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Process Based Failure Mode Analysis with Fuzzy Multi Criteria Decision Making Methods in Supply Chain Risk Management

Year 2022, Volume: 8 Issue: 1, 86 - 104, 10.03.2022
https://doi.org/10.28979/jarnas.937779

Abstract

Today, increasing supply chain risks are encountered as a result of increasing natural disasters, rapid development of technology, shortening of product life, increasing costs, political reasons, uncertainties in supply and demand. Businesses have to systematically manage their supply chain risks in order to gain competitive advantage, meet customer expectations, increase their market share in the sector and maintain their sustainability. In the study, errors that occur in the demand planning process of a company in the air conditioning and heating sector were analyzed. In this study, due to the weaknesses of the Process FMEA method, it is aimed to introduce a new process-step approach based on Fuzzy SWARA-Fuzzy COPRAS to risk analysis. The aim is to improve the weaknesses of Process FMEA method in the way that expert decision makers cannot consider their knowledge and experience in evaluations. Fuzzy SWARA and Fuzzy COPRAS based process step MCDM (Multi Criteria Decision Making) model is proposed for risk analysis. The SWARA method was used to determine the importance levels of the criteria, and the COPRAS method was used to sort the errors. According to this method, the most important errors; It has been determined that "the deficient production is noticed in the production deficit report as a result of incomplete or incorrect demand figures" and "the incomplete production is noticed at the S&OP (Sales and Operation Planning) meeting as a result of incomplete or incorrect demand numbers uploaded to the system. In future studies, the effectiveness of risk analysis can be evaluated with similar approaches and integrated into decision support systems and used in the evaluation of current or future scenarios.

