Dergiler / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2025 / Cilt: 40 Sayı: 4

Hastane içi tedarik zinciri yönetimi performansının PBAHP ve PBTOPSIS yöntemleriyle karşılaştırmalı analizi

Comparative analysis of ınternal hospital supply chain management performance with PFAHP and PFTOPSIS methods

Bahar Özyörük(GAZİ ÜNİVERSİTESİ)0000-0001-5434-6697
Beste Desticioğlu Taşdemir(MİLLİ SAVUNMA ÜNİVERSİTESİ, ALPARSLAN SAVUNMA BİLİMLERİ ENSTİTÜSÜ, HAREKAT ARAŞTIRMASI ANABİLİM DALI)0000-0001-8321-4554
Sena Kumcu(GAZİ ÜNİVERSİTESİ)0000-0002-9648-6281
Bora Karacaer(GAZİ ÜNİVERSİTESİ)0000-0001-6393-4116
Sayfa
2839–2850
Yayın Tarihi
2025-12-31
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bu sayede, kamu hastanelerinde kaynakların etkin ve verimli kullanımına katkı sağlayacak bir yapı ortaya konulmuştur.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Hastanelerde hastalara kaliteli ve verimli sağlık hizmeti sunulabilmesi için etkili bir tedarik zinciri ağnını kurulması gerekmektedir.
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Temel Bulgular & Veri:Bu doğrultuda, hizmet kalitesini artırmak ve maliyetleri azaltmak amacıyla hastane iç tedarik zinciri yönetimi (HİTZY) faaliyetlerine stratejik düzeyde önem verilmesi gerekmektedir.
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Sonuç & Katkı:Bu sayede, kamu hastanelerinde kaynakların etkin ve verimli kullanımına katkı sağlayacak bir yapı ortaya konulmuştur.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:2016 Verileri
⚙️Değişkenler:PBTOPSIS · PBAHP · pisagor bulanık sayılar

Özet

Hastanelerde hastalara kaliteli ve verimli sağlık hizmeti sunulabilmesi için etkili bir tedarik zinciri ağnını kurulması gerekmektedir. Bu doğrultuda, hizmet kalitesini artırmak ve maliyetleri azaltmak amacıyla hastane iç tedarik zinciri yönetimi (HİTZY) faaliyetlerine stratejik düzeyde önem verilmesi gerekmektedir. Ayrıca, hastane süreçlerinde paydaş ve sağlık hizmeti sağlayıcılarının katılım düzeyinin değerlendirilmesi için HİTZY performans göstergelerinin belirlenmesi gerekmektedir. Bu çalışmada, hastanelerin HİTZY performanslarını değerlendirmeye yönelik çok kriterli bir karar verme modeli geliştirilmiştir. Araştırma kapsamında, 2016 yılına kadar askeri hastane olarak hizmet vermiş ve günümüzde kamu hastanesi statüsünde olan Türkiye'deki dört hastane ele alınmıştır. Literatürde bilindiği kadarıyla bu çalışma, dönüşen kamu hastanelerinin iç tedarik zinciri yönetimi performansının Pisagor Bulanık AHP (PBAHP) ve Pisagor Bulanık TOPSIS (PBTOPSIS) yöntemleriyle karşılaştırıldığı ilk çalışmadır. Çalışmada, uzman görüşlerinden yararlanılarak belirlenen HİTZY performans göstergeleri PBAHP yöntemiyle ağırlıklandırılmış, ardından PBTOPSIS yöntemiyle hastaneler performans açısından sıralanmıştır. Son olarak, gösterge ağırlıklarının ikili değişimleri dikkate alınarak duyarlılık analizi yapılmış ve sonuçlar değerlendirilmiştir. Bu sayede, kamu hastanelerinde kaynakların etkin ve verimli kullanımına katkı sağlayacak bir yapı ortaya konulmuştur.

Abstract

In order to provide patients with high-quality and efficient healthcare services, it is essential to establish an effective supply chain network in hospitals. In this context, internal hospital supply chain management (IHSCM) activities should be given strategic importance in order to increase service quality and reduce costs. In addition, IHSCM performance indicators need to be determined to evaluate the level of participation of stakeholders and healthcare providers in hospital processes. In this study, a multi-criteria decision-making model was developed to evaluate the IHSCM performance of hospitals. Within the scope of the research, four hospitals in Türkiye, which served as military hospitals until 2016 and currently have public hospital status, were examined. To the best of our knowledge in the literature, this is the first study to compare the internal supply chain management performance of transformed public hospitals with Pythagorean Fuzzy AHP (PFAHP) and Pythagorean Fuzzy TOPSIS (PFTOPSIS) methods. In the study, IHSCM performance indicators determined by using expert opinions were weighted with the PFAHP method, and then the hospitals were ranked in terms of performance with the PFTOPSIS method. Finally, sensitivity analysis was conducted by considering the binary changes of indicator weights and the results were evaluated. In this way, a structure that will contribute to the effective and efficient use of resources in public hospitals was established.

