BANKA SEKTÖRÜNDE İNSAN HATA ANALİZİ İÇİN YENİ BİR BÜTÜNLEŞİK YÖNTEM: İFASS&ÇK-KBDTK

Yıl 2018, Cilt: 1 Sayı: 2, 108 - 122, 31.08.2018

Tuba Adar Elif Kılıç Delice

https://doi.org/10.33439/ergonomi.482091

Cited By: 1

Öz

Bu çalışmada amaç; banka çalışanlarının hata yapmasında etkili olan
faktörleri ve insan hatalarını önleyecek önlemleri belirlemektir. Çalışmada 2
yöntem kullanılmıştır. Biri, Reason’ın gizli ve faal hatalarını ana hatları ile
belirlemek için dört hata seviyesinden oluşan, İnsan Faktör Analizi ve
Sınıflandırma Sistemi (İFASS)’dir. Diğer yöntem, karar vericinin
serbest-içerikli dilsel terim setleri kullanılarak görüşlerini ifade etmesinde
zenginlik ve farklı ifadeler kullanma esnekliği sağlayan ve temeli bulanık
dilbilgisine dayanan Çok-Kriterli Kararsız Bulanık Dilsel Terim Seti (ÇK-KBDTS)
yöntemidir. Elde edilen sonuçlara göre, ana faktörlerden Personel faktörü en
çok ağırlığa sahipken, alt kriterlerin global ağırlıklarına göre Fiziksel
Çevre, Teknolojik Çevre sonrasında Zihinsel Sınırlamalar en önemli kriterler
olarak belirlenmiştir. Bankacılık hizmet sektöründe çalışanlarda parasal
mevzularda hata yapma ve bunun sonucunun nelere sebep açacağı korkusu ve
baskısı nedeniyle zihinsel sınırlamaların payı yüksektir. Literatüre
bakıldığında İFASS yönteminin hizmet sektöründe sadece hastanelerde uygulandığı
görülmüştür. Bu çalışmada ilk defa, bankacılık sektöründe hata faktörleri ele
alınmış ve İFASS&ÇK-KBDTS yöntemleri entegre edilmiştir. Bu nedenle çalışmanın
literatüre katkısı olacağı düşünülmektedir.

Anahtar Kelimeler

İnsan Faktör Analizi ve Sınıflandırma Sistemi, Kararsız Bulanık Dilsel Terim Seti, Hata Faktörleri, Banka