References

  • Aksoy, E., Ömürbek, N. ve Karaatlı, M., (2015),“AHP temelli MULTIMOORA ve COPRAS yöntemi ile Türkiye Kömür İşletmeleri’nin performans değerlendirmesi”, Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33 (4): 1-28.
  • Altaş, İ. H., (1999), “Bulanık Mantık: Bulanıklık Kavramı”, Enerji, Elektrik, Elektromekanik-3e, Bileşim Yayıncılık, A.Ş., 62: 80-85.
  • Baryannis, G., Dani, S. ve Antoniou, G., (2019), “Predicting supply chain risks using machine learning: The trade-off between performance and interpretability”, Future Generation Computer Systems, 101: 993-1004.
  • Baykasoğlu, A., Dereli, T., Yılankırkan, N. ve Yılankırkan, A., (2003), “Hata Türü Ve Etkileri Analizi (HTEA) ve Gaziantep'te Orta Ölçekli Bir Firmada Uygulanması”, II. Makina Tasarım ve İmalat Teknolojileri Kongresi, Konya, 157-163.
  • Belu, N., Emil, M., Rachieru, N. ve Anghel, D.C., (2012) “Application of FMEA in Product Development Stage”, Academic Journal of Manufacturing Engineering, 10 (3): 12-19.
  • Chowdhruy, N. A., Ali, S. M., Mahtab, Z., Rahman, T., Kabir, G. ve Paul, S. K., (2019), “A structural model for investigating the driving and dependence power of supply chain risks in the readymade garment industry”, Journal of Retailing and Consumer Services, 51: 102-113.
  • Dong, Q., ve Cooper, O., (2016), “An orders-of-magnitude AHP supply chain risk assessment framework”, International Journal of Production Economics, 182: 144-156.
  • Fan, Y., Feng, Y. ve Shou, Y., (2020), “A risk-averse and buyer-led supply chain under option contract: CVAR minimization and channel coordination”, International Journal of Production Economics, 219: 66-81.
  • Fouladgar, M. M., Yazdani-Chamzini, A., Zavadskas, E. K. ve Haji Moini, S. H., (2012), “A new hybrid model for evaluating the working strategies: case study of construction company”, Technological and Economic Development of Economy, 18 (1): 164-188.
  • Gallab, M., Bouloiz, H., Garbolino, E., Alaoui Y. L. ve Tkiouat, M., (2019), “Risk Assessment of Main-tenance activities using Fuzzy Logic”, Procedia Computer Science, 148: 226-235.
  • Gallab, M., Bouloiz, H., Garbolino, E., Tkiouat, M., Elkilani M. A. ve Bureau, N., (2017), “Risk analysis of maintenance activities in a LPG supply chain with a Multi-Agent approach”, Journal of Loss Pre-vention in the Process Industries, 47: 41-56.
  • Giannakis, M., ve Papadopoulos, T., (2016), “Supply chain sustainability: A risk management approach”, International Journal of Production Economics, 171 (4): 455-470.
  • Hsieh, T.Y., Lu, S.T. ve Tzeng, G.H., (2004), “Fuzzy MCDA approach for planning and design tenders selection in public office buildings”, International Journal of Project Management, 22 (7): 573–584.
  • Hsu, P. Y., Aurisiccihio, M. ve Angeloudis, P., (2019), “Risk-averse supply chain for modular construction projects”, Automation in Construction, 106: 1-10.
  • İnternet: Taşdemir, O., Dilaver, M. ve Sönmez, Y.M., (2016), Proses Tehlike Analizlerindeki Belirsizlikle-rin Bulanık Mantık İle Kantitatifleştirilmesi”, https://www.proscon.com.tr/proses-tehlike-analizlerindeki-belirsizliklerin bulanik-mantik-ile-kantitatiflestirilmesi/.
  • Jajja, M. S. S., Chatha, K. A. ve Farooq, S., (2018), “Impact of supply chain risk on agility performance: Mediating role of supply chain integration”, International Journal of Production Economics, 205: 118-138.
  • Jiang, B., Li, J. ve Shen, S., (2018), “Supply Chain Risk Assessment and Control of Port Enterprises: Qingdao port as case study”, The Asian Journal of Shipping and Logistics, 34 (3): 198-208.
  • Jovic, S., (2014), “Gıda sektöründe tedarik zinciri risk faktörlerinin belirlenmesi”, Yüksek Lisans Tezi, Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul, 1-9.
  • Kaklauskas, A., Zavadskas, E.K., Raslanas, S., Gınevıcıus, R., Komka, A. ve Malinauskas, P., (2006), “Selection Of Low-E Windows İn Retrofit Of Public Buildings By Applying Multiple Criteria Met-hod COPRAS: A Lithuanian Case”, Energy and Buildings, 38 (5):454-462.
  • Katrancı A., ve Kundakçı, N., (2020), “SWARA Temelli Bulanık COPRAS Yöntemi ile Soğuk Hava De-posu Seçimi”, Optimum Ekonomi ve Yönetim Bilimleri Dergisi, 7 (1): 63-80.
  • Keršuliene, V., Zavadskas, E. K., ve Turskis, Z., (2010), “ Selection of Rational Dispute Resolution Met-hod by Applying New Step-Wise Weight Assessment Ratio Analysis (SWARA)”, Journal of Busi-ness Economics and Management, 11 (2): 243– 258.
  • Leblanc, D. I., Villeneuve, S., Beni, L. H., Otten, A., Fazil, A., McKellar, R. ve Delaquis, P., (2015), “A national produce supply chain database for food safety risk analysis”, Journal of Food Engineering, 147: 24-38.
