Evaluating the Barriers to Logistics Outsourcing through a Fuzzy Multi-Criteria Decision-Making Model

Authors

DOI:

https://doi.org/10.31181/smeor31202653

Keywords:

Logistics outsourcing, CIMAS, Barriers, MCDM, Fuzzy sets

Abstract

Logistics outsourcing has become a common strategic practice for enterprises seeking cost efficiency and greater operational flexibility. Achieving these benefits, however, depends on the effective management of outsourcing relationships through suitable governance mechanisms. In practice, several barriers limit the realization of these advantages. With a specific focus on Africa, this study applies a fuzzy criteria importance assessment (F-CIMAS) method to systematically assess the barriers to logistics outsourcing. Data was collected from four domain experts who evaluated twenty-two identified barriers, and the proposed method was then applied to determine the relative importance of each criterion. The results reveal that congested roads and weak infrastructure, poor governance, corruption and unethical conduct, inadequate regulations, and excessive cost of business operations constitute the five most critical impediments to logistics outsourcing. The study makes a meaningful contribution to the decision sciences and management literature by offering practical insights for logistics outsourcing practitioners, and it concludes by outlining clear avenues for future research. 

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References

McCarthy, I., & Anagnostou, A. (2004). The impact of outsourcing on the transaction costs and boundaries of manufacturing. International Journal of Production Economics, 88(1), 61-71. https://doi.org/10.1016/S0925-5273(03)00183-X

Aguezzoul, A. (2014). Third-party logistics selection problem: A literature review on criteria and methods. Omega, 49, 69-78. https://doi.org/10.1016/j.omega.2014.05.009

Thakkar, J., Deshmukh, S., Gupta, A., & Shankar, R. (2005). Selection of third-party logistics (3PL): a hybrid approach using interpretive structural modeling (ISM) and analytic network process (ANP). Supply Chain Forum: An International Journal. https://doi.org/10.1080/16258312.2005.11517137

Jovčić, S., & Průša, P. (2021). A hybrid MCDM approach in third-party logistics (3PL) provider selection. Mathematics, 9(21), 2729. https://doi.org/10.3390/math9212729

Marchet, G., Melacini, M., Sassi, C., & Tappia, E. (2017). Assessing efficiency and innovation in the 3PL industry: an empirical analysis. International Journal of Logistics Research and Applications, 20(1), 53-72. https://doi.org/10.1080/13675567.2016.1226789

Vivaldini, M., Pires, S., & De Souza, F. B. (2008). Collaboration between 4PL and 3PL: a study within the fast food industry. 7th International meeting for research in logistics.

Alnahhal, M., Tabash, M. I., & Ahrens, D. (2021). Optimal selection of third-party logistics providers using integer programming: A case study of a furniture company storage and distribution. Annals of Operations Research, 302(1), 1-22. https://doi.org/10.1007/s10479-021-04034-y

Gardas, B. B., D. Raut, R., & Narkhede, B. E. (2019). Analysing the 3PL service provider's evaluation criteria through a sustainable approach. International Journal of Productivity and Performance Management, 68(5), 958-980. https://doi.org/10.1108/IJPPM-04-2018-0154

Vivaldini, M. (2023). The effect of logistical immediacy on logistics service providers'(LSPs') business. Benchmarking: An International Journal, 30(3), 899-923. https://doi.org/10.1108/BIJ-09-2021-0562

Alioni, C., Park, B. I., & Min, H. (2024). Examining success factors for logistics outsourcing in Sub‐Saharan Africa. Transportation Journal, 63(2), 98-110. https://doi.org/10.1002/tjo3.12004

Olubiyo, O. C. (2022). Investigating the impact of third-party logistics outsourcing on the performance of clothing manufacturing SMES in Nigeria [University of Johannesburg].

Etokudoh, E. P., Boolaky, M., & Gungaphul, M. (2017). Third party logistics outsourcing: An exploratory study of the oil and gas industry in Nigeria. Sage open, 7(4), 2158244017735566. https://doi.org/10.1177/2158244017735566

Esima, O., & Wordu, S. (2019). Overcoming the challenges of logistics outsourcing in selected oil and gas companies in Rivers state. International Journal of Scientific Research and Management, 7(3). https://doi.org/10.18535/ijsrm/v7i3.el06

Okeke, O. (2024). Third-party logistics in Nigeria: the development of a Nigerian third-party logistics decision support framework [Buckinghamshire New University (Awarded by Staffordshire University)].

