Araştırma Makalesi
BibTex RIS Kaynak Göster

CRITICAL PART ANALYSIS IN SCM WITH MATHEMATICAL APPROACHES BASED ON GROUP DECISION MAKING: A REAL LIFE APPLICATION OF AUTOMOTIVE INDUSTRY

Yıl 2024, Cilt: 35 Sayı: 3, 410 - 436

Öz

Nowadays, where the competitive business environment is getting deeper, improvements in Supply Chain Management (SCM) operations, which fundamentally affect the profitability, supply agility and process optimization of companies, are gaining importance. SCM is a process that aims to reduce costs and increase efficiency by managing the operations from the supply of raw materials to the delivery of final products to customers. SCM includes complex decision problems that require different decisions to be made under conflicting factors such as quality, cost and delivery time, and due to this structure, these problems are suitable to be addressed as Multi-Criteria Decision Making (MCDM) problems. In this study, the improvement of SCM processes of an international company was analyzed, and a solution approach based on mathematical methods was developed for the problem identified as a result of the root cause analysis carried out in the real-life application. In the study, first of all, the SCM process of Turkey-produced export products of a European-based automotive company was examined; root cause analysis was made for the problem with the fastest and least costly solution potential; the decision problem was addressed to eliminate the determined root cause was structured; and than, was solved with an approach based on the Best-Worst Method (BWM) - Combined Compromise Solution (CoCoSo). Multiple decision makers who manage the operations related to the strategic goals were employed in decision process in order to develop a solution that could simultaneously meet all three strategically determined managerial goals of the company. In this context; unlike the existing literature examples, the "K2: size and physical compatibility" criterion had the highest importance value, while, Supplier C was determined as the best alternative. The problem dimensions, factors and parameters considered in the decision problem were explained in depth to the readers, and, the results obtained were supported by detailed visual representations and presented to scientific researchers and field practitioners.

Etik Beyan

Research and publication ethics were complied within this article.