  • Mavi, R.K., Goh, M. ve Zarbakhshnia, N., (2017), “Sustainable third-party reverse logistic provider selec-tion with fuzzy SWARA and fuzzy MOORA in plastic industry“, The International Journal of Ad-vanced Manufacturing Technology, 91: 5-8.
  • Mensaha, P., Merkuryeva, Y., Klavinsa, E. ve Manakb, S., (2017), “Supply Chain Risks Analysis of a Logging Company: Conceptual Model”, Procedia Computer Science, 104: 313-320.
  • Ming, C. T., Cheng, M., Bin, S. ve Qi, S., (2019), “Optimal pricing in mass customization supply chains with risk-averse agents and retail competition”, Omega, 88: 150-161.
  • Nagurney, A., Jose C., June D. ve Ding Z., (2005), “Supply Chain Networks, Electronic Commerce and Supply Side and Demand Side Risk”, European Journal of Operational Research, 164 (1): 120-142.
  • Nguyen, H. T., Dawal, S. Z. M., Nukman, Y., Aoyama, H. ve Case, K., (2015),“An integrated approach of fuzzy linguistic preference based AHP and fuzzy COPRAS for machine tool evaluation”, Plos one, 10 (9): 1-24.
  • Oliveira, F. N., Leiaras, A. ve Ceryno, P., (2019), “Environmental risk management in supply chains: A taxonomy, a framework and future research avenues”, Journal of Cleaner Production, 232: 1257-1271.
  • Peng, H., ve Pang, T., (2019), “Optimal strategies for a three-level contract-farming supply chain with sub-sidy”, International Journal of Production Economics, 216: 274-286.
  • Podvezko V., (2011), “The Comparative Analysis Of MCDA Methods SAW And COPRAS”, Inzinerine Ekonomika-Engineering Economics, 22 (2):134-146.
  • Prakash, A., Agarwal, A, ve Kumar, A.,(2018), “Risk Assessment in Automobile Supply Chain”, Materials Today: Proceedings, 5(2): 3571-3580.
  • Rajesh, R., ve Ravi, V., (2015), “Modeling enablers of supply chain risk mitigation in electronic supply chains: A Grey–DEMATEL approach”, Computers & Industrial Engineering, 87: 126-139.
  • Raza, S. A., ve Govindaluri, S. M., (2019), “Pricing strategies in a dual-channel green supply chain with cannibalization and risk aversion”, Operations Research Perspectives, 6: 1- 14.
  • Sabouhi, F., Psihvaee, M. S. ve Jabalemeli, M. S., (2018), “Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain”, Computers & Industrial Engineering, 126: 657-672.
  • Schaefer, T., Udenio, M., Quinn, S. ve Fransoo, J. C.,(2019), “Water risk assessment in supply chains”, Journal of Cleaner Production, 208: 636-648. Serrano, A., Oliva, R ve Kraiselburd, S., (2018), “Risk propagation through payment distortion in supply chains”, Journal of Operations Management, 58-59: 1-14.
  • Shojei, P., ve Haeri, S. A. S., (2019), “Development of supply chain risk management approaches for construction projects: A grounded theory approach”, Computers & Industrial Engineering, 128: 837-850.
  • Silva, C., Pavoa, A. P. B. ve Carvalho, A.,(2019), “Green Supply Chain: Integrating Financial Risk Measu-res while Monetizing Environmental Impacts”, Computer Aided Chemical Engineering, 46: 1549-1554.
  • Şimşir, F. Demir, H.İ. ve Azdemir, S., "Demir Çelik Sektöründe Hibrid DEMATEL ve TOPSİS-ELECTRE Yöntemleri ile Hata Türleri ve Etkileri Analizi", Academic Platform Journal of Engineering and Sci-ence, c. 6, sayı. 3, ss. 22-34, Eyl. 2018, doi:10.21541/apjes.455767
  • Thun, J. H., ve Hoeing, D., (2011), “An empirical analysis of supply chain risk management in the German automotive industry”, International Journal of Production Economics, 131 (1): 242-249.
  • Valinejad, F., ve Rahmani, D., (2018), “Sustainability risk management in the supply chain of telecommu-nication companies: A case study”, Journal of Cleaner Production, 203: 53-67.
  • Venkatesh, V. G., Rathi, S. ve Patwa, S., (2015), “Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using Interpretive structural modeling”, Journal of Retailing and Consumer Services, 26: 153-167.
  • Vilko, J., Ritala, P. ve Hallikas, J., (2019), “Risk management abilities in multimodal maritime supply cha-ins: Visibility and control perspectives”, Accident Analysis & Prevention, 123: 469-481.
  • Wu, Y., Jia, W., Li, I., Song, Z., Xu, C. ve Liu, F.,(2019), “Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation”, Energy, 182: 397-411.
  • Yang, B.,(2019), “Construction of logistics financial security risk ontology model based on risk association and machine learning”, Safety Science, 123: 1-10,
  • Yazdani, M., Alidoosti, A. ve Zavadskas, E. K., (2011), “Risk analysis of critical infrastructures using fuzzy COPRAS”, Economic Research-Ekonomska İstraživanja, 24 (4): 27-40.

