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Assessment of Third-Party Reverse Logistics Provider Through the SWARA-WASPAS Integrated Framework

Yıl 2024, Cilt: 21 Sayı: 1, 204 - 217, 30.04.2024

Hasan Emin Gürler

https://doi.org/10.33437/ksusbd.1422598

Öz

The selection of the most suitable third-party provider for reverse logistics (RL) activities is a key factor in initiating effective RL processes. However, the process of identifying the optimal third-party provider involves navigating through various conflicting objectives and criteria. This task is intricate and time-consuming, necessitating the application of multi-criteria decision-making (MCDM) approaches. This study addresses the evaluation and selection of the best 3PRLP by introducing a comprehensive MCDM framework. Therefore, this study aims to assist a manufacturer specializing in milk and dairy products who has opted to transfer its reverse logistics operations to a logistics service provider. The objective is to aid them in selecting the most suitable 3PRLP. The proposed framework incorporates the SWARA (Step-wise Weight Assessment Ratio Analysis) technique for determining weights and the WASPAS (Weighted Aggregated Sum Product Assessment) method for thoroughly evaluating alternatives. In this study, a panel of four experts made a joint decision after identifying six criteria and five alternatives. The research concluded that the reverse logistics cost (C1) emerges as the most pivotal factor, with the service quality of reverse logistics (C3) being recognized as the least significant criterion. As a result, A1 stands out as the top choice among the 3PRLP.