Destekleyen Kurum

-

Teşekkür

-

Kaynakça

  • Adar, E., Delice, E.K. ve Adar, T. (2022). Prioritizing of industrial wastewater management processes using an integrated AHP–CoCoSo model: comparative and sensitivity analyses. International Journal of Environmental Science and Technology, 19, 4691–4712. Doi: https://doi.org/10.1007/s13762-021-03511-y
  • Ahmadi, H. B., Kusi-Sarpong, S., ve Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99-106. Doi: https://doi.org/10.1016/j.resconrec.2017.07.020
  • Akbulut, O. Y., ve Hepşen, A. (2021). Finansal performans ve pay senedi getirileri arasındaki ilişkinin Entropi ve CoCoSo ÇKKV teknikleriyle analiz edilmesi. Ekonomi Politika ve Finans Araştırmaları Dergisi, 6(3), 681-709. Doi: https://doi.org/10.30784/epfad.945770
  • Andrejić, M., ve Pajić, V. (2023). Optimizing personnel selection in transportation: an application of the BWM-CoCoSo decision-support model. Journal of Organizations, Technology and Entrepreneurship, 1(1), 35-46. Doi: https://doi.org/10.56578/jote010103
  • Chang, T. L., Chen, K., ve Liou, J. (2019). A novel FMEA model based on rough BWM and rough TOPSIS-AL for risk assessment. Mathematics, 7(10), 874. Doi: https://doi.org/10.3390/math7100874
  • Chopra, S., ve Meindll, P. (2013). Supply chain management: Strategy, planning, and operation. New York: Pearson.
  • Christopher, M. (2016). Logistics and Supply Chain Management: Logistics & Supply Chain Management. New York: Pearson.
  • Çakır, E., ve Can, M. (2019). Best-worst yöntemine dayalı ARAS yöntemi ile dış kaynak kullanım tercihinin belirlenmesi: Turizm sektöründe bir uygulama. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 23(3), 1273-1300.
  • Dickson, G. W. (1966). An analysis of vendor selection systems and decisions. Journal of Purchasing, 2(1), 5-17. Doi: https://doi.org/10.1111/j.1745-493X.1966.tb00818.x
  • Google Resimler: “Otobüs havalandırma filtresi çeşitleri” (2024). Erişim adresi: https://www.google.com/search?sca_esv=7d41e6a1da68bbf8&sca_upv=1&q=otob%C3%BCs+havaland%C4%B1rma+filtresi+%C3%A7e%C5%9Fitleri&udm=2&fbs=AEQNm0CSvsjWvChtArk22jMGgQq8FH_E5B5QsNrgRF4E2T2FwGVSQOMJu09QlJxnIjpqFpJb-4gJHilnHeiNcslTZzypL6d3QvR-lFOdJ6Mxi10B_TI7J1sMbYfS96hoq8L95FxAC60iqNSomCj6aznr37LuWxOLsyI9yzRhQ-m1xWGVFAFxjQ58UYdZLNYWcAGLyJADeDwOwuU3Eia7rEcfIDHM8OZl3A&sa=X&ved=2ahUKEwjbh_r0rMSHAxU4HrkGHeXrMkUQtKgLegQIExAB&biw=1920&bih=927&dpr=1,
  • Guo, S., ve Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31. Doi: https://doi.org/10.1016/j.knosys.2017.01.010
  • Gupta, H., ve Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152, 242-258. Doi: https://doi.org/10.1016/ j.jclepro.2017.03.125
  • Jahan, A., Ismail, M., Mustapha, F., ve Sapuan, S. (2010). Material selection based on ordinal data. Materials and Design, 31, 3180–3187. Doi: https://doi.org/10.1016/j.matdes.2010.02.024
  • Kara, M. E., ve Fırat, O. (2018). Supplier risk assessment based on Best-Worst Method and K-means clustering: A case study. Sustainability, 10(4), 1066. Doi: https://doi.org/10.3390/su10041066
  • Karşıgil, G. (2024). Tedarik zinciri yönetimi optimizasyonunda tedarikçi seçimi probleminin grup karar verme yöntemleri ile matematiksel olarak analizi (Tezsiz Yüksek Lisans Dönem Projesi ) Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Anabilim Dalı, Ankara.
  • Kılıç Delice, E., ve Can, G.F. (2020). A new approach for ergonomic risk assessment integrating KEMIRA, Best–Worst and MCDM methods. Soft Computing, 24, 15093–15110. Doi: https://doi.org/10.1007/s00500-020-05143-9
