Problems Encountered in Humanitarian Aid Logistics and Solution Suggestions by Integrated QFD Method: The Case of Hatay

Year 2024, Volume: 17 Issue: 3, 366 - 387

Gülşah Ayvazoğlu , İskender Peker

https://doi.org/10.17218/hititsbd.1453537

Abstract

In recent years, extraordinary situations and humanitarian crises have increased the number of studies in the field of humanitarian aid logistics. Although various solutions have been tried to be produced for the problems experienced in humanitarian aid logistics in disasters occurring worldwide and, in our country, these solutions have generally not been suitable for regional needs. The aim of this study is to identify the problems encountered in humanitarian aid activities carried out in Hatay province and to develop solutions to these problems. Humanitarian logistics is a complex process involving many actors. This complexity requires a combination of both requirements and decision criteria to be evaluated. In this study, a model that can provide flexible yet effective solutions to the problems encountered in the humanitarian logistics planning process is proposed. This model involves combining qualitative and quantitative data using the Quality Function Deployment (QFD) method. In this study, a model that integrates Delphi and Analytical Network Process (ANP) methods with Quality Function Deployment (QFD) technique has been developed to identify the problems encountered in humanitarian aid logistics in Hatay province and to develop solutions to these problems. In line with the developed model, the study generally consisted of three stages. In the first stage, the Delphi method was used to identify the problems in humanitarian aid logistics by taking into account the relevant literature and the opinions of the representatives of the institutions/organizations providing humanitarian aid services in Hatay. Then, the priority values (weights) of these problems were determined by Analytic Network Process (ANP) method. In the last stage, solution proposals were developed through the Quality Function Deployment (QFD) relationship matrices. In humanitarian aid logistics, 14 problems were identified by Delphi technique. With the prioritisation of the results obtained with ANP, ‘Problems Arising from Lack of Continuity-Sustainability’ emerged as the most important problem. Then, among the 13 solution suggestions determined by expert opinions, ‘Establishing a system to monitor humanitarian aid’ was determined as the most effective solution suggestion in eliminating the problems in humanitarian aid logistics. In the future, it would be useful to conduct similar research with the participation of more experts in different regions or countries. In addition, the findings of the study can be compared with different multi-criteria decision-making methods to provide a broader perspective. In this context, the use of integrated methodologies in the field of humanitarian aid logistics will allow for a more comprehensive approach to the problems.

Keywords

ANP, Delphi, Humanitarian aid, Humanitarian aid logistics, QFD

Entegre KFG Yöntemi ile İnsani Yardım Lojistiğinde Karşılaşılan Sorunlar ve Çözüm Önerileri: Hatay Örneği

Abstract

Son yıllarda yaşanan olağanüstü durumlar ve insani krizler, insani yardım lojistiği alanında yapılan çalışmaların sayısını artırmıştır. Dünya genelinde ve ülkemizde meydana gelen afetlerde, insani yardım lojistiğinde yaşanılan sorunlar için çeşitli çözümler üretilmeye çalışılmışsa da bu çözümler genellikle bölgesel ihtiyaçlara uygun olmamıştır. Bu çalışmanın amacı, Hatay ilinde gerçekleştirilen insani yardım faaliyetlerinde karşılaşılan sorunları belirlemek ve bu sorunlara çözümler geliştirmektir. İnsani yardım lojistiği birçok aktörün yer aldığı karmaşık bir süreçtir. Bu karmaşıklık, hem gereksinimlerin hem de karar kriterlerinin bir arada değerlendirilmesini gerektirmektedir. Bu çalışmada, insani yardım lojistiği planlama sürecinde karşılaşılan sorunlara esnek ancak etkili çözümler sunabilecek bir model önerilmektedir. Bu model, Kalite Fonksiyon Yayılımı (QFD) yöntemi kullanılarak nitel ve nicel verilerin birleştirilmesini içermektedir. Bu çalışmada, Hatay ili özelinde insani yardım lojistiğinde karşılaşılan sorunların neler olduğunu belirlemek ve bu sorunlara çözüm önerileri geliştirmek üzere Delphi ve Analitik Ağ Süreci (AAS) yöntemlerinin Kalite Fonksiyon Göçerimi (KFG) tekniğine entegre edildiği bir model geliştirilmiştir. Geliştirilen model doğrultusunda çalışma genel itibariyle üç aşamadan oluşmuştur. Birinci aşamada Delphi yöntemi kullanılarak ilgili literatür ve Hatay’da insani yardım hizmeti sunan kurum/kuruluş temsilcilerinin görüşleri dikkate alınarak insani yardım lojistiğindeki sorunlar tespit edilmiştir. Ardından bu sorunların öncelik değerleri (ağırlıkları) Analitik Ağ Süreci (AAS) yöntemi ile belirlenmiştir. Son aşamada ise Kalite Fonksiyon Göçerimi (KFG) ilişki matrisleri sayesinde çözüm önerileri geliştirilmiştir. İnsani yardım lojistiği konusunda 14 sorun Delphi tekniği ile belirlenmiştir. Elde edilen sonuçların AAS ile önceliklendirilmesi ile “Süreklilik-Sürdürebilirlik Eksikliğinden Kaynaklanan Problemler” en önemli sorun olarak karşımıza çıkmıştır. Ardından uzaman görüşleriyle belirlenen 13 çözüm önerisi arasından “İnsani yardımların takip edileceği sistem oluşturulması” ise insani yardım lojistiğindeki sorunların bertaraf edilmesindeki en etkili çözüm önerisi olarak belirlenmiştir. Gelecekte benzer araştırmaların farklı bölgelerde veya ülkelerde daha fazla uzmanın katılımı ile gerçekleştirilmesi faydalı olacaktır. Ayrıca, çalışmanın bulguları, farklı çok kriterli karar verme yöntemleri ile karşılaştırılarak daha geniş bir perspektif sağlanabilir. Bu bağlamda, insani yardım lojistiği alanında entegre metodolojilerin kullanılması, sorunların daha kapsamlı bir şekilde ele alınmasına olanak tanıyacaktır.