Tedarik Zinciri Risk Yönetiminde Bulanık Çok Kriterli Karar Verme Yöntemleri ile Süreç Bazlı Hata Türleri Analizi

Year 2022, Volume: 8 Issue: 1, 86 - 104, 10.03.2022
https://doi.org/10.28979/jarnas.937779

Abstract

Günümüzde; doğal afetlerin artması, teknolojinin hızla gelişmesi, ürün ömrünün kısalması, maliyetlerin artması, politik sebepler, arz ve talepteki belirsizlikler sonucu giderek artan tedarik zinciri riskleri ile karşılaşılmaktadır. İşletmeler rekabet avantajı sağlayabilmek, müşteri beklentilerini karşılayabilmek, sektörde pazar paylarını artırabilmek ve sürdürülebilirliklerini koruyabilmek için tedarik zinciri risklerini sistemli bir şekilde yönetmek durumundadırlar. Çalışmada, iklimlendirme ve ısıtma sektöründeki bir firmanın talep planlama sürecinde oluşan hatalar analiz edilmiştir. Bu çalışmada amaçlanan; Süreç HTEA (Hata Türü ve Etkileri Analizi) yönteminin zayıf yönleri sebebiyle, risk analizine Bulanık SWARA-Bulanık COPRAS tabanlı süreç aşamalı yeni bir yaklaşım getirmektir. Süreç HTEA yönteminin, alanında uzman karar vericilerin bilgi ve tecrübelerini değerlendirmelerde göz önüne alamaması şeklindeki zayıflıklarını iyileştirmek hedeflenmiştir. Bu amaçla, risk analizi için Bulanık SWARA-COPRAS tabanlı süreç aşamalı ÇKKV (Çok Kriterli Karar Verme) modeli önerilmiştir. Kriterlerin önem ağırlıklarını belirleyebilmek için Bulanık SWARA yöntemi, hataları sıralayabilmek için Bulanık COPRAS yöntemi kullanılmıştır. Bu yönteme göre, en önemli hataların; “HT-1: Talep rakamlarının eksik veya yanlış olması sonucu eksik üretimin S&OP (Satış ve Operasyon Planlama) toplantısında farkedilmesi” ve “HT5:Talep rakamları belirlenirken master datanın eksik tanımlanması sonucu eksik üretimin S&OP toplantısında fark edilmesi” olduğu tespit edilmiştir. İlerleyen çalışmalarda, risk analizlerinin etkinlikleri benzer yaklaşımlarla değerlendirilebileceği ve karar destek sistemlerine entegre edilerek mevcut veya gelecek senaryoların analizinde kullanılabileceği öngörülmektedir.