  • Mavi, R. K., ve Standing, C. (2018). Critical success factors of sustainable project management in construction: A fuzzy DEMATEL-ANP approach. Journal of Cleaner Production, 194, 751-765. Doi: https://doi.org/10.1016/j.jclepro.2018.05.120
  • Norouzi, A., ve Namin, H. G. (2019). A Hybrid Fuzzy TOPSIS – Best Worst Method for Risk Prioritization in Megaprojects. Civil Engineering Journal, 5(6), 1257–1272. Doi: 10.28991/cej-2019-03091330
  • Pamucar, D., Ulutaş, A., Topal, A., Karamaşa, Ç., & Ecer, F. (2024). Fermatean fuzzy framework based on preference selection index and combined compromise solution methods for green supplier selection in textile industry. International Journal of Systems Science: Operations & Logistics, 11(1), 2319786. Doi: https://doi.org/10.1080/23302674.2024.2319786
  • Razzaq, A., Riaz, M., & Aslam, M. (2024). Efficient picture fuzzy soft CRITIC-CoCoSo framework for supplier selection under uncertainties in Industry 4.0. AIMS Mathematics, 9(1), 665-701. Doi: 10.3934/math.2024035
  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. Doi: https://doi.org/10.1016/j.omega.2014.11.009
  • Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130. Doi: 10.1016/j.omega.2015.12.001
  • Sotoudeh-Anvari, A., ve Mousavi-Nasab, S. H. (2018). A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem. Journal of Cleaner Production, 182, Doi: 10.1016/j.jclepro.2018.02.062
  • Torkayesh, A. E., Chatterjee, P., ve Yazdani, M. (2020). An integrated decision-making model for supplier evaluation in public healthcare system: the case study of a Spanish hospital. Operations Research Perspectives, 7, 100163. Doi: https://doi.org/10.1108/JEIM-09-2019-0294
  • Wei, D., Meng, D., Rong, Y., Liu, Y., Garg, H., & Pamucar, D. (2022). Fermatean Fuzzy Schweizer–Sklar operators and BWM-entropy-based combined compromise solution approach: an application to green supplier selection. Entropy, 24(6), 776. Doi: https://doi.org/10.3390/e24060776
  • Wu, C., Lin, Y., ve Barnes, D. (2019). An integrated decision-making approach for sustainable supplier selection in the chemical industry. Journal of Cleaner Production, 222, 36-50. Doi: https://doi.org/10.1016/j.eswa.2021.115553
  • Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management decision, 57(9), 2501-2519. Doi: https://doi.org/10.1108/MD-05-2017-0458
  • Yazdani, M., Wen, Z., Liao, H., Banaitis, A., & Turskis, Z. (2019). A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. Journal of Civil Engineering and Management, 25(8), 858-874. Doi: https://doi.org/10.3846/jcem.2019.11309
  • Yılmaz, B. (2010). Ekipman Seçimi Problemi İçin Bulanık PROMETHEE Ve 0-1 Hedef Programlama Yöntemlerinin Bütünleşik Kullanımı (Yüksek Lisans Tezi) Gazi Üniversitesi, Fen Bilimler Enstitüsü, Endüstri Mühendisliği Anabilim Dalı, Ankara.
  • Yılmaz Kaya, B. (2022a). Minimizing OHS risks with spherical fuzzy sets as a verdict to inventory management: A case regarding energy companies. Discrete Dynamics in Nature & Society, 3, 12-32. Doi: https://doi.org/10.1155/2022/9511339
  • Yılmaz Kaya, B. (2022b). Human factors engineering on the edge of industry 4.0: Analysis for IoT-aided technologies. Endüstri Mühendisliği, 33(1), 1-21. Doi: https://doi.org/10.46465/endustrimuhendisligi.1025701
  • Zolfani, S. H., Chatterjee, P. ve Yazdani, M. (2019). A structured framework for sustainable supplier selection using a combined BWM-CoCoSo model. International Scientific Conference Contemporary Issues in Business, Management and Economics Engineering’2019 toplantısında sunulan bildiri, Vilnius: Vilnius Tech, 797-804. Doi: https://doi.org/10.3846/cibmee.2019.081