Keywords

AAS, Delphi, İnsani yardım, İnsani yardım lojistiği, KFG

References

• Abdel-Basset, M., Manogaran, G., Mohamed, M., & Chilamkurti, N. (2018). Three-way decisions based on neutrosophic sets and AHP-QFD framework for supplier selection problem. Future Generation Computer Systems, 89(6), 19-30. https://doi.org/10.1016/j.future.2018.06.024
• Adivar, B., Atan, T., Sevil Oflaç, B., & Örten, T. (2010). Improving social welfare chain using optimal planning model, Supply Chain Management, 15(4), 290-305. https://doi.org/10.1108/13598541011054661
• AFAD (Disaster and Emergency Management Presidency), (2022). Türkiye disaster response plan (TAMP), Retrieved from: https://www.afad.gov.tr/kurumlar/afad.gov.tr/e_Kutuphane/Planlar/TAMP.pdf
• Bacın, M. (2018). Sustainable supplier selection problem with integrated QFD–ANP approach in Turkish textile and clothing industry. Graduate School of Science znd Engineering, Galatasaray University, Istanbul
• Besiou, M., Pedraza-Martinez, A. J., & Van Wassenhove, L. N. (2018). OR applied to humanitarian operations. European Journal of Operational Research, 269(2), 397–405. https://doi.org/10.1016/j.ejor.2018.02.046
• Blecken, A. (2010). Supply chain process modelling for humanitarian organizations, International Journal of Physical Distribution & Logistics Management, 40(8/9), 675-692. https://doi.org/10.1108/09600031011079328
• Bottani, E., Centobelli, P., Murino, T., & Shekarian, E. (2018). A QFD-ANP method for supplier selection with benefits, opportunities, costs and risks considerations. International Journal of İnformation Technology & Decision Making, 17(03), 911-939. https://doi.org/10.1142/S021962201850013X
• Bottani, E., & Rizzi, A. (2006). Strategic management of logistics service: A fuzzy QFD approach. International Journal of Production Economics, 103(2), 585–599. https://doi.org/10.1016/j.ijpe.2005.11.006
• Broomfield, D., & Humphris, G. M. (2001). Using the Delphi technique to identify the cancer education requirements of general practitioners. Medical education, 35(10), 928-937. https://doi.org/10.1111/j.1365-2923.2001.01022.x
• Cengiz Toklu, M. (2023). A fuzzy multi-criteria approach based on Clarke and Wright savings algorithm for vehicle routing problem in humanitarian aid distribution. Journal of Intelligent Manufacturing, 34(5), 2241-2261. https://doi.org/10.1007/s10845-022-01917-0
• Chang, A. Y., & Cho, C. (2019). A Mixed QFD–ANP Approach to Mitigating Bullwhip Effect by Deploying Agility in the Supply Chain System. In Proceedings of the World Congress on Engineering (ss. 272-277). Retrieved from: https://www.iaeng.org/publication/WCE2019/WCE2019_pp272-277.pdf
• Cheng, E. W., & Li, H. (2004). Contractor selection using the analytic network process. Construction management and Economics, 22(10), 1021-1032. https://doi.org/10.1080/0144619042000202852
• Dursun, M., & Karsak, E. E. (2013). A QFD-based fuzzy MCDM approach for supplier selection. Applied Mathematical Modelling, 37(8), 5864-5875. https://doi.org/10.1016/j.apm.2012.11.014
• Eligüzel, İ. M., Özceylan, E., & Weber, G. W. (2022). Location-allocation analysis of humanitarian distribution plans: a case of United Nations Humanitarian Response Depots. Annals of Operations Research, 1-30. https://doi.org/10.1007/s10479-022-04886-y
• Eren, T., & Özbek, A. (2013). Selecting the Third Party Logistic(3PL) Firm through the Analytic Network Process (ANP). Atatürk University Journal of Economics and Administrative Sciences, 27(1), 95-113. Retrieved from: https://dergipark.org.tr/en/download/article-file/353740