References

  • Aksoy, E., Ömürbek, N. ve Karaatlı, M., (2015),“AHP temelli MULTIMOORA ve COPRAS yöntemi ile Türkiye Kömür İşletmeleri’nin performans değerlendirmesi”, Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33 (4): 1-28.
  • Altaş, İ. H., (1999), “Bulanık Mantık: Bulanıklık Kavramı”, Enerji, Elektrik, Elektromekanik-3e, Bileşim Yayıncılık, A.Ş., 62: 80-85.
  • Baryannis, G., Dani, S. ve Antoniou, G., (2019), “Predicting supply chain risks using machine learning: The trade-off between performance and interpretability”, Future Generation Computer Systems, 101: 993-1004.
  • Baykasoğlu, A., Dereli, T., Yılankırkan, N. ve Yılankırkan, A., (2003), “Hata Türü Ve Etkileri Analizi (HTEA) ve Gaziantep'te Orta Ölçekli Bir Firmada Uygulanması”, II. Makina Tasarım ve İmalat Teknolojileri Kongresi, Konya, 157-163.
  • Belu, N., Emil, M., Rachieru, N. ve Anghel, D.C., (2012) “Application of FMEA in Product Development Stage”, Academic Journal of Manufacturing Engineering, 10 (3): 12-19.
  • Chowdhruy, N. A., Ali, S. M., Mahtab, Z., Rahman, T., Kabir, G. ve Paul, S. K., (2019), “A structural model for investigating the driving and dependence power of supply chain risks in the readymade garment industry”, Journal of Retailing and Consumer Services, 51: 102-113.
  • Dong, Q., ve Cooper, O., (2016), “An orders-of-magnitude AHP supply chain risk assessment framework”, International Journal of Production Economics, 182: 144-156.
  • Fan, Y., Feng, Y. ve Shou, Y., (2020), “A risk-averse and buyer-led supply chain under option contract: CVAR minimization and channel coordination”, International Journal of Production Economics, 219: 66-81.
  • Fouladgar, M. M., Yazdani-Chamzini, A., Zavadskas, E. K. ve Haji Moini, S. H., (2012), “A new hybrid model for evaluating the working strategies: case study of construction company”, Technological and Economic Development of Economy, 18 (1): 164-188.
  • Gallab, M., Bouloiz, H., Garbolino, E., Alaoui Y. L. ve Tkiouat, M., (2019), “Risk Assessment of Main-tenance activities using Fuzzy Logic”, Procedia Computer Science, 148: 226-235.
  • Gallab, M., Bouloiz, H., Garbolino, E., Tkiouat, M., Elkilani M. A. ve Bureau, N., (2017), “Risk analysis of maintenance activities in a LPG supply chain with a Multi-Agent approach”, Journal of Loss Pre-vention in the Process Industries, 47: 41-56.
  • Giannakis, M., ve Papadopoulos, T., (2016), “Supply chain sustainability: A risk management approach”, International Journal of Production Economics, 171 (4): 455-470.
  • Hsieh, T.Y., Lu, S.T. ve Tzeng, G.H., (2004), “Fuzzy MCDA approach for planning and design tenders selection in public office buildings”, International Journal of Project Management, 22 (7): 573–584.
  • Hsu, P. Y., Aurisiccihio, M. ve Angeloudis, P., (2019), “Risk-averse supply chain for modular construction projects”, Automation in Construction, 106: 1-10.
  • İnternet: Taşdemir, O., Dilaver, M. ve Sönmez, Y.M., (2016), Proses Tehlike Analizlerindeki Belirsizlikle-rin Bulanık Mantık İle Kantitatifleştirilmesi”, https://www.proscon.com.tr/proses-tehlike-analizlerindeki-belirsizliklerin bulanik-mantik-ile-kantitatiflestirilmesi/.
  • Jajja, M. S. S., Chatha, K. A. ve Farooq, S., (2018), “Impact of supply chain risk on agility performance: Mediating role of supply chain integration”, International Journal of Production Economics, 205: 118-138.
  • Jiang, B., Li, J. ve Shen, S., (2018), “Supply Chain Risk Assessment and Control of Port Enterprises: Qingdao port as case study”, The Asian Journal of Shipping and Logistics, 34 (3): 198-208.
  • Jovic, S., (2014), “Gıda sektöründe tedarik zinciri risk faktörlerinin belirlenmesi”, Yüksek Lisans Tezi, Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul, 1-9.
  • Kaklauskas, A., Zavadskas, E.K., Raslanas, S., Gınevıcıus, R., Komka, A. ve Malinauskas, P., (2006), “Selection Of Low-E Windows İn Retrofit Of Public Buildings By Applying Multiple Criteria Met-hod COPRAS: A Lithuanian Case”, Energy and Buildings, 38 (5):454-462.
  • Katrancı A., ve Kundakçı, N., (2020), “SWARA Temelli Bulanık COPRAS Yöntemi ile Soğuk Hava De-posu Seçimi”, Optimum Ekonomi ve Yönetim Bilimleri Dergisi, 7 (1): 63-80.
  • Keršuliene, V., Zavadskas, E. K., ve Turskis, Z., (2010), “ Selection of Rational Dispute Resolution Met-hod by Applying New Step-Wise Weight Assessment Ratio Analysis (SWARA)”, Journal of Busi-ness Economics and Management, 11 (2): 243– 258.
  • Leblanc, D. I., Villeneuve, S., Beni, L. H., Otten, A., Fazil, A., McKellar, R. ve Delaquis, P., (2015), “A national produce supply chain database for food safety risk analysis”, Journal of Food Engineering, 147: 24-38.