GRUP KARAR VERMEYE DAYALI MATEMATİKSEL YAKLAŞIMLAR İLE TZY’DE KRİTİK PARÇA ANALİZİ: OTOMOTİV SEKTÖRÜNDE BİR GERÇEK HAYAT UYGULAMASI

Yıl 2024, Cilt: 35 Sayı: 3, 410 - 436

Öz

Rekabetçi iş ortamının gittikçe derinleştiği günümüzde şirketlerin karlılığını, arz çevikliğini ve süreç optimizasyonunu temelden etkileyen Tedarik Zinciri Yönetimi (TZY) operasyonlarındaki iyileştirmeler önem kazanmaktadır. TZY, hammaddelerin tedariğinden nihai ürünlerin müşterilere teslimine kadar olan süreçleri yöneterek maliyetleri düşürmeyi ve verimliliği artırmayı amaçlayan bir süreçtir. TZY içerisinde kalite, maliyet ve teslimat süresi gibi birbiriyle çelişen faktörler altında farklı kararlarının alınmasını gerektiren karmaşık karar problemleri barındırır, ve yapıları gereği bu problemler Çok Kriterli Karar Verme (ÇKKV) problemi olarak ele alınmaya uygundur. Bu çalışmada uluslararası bir firmanın TZY süreçlerinin iyileştirilmesi analiz edilmiş, ele alınan gerçek hayat uygulamasında yürütülen kök neden analizi sonucunda belirlenen problem için matematiksel yöntemleri taban alan bir çözüm yaklaşımı geliştirilmiştir. Çalışmada öncelikle Avrupa merkezli otomotiv firmasının Türkiye üretimli ihraç ürünlerinin TZY süreci incelenmiş, en hızlı ve en az maliyetli çözüm potansiyeline sahip problem için kök neden analizi yapılmış, belirlenen kök nedenin ortadan kaldırılması için ele alınan karar problemi yapılandırılmış ve En İyi-En Kötü Yöntemi (Best Worst Method – BWM) - Birleştirilmiş Uzlaşık Çözüm (Combined Compromise Solution - CoCoSo) temelli bir yaklaşım ile sonuca ulaşturulmıştır. Uygulama çalışmasında firmanın stratejik olarak belirlemiş olduğu üç yönetimsel amacı eşzamanlı sağlayabilecek çözümün geliştirilebilmesi adına bu stratejik hedeflere bağlı operasyonları yöneten birden çok karar verici yer almıştır. Bu bağlamda; mevcut literatür örneklerinden farklı olarak “K2:boyut ve fiziksel uyumluluk” kriteri en yüksek ağırlık değerine sahip olurken, Tedarikçi C en iyi alternatif olarak belirlenmiştir. Karar probleminde ele alınan problem boyutları, faktör ve parametreler okuyuculara derinlemesine açıklanmış ve elde edilen sonuçlar detaylı şekilsel gösterimlerle desteklenerek bilimsel araştırmacıların ve alan uygulayıcılarının kullanımına sunulmuştur.

Etik Beyan

Bu makalede araştırma ve yayın etiğine uyulmuştur.