Anahtar Kelimeler

Reverse logistics Third-party reverse logistics providers MCDM SWARA WASPAS

Kaynakça

  • Agarwal, S., Kant, R., & Shankar, R. (2020). Evaluating solutions to overcome humanitarian supply chain management barriers: A hybrid fuzzy SWARA – Fuzzy WASPAS approach. International Journal of Disaster Risk Reduction, 51.
  • Ahmadi, S., Shokouhyar, S., Amerioun, M., & Salehi Tabrizi, N. (2024). A social media analytics-based approach to customer-centric reverse logistics management of electronic devices: A case study on notebooks. Journal of Retailing and Consumer Services, 76.
  • Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000–3011.
  • Cheng, Y. H., & Lee, F. (2010). Outsourcing reverse logistics of high-tech manufacturing firms by using a systematic decision-making approach: TFT-LCD sector in Taiwan. Industrial Marketing Management, 39(7), 1111–1119.
  • de Almeida, A. T. (2007). Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method. Computers and Operations Research, 34(12), 3569–3574.
  • Efendigil, T., Önüt, S., & Kongar, E. (2008). A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Computers and Industrial Engineering, 54(2), 269–287.
  • Eydi, A., & Rastgar, S. (2022). A DEA model with dual-role factors and fuzzy data for selecting third-party reverse logistics provider, case study: Hospital waste collection. Ain Shams Engineering Journal, 13(2).
  • Goodarzi, F., Abdollahzadeh, V., & Zeinalnezhad, M. (2022). An integrated multi-criteria decision-making and multi-objective optimization framework for green supplier evaluation and optimal order allocation under uncertainty. Decision Analytics Journal, 4, 100087.
  • Govindan, K., Kadziński, M., Ehling, R., & Miebs, G. (2019). Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA. Omega (United Kingdom), 85, 1–15.
  • Guarnieri, P., Sobreiro, V. A., Nagano, M. S., & Marques Serrano, A. L. (2015). The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: A Brazilian case. Journal of Cleaner Production, 96, 209–219.
  • Guggeri, E. M., Ham, C., Silveyra, P., Rossit, D. A., & Piñeyro, P. (2023). Goal programming and multi-criteria methods in remanufacturing and reverse logistics: Systematic literature review and survey. Computers and Industrial Engineering, 185.
  • Haq, M., Moazzam, M., Khan, A. S., & Ahmed, W. (2023). The impact of reverse logistics process coordination on third party relationship quality: A moderated mediation model for multichannel retailers in the fashion industry. Journal of Retailing and Consumer Services, 73.
  • Haseli, G., Torkayesh, A. E., Hajiaghaei-Keshteli, M., & Venghaus, S. (2023). Sustainable resilient recycling partner selection for urban waste management: Consolidating perspectives of decision-makers and experts. Applied Soft Computing, 137.
  • Jauhar, S. K., Amin, S. H., & Zolfagharinia, H. (2021). A proposed method for third-party reverse logistics partner selection and order allocation in the cellphone industry. Computers and Industrial Engineering, 162.
  • Jayant, A., Gupta, P., Garg, S. K., & Khan, M. (2014). TOPSIS-AHP based approach for selection of reverse logistics service provider: A case study of mobile phone industry. Procedia Engineering, 97, 2147–2156.
  • Jharkharia, S., & Shankar, R. (2007). Selection of logistics service provider: An analytic network process (ANP) approach. Omega, 35(3), 274–289.
  • Kafa, N., Hani, Y., & El Mhamedi, A. (2015). An integrated sustainable partner selection approach with closed-loop supply chain network configuration. IFAC-PapersOnLine, 28(3), 1840–1845.
  • Kannan, G., Pokharel, S., & Kumar, P. S. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling, 54(1), 28–36.
  • Keršulienė, V., Kazimieras Zavadskas, E., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258.
  • Krumwiede, D. W., & Sheu, C. (2002). A model for reverse logistics entry by third-party providers. Omega, 30.
  • Li, Y. L., Ying, C. S., Chin, K. S., Yang, H. T., & Xu, J. (2018). Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. Journal of Cleaner Production, 195, 573–584.
  • Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017a). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686–1698.
  • Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017b). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686–1698.
  • Min, H., & Ko, H. J. (2008). The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. International Journal of Production Economics, 113(1), 176–192.
  • Mishra, A. R., & Rani, P. (2021). Assessment of sustainable third party reverse logistic provider using the single-valued neutrosophic Combined Compromise Solution framework. Cleaner and Responsible Consumption, 2.
  • Mohammadkhani, A., & Mousavi, S. M. (2022). Assessment of third-party logistics providers by introducing a new stochastic two-phase compromise solution model with last aggregation. Computers and Industrial Engineering, 170.
  • Mohammadkhani, A., & Mousavi, S. M. (2023). A new last aggregation fuzzy compromise solution approach for evaluating sustainable third-party reverse logistics providers with an application to food industry. Expert Systems with Applications, 216.
  • Palmgren, R., Pohjalainen, E., Wahlström, M., Hinkka, V., & Ruohomäki, I. (2023). Identifying reverse logistics and related regulations in the circular supply chain of sustainable floor coverings. Transportation Research Procedia, 72, 2370–2376.
  • Prakash, C., & Barua, M. K. (2016a). A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry. Sustainable Production and Consumption, 7, 66–78.
  • Prakash, C., & Barua, M. K. (2016b). An analysis of integrated robust hybrid model for third-party reverse logistics partner selection under fuzzy environment. Resources, Conservation and Recycling, 108, 63–81.
  • Ravi, V., Shankar, R., & Tiwari, M. K. (2005). Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach. Computers and Industrial Engineering, 48(2), 327–356.
  • Rostamzadeh, R., Esmaeili, A., Sivilevičius, H., & Nobard, H. B. K. (2020). A fuzzy decision-making approach for evaluation and selection of third party reverse logistics provider using fuzzy aras. Transport, 35(6), 635–657.
  • Sremac, S., Stević, Ž., Pamučar, D., Arsić, M., & Matić, B. (2018). Evaluation of a third-party logistics (3PL) provider using a rough SWARA-WASPAS model based on a new rough dombi aggregator. Symmetry, 10(8).
  • Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers and Industrial Engineering, 140.
  • Stock, J. (1992). Reverse logistics. Council of Logistics Management.
  • Tasoglu, G., & Ilgin, M. A. (2024). A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing. Computers and Industrial Engineering, 187.
  • Thibbotuwawa, A., Nanayakkara, P. R., Fernando, W. M., Jayalath, M. M., Perera, H. N., & Nielsen, P. (2023). A Reverse Logistics Network Model for Handling E-commerce Returns. IFAC-PapersOnLine, 56(2), 138–143.
  • Yang, Q., Yan, W. M., Liu, M., Deveci, M., Garg, H., & Chen, Z. S. (2024). A hybrid generalized TODIM approach for sustainable 3PRLP selection in electronic manufacturing industry. Advanced Engineering Informatics, 59.
  • Yang, Y., Fu, Y., Lin, J., & Huang, G. Q. (2022). An attitudinal consensus method under uncertainty in 3PRLP selection. Computers and Industrial Engineering, 172.
  • Yücenur, G. N., & Ipekçi, A. (2021). SWARA/WASPAS methods for a marine current energy plant location selection problem. Renewable Energy, 163, 1287–1298.
  • Zarbakhshnia, N., Wu, Y., Govindan, K., & Soleimani, H. (2020). A novel hybrid multiple attribute decision-making approach for outsourcing sustainable reverse logistics. Journal of Cleaner Production, 242.
  • Zavadskas, E. K., Turskis, Z., Antuchevičienė, J., & Zakarevičius, A. (2012). Optimization of weighted aggregated sum product assessment. Electronics & Electrical Engineering, 6(12), 3–6