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Bu Makaleye Atıf Yap

Özyörük, B., Taşdemir, B. D., Kumcu, S., Karacaer, B. (2025). Hastane içi tedarik zinciri yönetimi performansının PBAHP ve PBTOPSIS yöntemleriyle karşılaştırmalı analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 40 Sayı: 4, 2839–2850. https://doi.org/10.17341/gazimmfd.1739731

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Kaynakça (42)

  1. Marques, L., Martins, M., Araújo, C., The healthcare supply network: current state of the literature and research opportunities, Production Planning & Control, 31 (7), 590–609, 2020.
  2. Zamiela, C., Hossain, N.U.I., Jaradat, R., Enablers of resilience in the healthcare supply chain: A case study of U.S healthcare industry during COVID-19 pandemic, Research in Transportation Economics, 93, 101174, 2022.
  3. Rakovska, M. A., Stratieva, S. V., A taxonomy of healthcare supply chain management practices, Supply Chain Forum: An International Journal, 19 (1), 4-24, 2018.
  4. Dixit, A., Routroy, S., Dubey, S. K., A systematic literature review of healthcare supply chain and implications of future research, International Journal of Pharmaceutical and Healthcare Marketing, 13 (4), 405–435, 2019.
  5. Mustaffa, N. H., Potter, A., Healthcare supply chain management in Malaysia: A case study, Supply Chain Management, 14 (3), 234–243, 2009.
  6. Fallahnezhad, M., Langarizadeh, M., Vahabzadeh, A., Key performance indicators of hospital supply chain: a systematic review, BMC Health Serv Res, 24, 1610, 2024.
  7. de Vries, J., Huijsman, R., Supply chain management in health services: An overview, Supply Chain Management: An International Journal, 16 (3), 159–165, 2011.
  8. Moons, K., Waeyenbergh, G., Pintelon, L., Measuring the logistics performance of internal hospital supply chains – A literature study, Omega, 82, 205–217, 2019.
  9. Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda, Int. J. Prod. Econ., 183, 299–315, 2017.
  10. Rossetti, M. D., Selandari, F. Multi-objective analysis of hospital delivery systems, Comput. Ind. Eng., 41 (3), 309–333, 2001.
  11. Hassan, T., Baboli, A., Guinet, A., Leboucher, G., Brandon, M.T., Re-Organizing the Pharmaceutical Supply Chain Downstream: Implementation A New Pharmacy. IFAC Proceedings, 39 (3), 727–732, 2006.
  12. Di Martinelly, C., Riane, F., Guinet, A., A porter-scor modelling approach for the hospital supply chain, International Journal of Logistics Systems and Management, 5 (3–4), 436–456, 2009.
  13. Hoeur, S., Kritchanchai, D., Key Performance Indicator Framework for Measuring Healthcare Logistics in ASEAN, Toward Sustainable Operations of Supply Chain and Logistics Systems, Editör: Kachitvichyanukul, V., Sethanan, K., Golinska- Dawson, P. (eds) Eco Production. Springer, Cham, 37-50, 2015.
  14. De Pourcq, K., Gemmel P., Trybou, J., Measuring process performance in hospitals, 22th International Conference of European Operations Management Association (EUROMA), Barcelona-Spain, 1-10, 28.06 – 01.07.2015.
  15. Feibert, D. C., Jacobsen, P., Measuring process performance within healthcare logistics-a decision tool for selecting track and trace technologies. Academy of Strategic Management Journal, 14, 33-57, 2015.
  16. Scholz, S., Ngoli, B., Flessa, S., Rapid assessment of infrastructure of primary health care facilities - A relevant instrument for health care systems management Organization, structure and delivery of healthcare. BMC Health Services Research, 15 (1), 1–10, 2015.
  17. Supeekit, T., Somboonwiwat, T., Kritchanchai, D., DEMATEL-modified ANP to evaluate internal hospital supply chain performance, Comput. Ind. Eng., 102, 318–330, 2016.
  18. Kritchanchai, D., Hoeur, S., Engelseth, P., Develop a strategy for improving healthcare logistics performance, Supply Chain Forum: An International Journal, 19 (1), 55-69, 2018.
  19. Mirghafoori, S. H., Sharifabadi, A. M., Takalo, S. K., Development of causal model of sustainable hospital supply chain management using the Intuitionistic Fuzzy Cognitive Map (IFCM) method, Journal of Industrial Engineering and Management, 11 (3), 588–605, 2018.
  20. Longaray, A., Ensslin, L., Ensslin, S., Alves, G., Dutra, A., Munhoz, P., Using MCDA to evaluate the performance of the logistics process in public hospitals: the case of a Brazilian teaching hospital, Int. Tran. Oper. Res., 25(1), 133-156, 2018.
  21. Moons, K., Waeyenbergh, G., Pintelon, L., Timmermans, P., De Ridder, D., Performance indicator selection for operating room supply chains: An application of ANP, Oper. Res. Health Care, 23, 100229, 2019.