Destekleyen Kurum

-

Teşekkür

-

Kaynakça

  • Adar, E., Delice, E.K. ve Adar, T. (2022). Prioritizing of industrial wastewater management processes using an integrated AHP–CoCoSo model: comparative and sensitivity analyses. International Journal of Environmental Science and Technology, 19, 4691–4712. Doi: https://doi.org/10.1007/s13762-021-03511-y
  • Ahmadi, H. B., Kusi-Sarpong, S., ve Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99-106. Doi: https://doi.org/10.1016/j.resconrec.2017.07.020
  • Akbulut, O. Y., ve Hepşen, A. (2021). Finansal performans ve pay senedi getirileri arasındaki ilişkinin Entropi ve CoCoSo ÇKKV teknikleriyle analiz edilmesi. Ekonomi Politika ve Finans Araştırmaları Dergisi, 6(3), 681-709. Doi: https://doi.org/10.30784/epfad.945770
  • Andrejić, M., ve Pajić, V. (2023). Optimizing personnel selection in transportation: an application of the BWM-CoCoSo decision-support model. Journal of Organizations, Technology and Entrepreneurship, 1(1), 35-46. Doi: https://doi.org/10.56578/jote010103
  • Chang, T. L., Chen, K., ve Liou, J. (2019). A novel FMEA model based on rough BWM and rough TOPSIS-AL for risk assessment. Mathematics, 7(10), 874. Doi: https://doi.org/10.3390/math7100874
  • Chopra, S., ve Meindll, P. (2013). Supply chain management: Strategy, planning, and operation. New York: Pearson.
  • Christopher, M. (2016). Logistics and Supply Chain Management: Logistics & Supply Chain Management. New York: Pearson.
  • Çakır, E., ve Can, M. (2019). Best-worst yöntemine dayalı ARAS yöntemi ile dış kaynak kullanım tercihinin belirlenmesi: Turizm sektöründe bir uygulama. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 23(3), 1273-1300.
  • Dickson, G. W. (1966). An analysis of vendor selection systems and decisions. Journal of Purchasing, 2(1), 5-17. Doi: https://doi.org/10.1111/j.1745-493X.1966.tb00818.x
  • Google Resimler: “Otobüs havalandırma filtresi çeşitleri” (2024). Erişim adresi: https://www.google.com/search?sca_esv=7d41e6a1da68bbf8&sca_upv=1&q=otob%C3%BCs+havaland%C4%B1rma+filtresi+%C3%A7e%C5%9Fitleri&udm=2&fbs=AEQNm0CSvsjWvChtArk22jMGgQq8FH_E5B5QsNrgRF4E2T2FwGVSQOMJu09QlJxnIjpqFpJb-4gJHilnHeiNcslTZzypL6d3QvR-lFOdJ6Mxi10B_TI7J1sMbYfS96hoq8L95FxAC60iqNSomCj6aznr37LuWxOLsyI9yzRhQ-m1xWGVFAFxjQ58UYdZLNYWcAGLyJADeDwOwuU3Eia7rEcfIDHM8OZl3A&sa=X&ved=2ahUKEwjbh_r0rMSHAxU4HrkGHeXrMkUQtKgLegQIExAB&biw=1920&bih=927&dpr=1,
  • Guo, S., ve Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31. Doi: https://doi.org/10.1016/j.knosys.2017.01.010
  • Gupta, H., ve Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152, 242-258. Doi: https://doi.org/10.1016/ j.jclepro.2017.03.125
  • Jahan, A., Ismail, M., Mustapha, F., ve Sapuan, S. (2010). Material selection based on ordinal data. Materials and Design, 31, 3180–3187. Doi: https://doi.org/10.1016/j.matdes.2010.02.024
  • Kara, M. E., ve Fırat, O. (2018). Supplier risk assessment based on Best-Worst Method and K-means clustering: A case study. Sustainability, 10(4), 1066. Doi: https://doi.org/10.3390/su10041066
  • Karşıgil, G. (2024). Tedarik zinciri yönetimi optimizasyonunda tedarikçi seçimi probleminin grup karar verme yöntemleri ile matematiksel olarak analizi (Tezsiz Yüksek Lisans Dönem Projesi ) Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Anabilim Dalı, Ankara.
  • Kılıç Delice, E., ve Can, G.F. (2020). A new approach for ergonomic risk assessment integrating KEMIRA, Best–Worst and MCDM methods. Soft Computing, 24, 15093–15110. Doi: https://doi.org/10.1007/s00500-020-05143-9
  • Mavi, R. K., ve Standing, C. (2018). Critical success factors of sustainable project management in construction: A fuzzy DEMATEL-ANP approach. Journal of Cleaner Production, 194, 751-765. Doi: https://doi.org/10.1016/j.jclepro.2018.05.120
  • Norouzi, A., ve Namin, H. G. (2019). A Hybrid Fuzzy TOPSIS – Best Worst Method for Risk Prioritization in Megaprojects. Civil Engineering Journal, 5(6), 1257–1272. Doi: 10.28991/cej-2019-03091330