Entegre SWARA-WASPAS yaklaşımı aracılığıyla üçüncü taraf tersine lojistik sağlayıcısının değerlendirilmesi

Yıl 2024, Cilt: 21 Sayı: 1, 204 - 217, 30.04.2024

Hasan Emin Gürler

https://doi.org/10.33437/ksusbd.1422598

Öz

Tersine lojistik faaliyetleri için en uygun üçüncü taraf sağlayıcının seçimi, etkili RL süreçlerinin başlatılmasında önemli bir faktördür. Bununla birlikte, en uygun üçüncü taraf sağlayıcıyı belirleme süreci birbiriyle çelişen çeşitli hedefler ve kriterler arasında karar vermeyi içerir. Bu görev karmaşık ve zaman alıcıdır ve çok kriterli karar verme yaklaşımlarının uygulanmasını gerektirir. Bu çalışma, kapsamlı bir ÇKKV çerçevesi sunarak en iyi 3PRLP'nin değerlendirilmesini ve seçimini ele almaktadır. Bu nedenle bu çalışma, tersine lojistik operasyonlarını bir lojistik hizmet sağlayıcısına devretmeyi seçen süt ve süt ürünleri konusunda uzmanlaşmış bir üreticiye yardımcı olmayı amaçlamaktadır. Amaç, firmaya en uygun 3PRLP'yi seçmede yardımcı olmaktır. Önerilen çerçeve, ağırlıkların belirlenmesi için SWARA (Adımsal Ağırlık Değerlendirme Oranı Analizi) tekniğini ve alternatiflerin kapsamlı bir şekilde değerlendirilmesi için WASPAS (Ağırlıklı Toplu Toplam Ürün Değerlendirmesi) yöntemini içermektedir. Bu çalışmada dört uzmandan oluşan bir ekip, altı kriter ve beş alternatif belirledikten sonra ortak karar almıştır. Araştırma, tersine lojistik maliyetinin (C1) en önemli faktör olarak ortaya çıktığı, tersine lojistiğin hizmet kalitesinin (C3) ise en az önemli kriter olarak kabul edildiği sonucuna varmıştır. Sonuç olarak A1, 3PRLP arasında en üst sırada yer alırken, A4 en düşük sırada yer almaktadır.