• Fallahi, A., Pourghazi, A., & Mokhtari, H. (2024). A Multi-product Humanitarian Supply Chain Network Design Problem: A Fuzzy Multi-objective and Robust Optimization Approach. International Journal of Engineering, 37(5), 941-958. https://doi.org/10.5829/ije.2024.37.05b.12
• Gavidia, J.V. (2017). A model for enterprise resource planning in emergency humanitarian logistics. Journal of Humanitarian Logistics and Supply Chain Management, 7(3), 246-265. https://doi.org/10.1108/JHLSCM-02-2017-0004 Ghannadpour, S. F., Hoseini, A. R., Bagherpour, M., & Ahmadi, E. (2021). Appraising the triple bottom line utility of sustainable project portfolio selection using a novel multicriteria house of portfolio. Environment, Development and Sustainability, 23(3), 3396- 3437. https://doi.org/10.1007/s10668-020-00724-y
• Grass, E., Ortmann, J., Balcik, B., & Rei, W. (2023). A machine learning approach to deal with ambiguity in the humanitarian decision‐making. Production and Operations Management, 32(9), 2956-2974. https://doi.org/10.1111/poms.14018
• Haiyun, C., Zhixiong, H., Yüksel, S., & Dinçer, H. (2021). Analysis of the innovation strategies for green supply chain management in the energy industry using the QFDbased hybrid interval valued intuitionistic fuzzy decision approach. Renewable and Sustainable Energy Reviews, 143, 110844. https://doi.org/10.1016/j.rser.2021.110844
• Hauser, J. R., & Clausing, D. (1988). The house of quality. Harvard Business Review. Retrieved from: https://blogs.ubc.ca/nvdteamb/files/2013/10/7-The-House-of-Quality.pdf
• Heyse, L., Morales, F. N., & Wittek, R. (2021). Evaluator perceptions of NGO performance in disasters: meeting multiple institutional demands in humanitarian aid projects. Disasters, 45(2), 324-354. https://doi.org/10.1111/disa.12419
• Humphrey-Murto, S., Varpio, L., Wood, T. J., Gonsalves, C., Ufholz, L.-A., Mascioli, K., … & Foth, T. (2017). The Use of the Delphi and Other Consensus Group Methods in Medical Education Research. Academic Medicine, 92(10), 1491–1498. https://doi.org/10.1097/acm.0000000000001812
• Hung, H. L., Altschuld, J. W., & Lee, Y. F. (2008). Methodological and conceptual issues confronting a cross-country Delphi study of educational program evaluation. Evaluation and program planning, 31(2), 191-198. https://doi.org/10.1016/j.evalprogplan.2008.02.005
• Kaptan, K., & Liebiediev, D. (2017). Logistics. Khorram-Manesh, A., Goniewicz, K., Hertelendy, A., & Dulebenets, M. (Eds.), Handbook of disaster and Emergency Management First Edition (pp.82-85). Kompendiet
• Kirac, E., & Milburn, A. B. (2018). A general framework for assessing the value of social data for disaster response logistics planning. European Journal of Operational Research, 269(2), 486-500. https://doi.org/10.1016/j.ejor.2018.02.011
• Lin, M. C., Tsai, C. Y., Cheng, C. C., & Chang, C. A. (2004). Using fuzzy QFD for design of low-end digital camera. International journal of applied science and engineering, 2(3), 222-233. Retrieved from https://gigvvy.com/journals/ijase/articles/ijase-200412-2-3-222.pdf
• Liu, J., Kamarudin, K. M., Liu, Y., & Zou, J. (2021). Developing Pandemic Prevention and Control by ANP-QFD Approach: A Case Study on Urban Furniture Design in China Communities. International Journal of Environmental Research and Public Health, 18(5), 2653. https://doi.org/10.3390/ijerph18052653
• MacCarthy, B.L., & Atthirawong, W. (2003). Factors affecting location decisions in international operations – a Delphi study. International Journal of Operations & Production Management, 23(7), 794-818. https://doi.org/10.1108/01443570310481568
• McGeary, J. (2009). A critique of using the Delphi technique for assessing evaluation capability-building needs. Evaluation Journal of Australasia, 9(1), 31-39. https://doi.org/10.1177/1035719X0900900105