  • Pamucar, D., Ulutaş, A., Topal, A., Karamaşa, Ç., & Ecer, F. (2024). Fermatean fuzzy framework based on preference selection index and combined compromise solution methods for green supplier selection in textile industry. International Journal of Systems Science: Operations & Logistics, 11(1), 2319786. Doi: https://doi.org/10.1080/23302674.2024.2319786
  • Razzaq, A., Riaz, M., & Aslam, M. (2024). Efficient picture fuzzy soft CRITIC-CoCoSo framework for supplier selection under uncertainties in Industry 4.0. AIMS Mathematics, 9(1), 665-701. Doi: 10.3934/math.2024035
  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. Doi: https://doi.org/10.1016/j.omega.2014.11.009
  • Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130. Doi: 10.1016/j.omega.2015.12.001
  • Sotoudeh-Anvari, A., ve Mousavi-Nasab, S. H. (2018). A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem. Journal of Cleaner Production, 182, Doi: 10.1016/j.jclepro.2018.02.062
  • Torkayesh, A. E., Chatterjee, P., ve Yazdani, M. (2020). An integrated decision-making model for supplier evaluation in public healthcare system: the case study of a Spanish hospital. Operations Research Perspectives, 7, 100163. Doi: https://doi.org/10.1108/JEIM-09-2019-0294
  • Wei, D., Meng, D., Rong, Y., Liu, Y., Garg, H., & Pamucar, D. (2022). Fermatean Fuzzy Schweizer–Sklar operators and BWM-entropy-based combined compromise solution approach: an application to green supplier selection. Entropy, 24(6), 776. Doi: https://doi.org/10.3390/e24060776
  • Wu, C., Lin, Y., ve Barnes, D. (2019). An integrated decision-making approach for sustainable supplier selection in the chemical industry. Journal of Cleaner Production, 222, 36-50. Doi: https://doi.org/10.1016/j.eswa.2021.115553
  • Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management decision, 57(9), 2501-2519. Doi: https://doi.org/10.1108/MD-05-2017-0458
  • Yazdani, M., Wen, Z., Liao, H., Banaitis, A., & Turskis, Z. (2019). A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. Journal of Civil Engineering and Management, 25(8), 858-874. Doi: https://doi.org/10.3846/jcem.2019.11309
  • Yılmaz, B. (2010). Ekipman Seçimi Problemi İçin Bulanık PROMETHEE Ve 0-1 Hedef Programlama Yöntemlerinin Bütünleşik Kullanımı (Yüksek Lisans Tezi) Gazi Üniversitesi, Fen Bilimler Enstitüsü, Endüstri Mühendisliği Anabilim Dalı, Ankara.
  • Yılmaz Kaya, B. (2022a). Minimizing OHS risks with spherical fuzzy sets as a verdict to inventory management: A case regarding energy companies. Discrete Dynamics in Nature & Society, 3, 12-32. Doi: https://doi.org/10.1155/2022/9511339
  • Yılmaz Kaya, B. (2022b). Human factors engineering on the edge of industry 4.0: Analysis for IoT-aided technologies. Endüstri Mühendisliği, 33(1), 1-21. Doi: https://doi.org/10.46465/endustrimuhendisligi.1025701
  • Zolfani, S. H., Chatterjee, P. ve Yazdani, M. (2019). A structured framework for sustainable supplier selection using a combined BWM-CoCoSo model. International Scientific Conference Contemporary Issues in Business, Management and Economics Engineering’2019 toplantısında sunulan bildiri, Vilnius: Vilnius Tech, 797-804. Doi: https://doi.org/10.3846/cibmee.2019.081
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Üretim ve Endüstri Mühendisliği (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Burcu Yılmaz Kaya 0000-0002-5088-5842

Gülfidan Karşıgil 0009-0006-1414-0743

Erken Görünüm Tarihi 18 Aralık 2024
Yayımlanma Tarihi
Gönderilme Tarihi 1 Ağustos 2024
Kabul Tarihi 31 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 35 Sayı: 3

Kaynak Göster

APA Yılmaz Kaya, B., & Karşıgil, G. (2024). GRUP KARAR VERMEYE DAYALI MATEMATİKSEL YAKLAŞIMLAR İLE TZY’DE KRİTİK PARÇA ANALİZİ: OTOMOTİV SEKTÖRÜNDE BİR GERÇEK HAYAT UYGULAMASI. Endüstri Mühendisliği, 35(3), 410-436.

19736      14617      26287       15235           15236           15240      15242