Anahtar Kelimeler

Reverse logistics Third-party reverse logistics providers MCDM SWARA WASPAS

Kaynakça

  • Agarwal, S., Kant, R., & Shankar, R. (2020). Evaluating solutions to overcome humanitarian supply chain management barriers: A hybrid fuzzy SWARA – Fuzzy WASPAS approach. International Journal of Disaster Risk Reduction, 51.
  • Ahmadi, S., Shokouhyar, S., Amerioun, M., & Salehi Tabrizi, N. (2024). A social media analytics-based approach to customer-centric reverse logistics management of electronic devices: A case study on notebooks. Journal of Retailing and Consumer Services, 76.
  • Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000–3011.
  • Cheng, Y. H., & Lee, F. (2010). Outsourcing reverse logistics of high-tech manufacturing firms by using a systematic decision-making approach: TFT-LCD sector in Taiwan. Industrial Marketing Management, 39(7), 1111–1119.
  • de Almeida, A. T. (2007). Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method. Computers and Operations Research, 34(12), 3569–3574.
  • Efendigil, T., Önüt, S., & Kongar, E. (2008). A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Computers and Industrial Engineering, 54(2), 269–287.
  • Eydi, A., & Rastgar, S. (2022). A DEA model with dual-role factors and fuzzy data for selecting third-party reverse logistics provider, case study: Hospital waste collection. Ain Shams Engineering Journal, 13(2).
  • Goodarzi, F., Abdollahzadeh, V., & Zeinalnezhad, M. (2022). An integrated multi-criteria decision-making and multi-objective optimization framework for green supplier evaluation and optimal order allocation under uncertainty. Decision Analytics Journal, 4, 100087.
  • Govindan, K., Kadziński, M., Ehling, R., & Miebs, G. (2019). Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA. Omega (United Kingdom), 85, 1–15.
  • Guarnieri, P., Sobreiro, V. A., Nagano, M. S., & Marques Serrano, A. L. (2015). The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: A Brazilian case. Journal of Cleaner Production, 96, 209–219.
  • Guggeri, E. M., Ham, C., Silveyra, P., Rossit, D. A., & Piñeyro, P. (2023). Goal programming and multi-criteria methods in remanufacturing and reverse logistics: Systematic literature review and survey. Computers and Industrial Engineering, 185.
  • Haq, M., Moazzam, M., Khan, A. S., & Ahmed, W. (2023). The impact of reverse logistics process coordination on third party relationship quality: A moderated mediation model for multichannel retailers in the fashion industry. Journal of Retailing and Consumer Services, 73.
  • Haseli, G., Torkayesh, A. E., Hajiaghaei-Keshteli, M., & Venghaus, S. (2023). Sustainable resilient recycling partner selection for urban waste management: Consolidating perspectives of decision-makers and experts. Applied Soft Computing, 137.
  • Jauhar, S. K., Amin, S. H., & Zolfagharinia, H. (2021). A proposed method for third-party reverse logistics partner selection and order allocation in the cellphone industry. Computers and Industrial Engineering, 162.
  • Jayant, A., Gupta, P., Garg, S. K., & Khan, M. (2014). TOPSIS-AHP based approach for selection of reverse logistics service provider: A case study of mobile phone industry. Procedia Engineering, 97, 2147–2156.
  • Jharkharia, S., & Shankar, R. (2007). Selection of logistics service provider: An analytic network process (ANP) approach. Omega, 35(3), 274–289.
  • Kafa, N., Hani, Y., & El Mhamedi, A. (2015). An integrated sustainable partner selection approach with closed-loop supply chain network configuration. IFAC-PapersOnLine, 28(3), 1840–1845.
  • Kannan, G., Pokharel, S., & Kumar, P. S. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling, 54(1), 28–36.
  • Keršulienė, V., Kazimieras Zavadskas, E., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258.
  • Krumwiede, D. W., & Sheu, C. (2002). A model for reverse logistics entry by third-party providers. Omega, 30.
  • Li, Y. L., Ying, C. S., Chin, K. S., Yang, H. T., & Xu, J. (2018). Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. Journal of Cleaner Production, 195, 573–584.
  • Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017a). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686–1698.
  • Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017b). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686–1698.
  • Min, H., & Ko, H. J. (2008). The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. International Journal of Production Economics, 113(1), 176–192.
  • Mishra, A. R., & Rani, P. (2021). Assessment of sustainable third party reverse logistic provider using the single-valued neutrosophic Combined Compromise Solution framework. Cleaner and Responsible Consumption, 2.
  • Mohammadkhani, A., & Mousavi, S. M. (2022). Assessment of third-party logistics providers by introducing a new stochastic two-phase compromise solution model with last aggregation. Computers and Industrial Engineering, 170.
  • Mohammadkhani, A., & Mousavi, S. M. (2023). A new last aggregation fuzzy compromise solution approach for evaluating sustainable third-party reverse logistics providers with an application to food industry. Expert Systems with Applications, 216.
  • Palmgren, R., Pohjalainen, E., Wahlström, M., Hinkka, V., & Ruohomäki, I. (2023). Identifying reverse logistics and related regulations in the circular supply chain of sustainable floor coverings. Transportation Research Procedia, 72, 2370–2376.
  • Prakash, C., & Barua, M. K. (2016a). A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry. Sustainable Production and Consumption, 7, 66–78.
  • Prakash, C., & Barua, M. K. (2016b). An analysis of integrated robust hybrid model for third-party reverse logistics partner selection under fuzzy environment. Resources, Conservation and Recycling, 108, 63–81.
  • Ravi, V., Shankar, R., & Tiwari, M. K. (2005). Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach. Computers and Industrial Engineering, 48(2), 327–356.
  • Rostamzadeh, R., Esmaeili, A., Sivilevičius, H., & Nobard, H. B. K. (2020). A fuzzy decision-making approach for evaluation and selection of third party reverse logistics provider using fuzzy aras. Transport, 35(6), 635–657.
  • Sremac, S., Stević, Ž., Pamučar, D., Arsić, M., & Matić, B. (2018). Evaluation of a third-party logistics (3PL) provider using a rough SWARA-WASPAS model based on a new rough dombi aggregator. Symmetry, 10(8).
  • Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers and Industrial Engineering, 140.
  • Stock, J. (1992). Reverse logistics. Council of Logistics Management.
  • Tasoglu, G., & Ilgin, M. A. (2024). A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing. Computers and Industrial Engineering, 187.
  • Thibbotuwawa, A., Nanayakkara, P. R., Fernando, W. M., Jayalath, M. M., Perera, H. N., & Nielsen, P. (2023). A Reverse Logistics Network Model for Handling E-commerce Returns. IFAC-PapersOnLine, 56(2), 138–143.
  • Yang, Q., Yan, W. M., Liu, M., Deveci, M., Garg, H., & Chen, Z. S. (2024). A hybrid generalized TODIM approach for sustainable 3PRLP selection in electronic manufacturing industry. Advanced Engineering Informatics, 59.
  • Yang, Y., Fu, Y., Lin, J., & Huang, G. Q. (2022). An attitudinal consensus method under uncertainty in 3PRLP selection. Computers and Industrial Engineering, 172.
  • Yücenur, G. N., & Ipekçi, A. (2021). SWARA/WASPAS methods for a marine current energy plant location selection problem. Renewable Energy, 163, 1287–1298.
  • Zarbakhshnia, N., Wu, Y., Govindan, K., & Soleimani, H. (2020). A novel hybrid multiple attribute decision-making approach for outsourcing sustainable reverse logistics. Journal of Cleaner Production, 242.
  • Zavadskas, E. K., Turskis, Z., Antuchevičienė, J., & Zakarevičius, A. (2012). Optimization of weighted aggregated sum product assessment. Electronics & Electrical Engineering, 6(12), 3–6
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Lojistik, Tedarik Zinciri Yönetimi
Bölüm Makaleler
Yazarlar

Hasan Emin Gürler 0000-0002-5813-1631

Yayımlanma Tarihi 30 Nisan 2024
Gönderilme Tarihi 19 Ocak 2024
Kabul Tarihi 5 Nisan 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 21 Sayı: 1

Kaynak Göster

APA Gürler, H. E. (2024). Assessment of Third-Party Reverse Logistics Provider Through the SWARA-WASPAS Integrated Framework. Kahramanmaraş Sütçü İmam Üniversitesi Sosyal Bilimler Dergisi, 21(1), 204-217. https://doi.org/10.33437/ksusbd.1422598
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