• Ocampo, L. A., Labrador, J. J. T., Jumao-as, A. M. B., & Rama, A.M.O. (2020). Integrated multiphase sustainable product design with a hybrid quality function deployment–multiattribute decision-making (QFD-MADM) framework. Sustainable Production and Consumption, 24, 62-78. https://doi.org/10.1016/j.spc.2020.06.013
• Özdemir, M.S. (2002). Bir işletmede analitik hiyerarşi süreci kullanılarak performans değerleme sistemi tasarımı. Endüstri Mühendisliği Dergisi, 13(2), 2–11. Retrieved from: https://www.mmo.org.tr/sites/default/files/e0928de075538c5_ek.pdf
• Palter, V. N., Macrae, H. M., & Grantcharov, T. P. (2011). Development of an objective evaluation tool to assess technical skill in laparoscopic colorectal surgery: A delphi methodology. The American Journal of Surgery, 201, 251–259. https://doi.org/10.1016/j.amjsurg.2010.01.031
• Patro, C. S., & Prasad, M. V. (2013). A Study on Implementation of Quality Function Deployment Technique in Product Design Stage. International Journal of Management Research and Reviews, 3(6), 2966-2974. Retrieved from:https://ijmrr.com/archieve.php
• Prinsen, C. A., Vohra, S., Rose, M. R., King-Jones, S., Ishaque, S., Bhaloo, Z., ... & Terwee, C. B. (2014). Core Outcome Measures in Effectiveness Trials (COMET) initiative: protocol for an international Delphi study to achieve consensus on how to select outcome measurement instruments for outcomes included in a ‘core outcome set’. Trials, 15(1), 1-7. https://doi.org/10.1186/1745-6215-15-247
• Profillidis, V. A., & Botzoris, G. N. (2019). Executive Judgment, Delphi, Scenario Writing, and Survey Methods. Modeling of Transport Demand, 125–161. https://doi.org/10.1016/B978-0-12-811513-8.00004-2
• Pusparani, N. A., Hidayanto, A. N., Purwandari, B., Budi, N. F. A., Setiawan, S., & Kosandi, M. (2020). Development of hybrid quality function deployment-analytical network process framework for e-services strategy formulation. Electronic Government, an International Journal, 16(4), 355-378. https://doi.org/10.1504/EG.2020.110608
• Rezvanian, T. (2019). Integrating Data-Driven Forecasting and Large-Scale Optimization to Improve Humanitarian Response Planning and Preparedness. Doctoral Thesis, Northeastern University. Retrieved from: https://www.proquest.com/docview/2354843281?pq-origsite=gscholar&fromopenview=true&sourcetype=Dissertations%20&%20Theses
• Rossman, M. H., & D.M. Carey (1995). Adult education and the Delphi technique: an explanation and application. (S. Ç. Peker, interpreter). Marmara University Atatürk Education Faculty Journal of Educational Sciences, 7, 233-237 (Publication date of the original work 1973.) Retrieved from: https://dergipark.org.tr/tr/download/article-file/1718
• Rowe, G., & Wright, G. (2001). Expert opinions in forecasting: the role of the Delphi technique. In Principles of forecasting (ss.125-144). Springer, Boston, MA. Retrieved from: https://link.springer.com/chapter/10.1007/978-0-306-47630-3_7
• Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical Modelling, 9(3-5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8
• Saaty, T. L. (2001). Decision making with the analytic network process (ANP) and its super decisions software: The national missile defense (NMD) example. ISAHP 2001 proceedings, 2-4. Retrieved from: https://www.isahp.org/uploads/106-p.pdf
• Saaty, T. L. (2004). Decision making—the analytic hierarchy and network processes (AHP/ANP). Journal of systems science and systems engineering, 13(1), 1-35. https://doi.org/10.1007/s11518-006-0151-5