  • Mavi, R.K., Goh, M. ve Zarbakhshnia, N., (2017), “Sustainable third-party reverse logistic provider selec-tion with fuzzy SWARA and fuzzy MOORA in plastic industry“, The International Journal of Ad-vanced Manufacturing Technology, 91: 5-8.
  • Mensaha, P., Merkuryeva, Y., Klavinsa, E. ve Manakb, S., (2017), “Supply Chain Risks Analysis of a Logging Company: Conceptual Model”, Procedia Computer Science, 104: 313-320.
  • Ming, C. T., Cheng, M., Bin, S. ve Qi, S., (2019), “Optimal pricing in mass customization supply chains with risk-averse agents and retail competition”, Omega, 88: 150-161.
  • Nagurney, A., Jose C., June D. ve Ding Z., (2005), “Supply Chain Networks, Electronic Commerce and Supply Side and Demand Side Risk”, European Journal of Operational Research, 164 (1): 120-142.
  • Nguyen, H. T., Dawal, S. Z. M., Nukman, Y., Aoyama, H. ve Case, K., (2015),“An integrated approach of fuzzy linguistic preference based AHP and fuzzy COPRAS for machine tool evaluation”, Plos one, 10 (9): 1-24.
  • Oliveira, F. N., Leiaras, A. ve Ceryno, P., (2019), “Environmental risk management in supply chains: A taxonomy, a framework and future research avenues”, Journal of Cleaner Production, 232: 1257-1271.
  • Peng, H., ve Pang, T., (2019), “Optimal strategies for a three-level contract-farming supply chain with sub-sidy”, International Journal of Production Economics, 216: 274-286.
  • Podvezko V., (2011), “The Comparative Analysis Of MCDA Methods SAW And COPRAS”, Inzinerine Ekonomika-Engineering Economics, 22 (2):134-146.
  • Prakash, A., Agarwal, A, ve Kumar, A.,(2018), “Risk Assessment in Automobile Supply Chain”, Materials Today: Proceedings, 5(2): 3571-3580.
  • Rajesh, R., ve Ravi, V., (2015), “Modeling enablers of supply chain risk mitigation in electronic supply chains: A Grey–DEMATEL approach”, Computers & Industrial Engineering, 87: 126-139.
  • Raza, S. A., ve Govindaluri, S. M., (2019), “Pricing strategies in a dual-channel green supply chain with cannibalization and risk aversion”, Operations Research Perspectives, 6: 1- 14.
  • Sabouhi, F., Psihvaee, M. S. ve Jabalemeli, M. S., (2018), “Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain”, Computers & Industrial Engineering, 126: 657-672.
  • Schaefer, T., Udenio, M., Quinn, S. ve Fransoo, J. C.,(2019), “Water risk assessment in supply chains”, Journal of Cleaner Production, 208: 636-648. Serrano, A., Oliva, R ve Kraiselburd, S., (2018), “Risk propagation through payment distortion in supply chains”, Journal of Operations Management, 58-59: 1-14.
  • Shojei, P., ve Haeri, S. A. S., (2019), “Development of supply chain risk management approaches for construction projects: A grounded theory approach”, Computers & Industrial Engineering, 128: 837-850.
  • Silva, C., Pavoa, A. P. B. ve Carvalho, A.,(2019), “Green Supply Chain: Integrating Financial Risk Measu-res while Monetizing Environmental Impacts”, Computer Aided Chemical Engineering, 46: 1549-1554.
  • Şimşir, F. Demir, H.İ. ve Azdemir, S., "Demir Çelik Sektöründe Hibrid DEMATEL ve TOPSİS-ELECTRE Yöntemleri ile Hata Türleri ve Etkileri Analizi", Academic Platform Journal of Engineering and Sci-ence, c. 6, sayı. 3, ss. 22-34, Eyl. 2018, doi:10.21541/apjes.455767
  • Thun, J. H., ve Hoeing, D., (2011), “An empirical analysis of supply chain risk management in the German automotive industry”, International Journal of Production Economics, 131 (1): 242-249.
  • Valinejad, F., ve Rahmani, D., (2018), “Sustainability risk management in the supply chain of telecommu-nication companies: A case study”, Journal of Cleaner Production, 203: 53-67.
  • Venkatesh, V. G., Rathi, S. ve Patwa, S., (2015), “Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using Interpretive structural modeling”, Journal of Retailing and Consumer Services, 26: 153-167.
  • Vilko, J., Ritala, P. ve Hallikas, J., (2019), “Risk management abilities in multimodal maritime supply cha-ins: Visibility and control perspectives”, Accident Analysis & Prevention, 123: 469-481.
  • Wu, Y., Jia, W., Li, I., Song, Z., Xu, C. ve Liu, F.,(2019), “Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation”, Energy, 182: 397-411.
  • Yang, B.,(2019), “Construction of logistics financial security risk ontology model based on risk association and machine learning”, Safety Science, 123: 1-10,
  • Yazdani, M., Alidoosti, A. ve Zavadskas, E. K., (2011), “Risk analysis of critical infrastructures using fuzzy COPRAS”, Economic Research-Ekonomska İstraživanja, 24 (4): 27-40.
There are 45 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Pelin Poyraz 0000-0003-4629-9514