Kaynakça

• Adem, A., & Dağdeviren, M. (2016). A life insurance policy selection via hesitant fuzzy linguistic decision making model. Procedia Computer Science, 102, 398-405.
• Aktas, A., & Kabak, M. (2016). A model proposal for locating wind turbines. Procedia Computer Science, 102, 426-433.
• Atanassov, K. T. (1999). Intuitionistic fuzzy sets. Intuitionistic fuzzy sets, Springer, 1-13
• Baber, E. (2007). Kazaların Çevresel ve Teknik Araştırılması. Yüksek Lisans Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü.
• Cap 718. (2002). Human Factors in Aircraft Maintenance and Inspection. Civil Aviation Authority, UK, 9-11.
• Chen, Z.-S., Chin, K.-S., Li, Y.-L., & Yang, Y. (2016). Proportional hesitant fuzzy linguistic term set for multiple criteria group decision making. Information Sciences, 357, 61-87.
• Chiu, M. C., & Hsieh, M. C. (2016). Latent human error analysis and efficient improvement strategies by fuzzy TOPSIS in aviation maintenance tasks. Applied ergonomics, 54, 136-147.
• Da, T., & Xu, Y. (2016). Evaluation on connectivity of urban waterfront redevelopment under hesitant fuzzy linguistic environment. Ocean & Coastal Management, 132, 101-110.
• Demirel, T., Öner, S. C., Tüzün, S., Deveci, M., Öner, M. and Demirel, N. Ç. (2018). Choquet integral-based hesitant fuzzy decision-making to prevent soil erosion. Geoderma. 313, 276-289.
• Dong, J.-y., F.-f. Yuan and S.-p. Wan, (2017). Extended VIKOR method for multiple criteria decision-making with linguistic hesitant fuzzy information. Computers & Industrial Engineering, 112, 305-319.
• Erjavac, A. J., Iammartino, R., & Fossaceca, J. M. (2018). Evaluation of preconditions affecting symptomatic human error in general aviation and air carrier aviation accidents. Reliability Engineering & System Safety.
• Fahmi, A., Kahraman, C., & Bilen, Ü. (2016). ELECTRE I method using hesitant linguistic term sets: An application to supplier selection. International Journal of Computational Intelligence Systems, 9, 153-167.
• Gou, X., Liao, H., Xu, Z., & Herrera, F. (2017). Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: A case of study to evaluate the implementation status of haze controlling measures. Information Fusion, 38, 22-34.
• Gou, X., Z. Xu and H. Liao, (2017). Hesitant fuzzy linguistic entropy and cross-entropy measures and alternative queuing method for multiple criteria decision making. Information Sciences, 225-246.
• Herrera-Viedma, E., Martinez, L., Mata, F. and Chiclana, F.. (2005). A consensus support system model for group decision-making problems with multigranular linguistic preference relations. IEEE Transactions on fuzzy Systems, 13(5), 644-658.
• Hsieh, M. C., Wang, E. M. Y., Lee, W. C., Li, L. W., Hsieh, C. Y., Tsai, W., ... & Liu, T. C. (2018). Application of HFACS, fuzzy TOPSIS, and AHP for identifying important human error factors in emergency departments in Taiwan. International Journal of Industrial Ergonomics, 67, 171-179.
• Hu, J., Zhang, L., Wang, Q., & Tian, B. (2018). A Hybrid Framework of Human Error Analysis for shale gas fracturing operation based on HAZOP and Fuzzy VIKOR. Safety Science.
• Islam, R., Khan, F., Abbassi, R., & Garaniya, V. (2018). Human error probability assessment during maintenance activities of marine systems. Safety and Health at Work, 9(1), 42-52 (a).
• Islam, R., Khan, F., Abbassi, R., & Garaniya, V. (2018). Human error assessment during maintenance operations of marine systems–What are the effective environmental factors?. Safety science, 107, 85-98 (b).
• Jahangiri, M., Hoboubi, N., Rostamabadi, A., Keshavarzi, S., & Hosseini, A. A. (2016). Human error analysis in a permit to work system: a case study in a chemical plant. Safety and health at work, 7(1), 6-11.
• Jiang, Y. (2007). An approach to group decision making based on interval fuzzy preference relations. Journal of Systems Science and Systems Engineering, 16(1), 113-120.
• Karnik, N. N., Mendel, J. M. and Liang, Q. (1999). Type-2 fuzzy logic systems. IEEE transactions on Fuzzy Systems, 7(6), 643-658.
• Khishtandar, S., Zandieh, M. and Dorri, B. (2017). A multi criteria decision making framework for sustainability assessment of bioenergy production technologies with hesitant fuzzy linguistic term sets: The case of Iran. Renewable and Sustainable Energy Reviews, 77, 1130-1145.
• Liao, H., & Xu, Z. (2015). Approaches to manage hesitant fuzzy linguistic information based on the cosine distance and similarity measures for HFLTSs and their application in qualitative decision making. Expert Systems with Applications, 42, 5328-5336.
• Liu, H., & Rodríguez, R. M. (2014). A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multi criteria decision making. Information Sciences. 258, 220-238.
• Montes, R., A. M. Sanchez, P. Villar and F. Herrera, (2017). Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust. Applied Soft Computing.