  22. Gedam, V., Raut, R., Inamdar, Z., Narkhede, B., Dharaskar, S., Narvane, V., COVID‐19 critical success factors in Indian healthcare industry—A DEMATEL approach. Journal of Multi‐Criteria Decision Analysis, 29 (1-2), 135-149, 2022.
  23. Soto Lopez, D., Garshasbi, M., Kabir, G., Bari, A. M., Ali, S. M., Evaluating interaction between internal hospital supply chain performance indicators: a rough-DEMATEL-based approach, International Journal of Productivity and Performance Management, 71 (6), 2087-2113, 2022.
  24. Dolatabad, H.A., Mahdiraji, H.A., Babgohari, A.Z., Arturo Garza-Reyes, J., Ai, A. Analyzing the key performance indicators of circular supply chains by hybrid fuzzy cognitive mapping and Fuzzy DEMATEL: evidence from healthcare sector. Environ. Dev. Sustainability, 1–27, 2022.
  25. Senna, P., Reis, A., Marujo, L. G., Ferro de Guimarães, J. C., Severo, E. A., dos Santos, A. C. de S. G., The influence of supply chain risk management in healthcare supply chains performance. Production Planning & Control, 35 (12), 1368–1383, 2023.
  26. Karacaer, B., Özyörük, B., Hastane Tedarik Zinciri Yönetiminde Anahtar Performans Göstergesi Belirlenmesine Yönelik Bir Ölçek Geliştirilmesi, Savunma Bilimleri Dergisi, 20 (2), 237-252, 2024.
  27. Karaca M., Demirtaş Ö., Delice Y., A model proposal for the evaluation of hospital service quality based on fuzzy swara and fuzzy fucom methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 40(2), 1385-1400, 2025.
  28. Gul, M., Ak, M. F., A comparative outline for quantifying risk ratings in occupational health and safety risk assessment. J. Cleaner Prod., 196, 653–664, 2018.
  29. Ilbahar, E., Karaşan, A., Cebi, S., Kahraman, C., A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system, Saf. Sci., 103, 124–136, 2018.
  30. Karasan, A., Erdogan, M., Cinar, M., Healthcare service quality evaluation: An integrated decision-making methodology and a case study. Socio-Economic Planning Sciences, 82, 101234, 2022.
  31. Yucesan, M., Gul, M., Hospital service quality evaluation: an integrated model based on Pythagorean fuzzy AHP and fuzzy TOPSIS, Soft Comput., 24 (5), 3237–3255, 2020.
  32. Bulut, M., Özcan, E., Integration of Battery Energy Storage Systems into Natural Gas Combined Cycle Power Plants in Fuzzy Environment, J. Energy Storage, 36, 102376, 2021.
  33. Desticioglu Tasdemir, B., Kumcu, S., Ozyoruk, B., Comparison of e-commerce sites with pythagorean fuzzy AHP and TOPSIS methods. International Conference on Intelligent and Fuzzy Systems 2023, İstanbul-Türkiye, 327-335, Cham: Springer Nature Switzerland, 22-24 August, 2023.
  34. Yürek, Y. T., Özyörük, B., Özcan, E., Bulut, M., Socio-political evaluation of renewable energy resources under uncertain environment, Eng. Appl. Artif. Intell., 126, 106881, 2023.
  35. Desticioğlu Taşdemir, B., Locating Emergency Stations Using Multi-Criteria Decision-Making (MCDM) Methods: Application of Ankara Province, The Eurasia Proceedings of Science Technology Engineering and Mathematics, 28, 448-461, 2024.
  36. Erik A., Kuvvetli Y., Fuzzy multi-criteria decision-making framework for digital marketing integration evaluation of manufacturing facilities, Journal of the Faculty of Engineering and Architecture of Gazi University, 40 (3), 1689-1703, 2025.
  37. Adunlin, G., Phd, M. A., Diaby, V., Xiao, H., Candidate, P., Application of multicriteria decision analysis in health care: a systematic review and bibliometric analysis, Health Expectations, 18 (6), 1894–1905, 2015. 38. Atanassov, K.T., Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 (1), 87–96, 1986.
  38. Yager, R. R., Pythagorean membership grades in multicriteria decision making, IEEE Trans. Fuzzy Syst., 22 (4), 958–965, 2014.
  39. Zhang, X., Xu, Z., Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets, Int. J. Intell. Syst., 29 (12), 1061–1078, 2014.
  40. Desticioglu Tasdemir, B., & Asilogullari Ayan, M., Sustainable Supplier Selection in the Defense Industry with Multi-criteria Decision-Making Methods, 12th International Symposium on Intelligent Manufacturing and Service Systems, Sakarya-Ankara, 95-106, Singapore: Springer Nature Singapore, 26-28 May, 2023.
  41. Bulut, M., Ozcan, E., Ranking of advertising goals on social network sites by Pythagorean fuzzy hierarchical decision making: Facebook, Eng. Appl. Artif. Intell., 117, 105542, 2023.
  42. Yıldırım, M., Karakaya, Ö., Altan, İ. M., TOPSIS yönteminde maliyet ve karlılık oranlarının kullanılmasıyla finansal performansın ölçümü: Ana metal sanayi sektöründen bir şirket örneği, Gazi İktisat ve İşletme Dergisi, 5 (3), 170-181, 2019.