• Saaty, T. L. (2006). Rank from comparisons and from ratings in the analytic hierarchy/network processes. European Journal of Operational Research, 168(2), 557- 570. https://doi.org/10.1016/j.ejor.2004.04.032
• Saaty, T. L., & Vargas, L. G. (2006). Decision making with the analytic network process. US: Springer Science+ Business Media, LLC. https://doi.org/10.1007/978-1-4614-7279-7
• Smarandache, F., Ricardo, J. E., Caballero, E. G., Vázquez, M. Y. L., & Hernández, N. B. (2020). Delphi method for evaluating scientific research proposals in a neutrosophic environment. Infinite Study. https://digitalrepository.unm.edu/nss_journal/vol34/iss1/26/
• Şahin, A. E. (2001). Delphi technique and its uses in educational research. Hacettepe University- Journal of Education, 20, 215–220. Retrieved from:https://dergipark.org.tr/en/download/article-file/87971
• Tanti, L., Efendi, S., Lydia, M. S., & Mawengkang, H. (2023). A Decision-Making Model to Determine Dynamic Facility Locations for a Disaster Logistic Planning Problem Using Deep Learning. Algorithms, 16(10), 468. https://doi.org/10.3390/a16100468
• Tavana, M., Yazdani, M., & Di Caprio, D. (2017). An application of an integrated ANP– QFD framework for sustainable supplier selection. International Journal of Logistics Research and Applications, 20(3), 254-275. https://doi.org/10.1080/13675567.2016.1219702
• Timperio, G., Kundu, T., Klumpp, M., de Souza, R., Loh, X. H., & Goh, K. (2022). Beneficiary-centric decision support framework for enhanced resource coordination in humanitarian logistics: A case study from ASEAN. Transportation Research Part E: Logistics and Transportation Review, 167, 102909. https://doi.org/10.1016/j.tre.2022.102909
• Trapp, M. M. (2016). You-will-kill-me-beans: Taste and the politics of necessity in humanitarian aid. Cultural Anthropology, 31(3), 412-437. https://doi.org/10.14506/ca31.3.08
• UNOCHA, (2021). Global humanitarian overview 2021. Retrieved from: https://www.unocha.org/sites/unocha/files/GHO_Monthly_Update_31MAY2021.pdf
• URL-1. Retrieved from: https://doc.emdat.be/docs/data-structure-and-content/general-definitions-and-concepts/
• Ülkü, M. A., Bookbinder, J. H., & Yun, N. Y. (2024). Leveraging Industry 4.0 Technologies for Sustainable Humanitarian Supply Chains: Evidence from the Extant Literature. Sustainability, 16(3), 1321. https://doi.org/10.3390/su16031321
• Van Notten, P. W., Rotmans, J., Van Asselt, M. B., & Rothman, D. S. (2003). An updated scenario typology. Futures, 35(5), 423-443. https://doi.org/10.1016/S0016-3287(02)00090-3
• Venkadesh, P., Divya, S. V., Marymariyal, P., & Keerthana, S. (2024). Predicting Natural Disasters with AI and Machine Learning. In Utilizing AI and Machine Learning for Natural Disaster Management (pp. 39-64). IGI Global. Retrieved from:https://www.igi-global.com/viewtitlesample.aspx?id=345853&ptid=335482&t=Predicting%20Natural%20Disasters%20With%20AI%20and%20Machine%20Learning&isxn=9798369333624
• Vosooghi, Z., Mirzapour Al-e-hashem, S. M. J., & Lahijanian, B. (2022). Scenario-based redesigning of a relief supply-chain network by considering humanitarian constraints, triage, and volunteers’ help. Socio-Economic Planning Sciences, 84, 101399. https://doi.org/10.1016/j.seps.2022.101399
• Yang, C. L., Huang, R. H., & Ke, W. C. (2012). Applying QFD to build green manufacturing system. Production Planning & Control, 23(2-3), 145-159. https://doi.org/10.1080/09537287.2011.591632
• Yurt, S., & Kadıoğlu, H. (2019). The usage of Delphi consensus technique in nursing. Journal of Education and Research in Nursing, 16(1), 48-53. Retrieved from: https://jag.journalagent.com/jern/pdfs/JERN_16_1_48_53.pdf