puška, A. and Chatterjee, P. (2020). “Sustainable Supplier Selection in Healthcare Industries using a New MCDM Method: Measurement of Alternatives and Ranking According to COmpromise Solution (MARCOS)”, Computers & Industrial Engineering, 140, 106231.
32. Zaoui, F. and Souissi, N. (2020). “Roadmap for Digital Transformation: A Literature Review”, Procedia Computer Science, 175, 2020, 621-628.
33. Zavadskas, E., Turskis, Z., Antucheviciene, J. and Zakarevicius, A. (2012). “Optimization of Weighted Aggregated Sum Product Assessment”, Elektronika ir Elektrotechnika,122(6), 3-6.