• Montes, R., Sánchez, A. M., Villar, P., & Herrera, F. (2015). A web tool to support decision making in the housing market using hesitant fuzzy linguistic term sets. Applied Soft Computing. 35, 949-957.
• Morag, I., Chemweno, P., Pintelon, L., & Sheikhalishahi, M. (2018). Identifying the causes of human error in maintenance work in developing countries. International Journal of Industrial Ergonomics, 68, 222-230.
• Nguyen, J., G. Sánchez-Hernández, A. Armisen, N. Agell, X. Rovira and C. Angulo, (2017). A linguistic multi-criteria decision-aiding system to support university career services. Applied Soft Computing.
• NTSB/ARG-07/01. (2006). Annual Review of Aircraft Accident Data. National Transportation Safety Board, Washington DC.Olivares, R. D. C., Rivera, S. S., & Mc Leod, J. E. N. (2018). A novel qualitative prospective methodology to assess human error during accident sequences. Safety science, 103, 137-152.
• Onar, S. Ç., Büyüközkan, G., Öztayşi, B., & Kahraman, C. (2016). A new hesitant fuzzy QFD approach: an application to computer workstation selection. Applied Soft Computing, 46, 1-16.
• Petrillo, A., Falcone, D., De Felice, F., & Zomparelli, F. (2017). Development of a risk analysis model to evaluate human error in industrial plants and in critical infrastructures. International journal of disaster risk reduction, 23, 15-24.
• Reason, J. (1990). Human Error. Cambridge University, United States of America, 1-90.
• Rodríguez, R. M., Liu, H. and Martínez, L. (2014). A fuzzy representation for the semantics of hesitant fuzzy linguistic term sets. Foundations of Intelligent Systems, Springer, 745-757.
• RodríGuez, R. M., Martınez, L. and Herrera, F. (2013). A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Information Sciences, 241, 28-42.
• Rodriguez, R. M., Martinez, L. and Herrera, F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109-119.
• Senvar, O., Otay, I., & Bolturk, E. (2016). Hospital site selection via hesitant fuzzy TOPSIS. IFAC-PapersOnLine, 49, 1140-1145.
• Song, Y. and J. Hu, (2017). A group decision-making model based on incomplete comparative expressions with hesitant linguistic terms. Applied Soft Computing, 59, 174-181.
• Topraklı, A. Y., Adem, A., & Dağdeviren, M. (2016). A Courthouse Site Selection Method Using Hesitant Fuzzy Linguistic Term Set: A Case Study For Turkey. Procedia Computer Science, 102, 603-610.
• Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529-539.Umano, M., Hatono, I. and Tamura, H. (1998). Linguistic labels for expressing fuzzy preference relations in fuzzy group decision making. IEEE Transactions on
• Systems, Man, and Cybernetics, Part B (Cybernetics), 28(2), 205-218.Ung, S. T. (2018). Human error assessment of oil tanker grounding. Safety science, 104, 16-28.
• Wang, J., Wang, J.-q., &Zhang, H.-y. (2016). A likelihood-based TODIM approach based on multi-hesitant fuzzy linguistic information for evaluation in logistics outsourcing. Computers & Industrial Engineering, 99, 287-299.
• Wang, L., Wang, Y., Cao, Q., Li, X., Li, J., & Wu, X. (2014). A framework for human error risk analysis of coal mine emergency evacuation in China. Journal of Loss Prevention in the Process Industries, 30, 113-123.
• Wang, W., Liu, X., & Qin, Y. (2018). A modified HEART method with FANP for human error assessment in high-speed railway dispatching tasks. International Journal of Industrial Ergonomics, 67, 242-258.
• Wiegmann D.A., Shappell S.A. (2005). A Human Error Approach to Aviation Accident Analysis. Ashgate Publishing Company, İngiltere, 1-98.
• Wu, H., Z. Xu, P. Ren and H. Liao, (2018). Hesitant fuzzy linguistic projection model to multi-criteria decision making for hospital decision support systems. Computers & Industrial Engineering, 115, 449-458.
• Wu, Y., Y. Wang, K. Chen, C. Xu and L. Li, (2017). Social sustainability assessment of small hydropower with hesitant PROMETHEE method. Sustainable Cities and Society, 35, 522-537.
• Wu, Z., & Xu, J. (2016). Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations. Omega, 65, 28-40.
• Xiao, J., Cai, J., Wang, X. (2017). A Hesitant Fuzzy Linguistic Multicriteria Decision-Making Method with Interactive Criteria and Its Application to Renewable Energy Projects Selection. Mathematical Problems in Engineering, pp:15.
• Yavuz, M., Oztaysi, B., Onar, S. C., & Kahraman, C. (2015). Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model. Expert Systems with Applications, 42, 2835-2848.
• Yu, W., Z. Zhang, Q. Zhong and L. Sun, (2017). Extended TODIM for multi-criteria group decision making based on unbalanced hesitant fuzzy linguistic term sets. Computers & Industrial Engineering, 114, 316-328.
• Yu, W.-Y., Zhong, Q.-Y., & Zhang, Z. (2016). Fusing multi-granular unbalanced hesitant fuzzy