Fuat Şimşir 0000-0001-7001-5951

Early Pub Date March 10, 2022
Publication Date March 10, 2022
Submission Date May 17, 2021
Published in Issue Year 2022 Volume: 8 Issue: 1

Cite

APA Poyraz, P., & Şimşir, F. (2022). Tedarik Zinciri Risk Yönetiminde Bulanık Çok Kriterli Karar Verme Yöntemleri ile Süreç Bazlı Hata Türleri Analizi. Journal of Advanced Research in Natural and Applied Sciences, 8(1), 86-104. https://doi.org/10.28979/jarnas.937779
AMA Poyraz P, Şimşir F. Tedarik Zinciri Risk Yönetiminde Bulanık Çok Kriterli Karar Verme Yöntemleri ile Süreç Bazlı Hata Türleri Analizi. JARNAS. March 2022;8(1):86-104. doi:10.28979/jarnas.937779
Chicago Poyraz, Pelin, and Fuat Şimşir. “Tedarik Zinciri Risk Yönetiminde Bulanık Çok Kriterli Karar Verme Yöntemleri Ile Süreç Bazlı Hata Türleri Analizi”. Journal of Advanced Research in Natural and Applied Sciences 8, no. 1 (March 2022): 86-104. https://doi.org/10.28979/jarnas.937779.
EndNote Poyraz P, Şimşir F (March 1, 2022) Tedarik Zinciri Risk Yönetiminde Bulanık Çok Kriterli Karar Verme Yöntemleri ile Süreç Bazlı Hata Türleri Analizi. Journal of Advanced Research in Natural and Applied Sciences 8 1 86–104.
IEEE P. Poyraz and F. Şimşir, “Tedarik Zinciri Risk Yönetiminde Bulanık Çok Kriterli Karar Verme Yöntemleri ile Süreç Bazlı Hata Türleri Analizi”, JARNAS, vol. 8, no. 1, pp. 86–104, 2022, doi: 10.28979/jarnas.937779.
ISNAD Poyraz, Pelin - Şimşir, Fuat. “Tedarik Zinciri Risk Yönetiminde Bulanık Çok Kriterli Karar Verme Yöntemleri Ile Süreç Bazlı Hata Türleri Analizi”. Journal of Advanced Research in Natural and Applied Sciences 8/1 (March 2022), 86-104. https://doi.org/10.28979/jarnas.937779.
JAMA Poyraz P, Şimşir F. Tedarik Zinciri Risk Yönetiminde Bulanık Çok Kriterli Karar Verme Yöntemleri ile Süreç Bazlı Hata Türleri Analizi. JARNAS. 2022;8:86–104.
MLA Poyraz, Pelin and Fuat Şimşir. “Tedarik Zinciri Risk Yönetiminde Bulanık Çok Kriterli Karar Verme Yöntemleri Ile Süreç Bazlı Hata Türleri Analizi”. Journal of Advanced Research in Natural and Applied Sciences, vol. 8, no. 1, 2022, pp. 86-104, doi:10.28979/jarnas.937779.
Vancouver Poyraz P, Şimşir F. Tedarik Zinciri Risk Yönetiminde Bulanık Çok Kriterli Karar Verme Yöntemleri ile Süreç Bazlı Hata Türleri Analizi. JARNAS. 2022;8(1):86-104.


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