Mitrović, D., Demir, G., Badi, I., & Bouraima, M. B. (2025). Balancing Efficiency and Risk in Public Sector Artificial Intelligence with Data Envelopment Analysis and Portfolio Approaches. Applied Decision Analytics, 1(1), 15-35. http://ada-journal.org/index.php/ada/article/view/4

Sahoo, S. K., Choudhury, B. B., & Dhal, P. R. (2024). A bibliometric analysis of material selection using MCDM methods: trends and insights. Spectrum of Mechanical Engineering and Operational Research, 1(1), 189-205. https://doi.org/10.31181/smeor11202417

Bouraima, M. B., & Więckowski, J. (2026). A robust decision support system for assessing green hydrogen market development in Africa. International Journal of Hydrogen Energy, 225, 154513. https://doi.org/10.1016/j.ijhydene.2026.154513

Bouraima, M. B., Więckowski, J., & Qian, S. (2025). A decision support system for prioritizing electric vehicles transition policies. Transportation research part D: transport and environment, 146, 104880. https://doi.org/10.1016/j.trd.2025.104880

Bouraima, M. B., Ayyıldız, E., Erdogan, M., & Pamucar, D. (2026). An Interval-valued Intuitionistic Fuzzy Group Decision Model for Evaluation of Cross-border Railway Development. Cognitive Computation, 18(1), 22. https://doi.org/10.1007/s12559-026-10551-4

Pani, A., Mishra, S., & Sahu, P. (2022). Developing multi-vehicle freight trip generation models quantifying the relationship between logistics outsourcing and insourcing decisions. Transportation Research Part E: Logistics and Transportation Review, 159, 102632. https://doi.org/10.1016/j.tre.2022.102632

Xu, X., He, Y., Liu, M., Qi, P., & Yu, L. (2025). Multi-stage resource leveling problem with self-operation and outsourcing cooperation in sharing logistics. Omega, 131, 103221. https://doi.org/10.1016/j.omega.2024.103221

Görçün, Ö. F., Chatterjee, P., Stević, Ž., & Küçükönder, H. (2024). An integrated model for road freight transport firm selection in third-party logistics using T-spherical Fuzzy sets. Transportation Research Part E: Logistics and Transportation Review, 186, 103542. https://doi.org/10.1016/j.tre.2024.103542

Zhu, W., Ng, S. C., Wang, Z., & Zhao, X. (2017). The role of outsourcing management process in improving the effectiveness of logistics outsourcing. International Journal of Production Economics, 188, 29-40. https://doi.org/10.1016/j.ijpe.2017.03.004

Ali, A., Cao, M., Allen, J., Liu, Q., Ling, Y., & Cheng, L. (2023). Investigation of the drivers of logistics outsourcing in the United Kingdom's pharmaceutical manufacturing industry. Multimodal Transportation, 2(1), 100064. https://doi.org/10.1016/j.multra.2022.100064

Dong, C., Huang, Q., Pan, Y., Ng, C. T., & Liu, R. (2023). Logistics outsourcing: Effects of greenwashing and blockchain technology. Transportation Research Part E: Logistics and Transportation Review, 170, 103015. https://doi.org/10.1016/j.tre.2023.103015

Giri, B., & Sarker, B. R. (2017). Improving performance by coordinating a supply chain with third party logistics outsourcing under production disruption. Computers & Industrial Engineering, 103, 168-177. https://doi.org/10.1016/j.cie.2016.11.022

Huang, M., Tu, J., Chao, X., & Jin, D. (2019). Quality risk in logistics outsourcing: A fourth party logistics perspective. European journal of operational research, 276(3), 855-879. https://doi.org/10.1016/j.ejor.2019.01.049

Dang, V. L., Wan, S., & Guo, J. (2025). Third-Party Logistics Outsourcing: A Review of Two Decades of Advancing Decision-Making Approaches with an Up-to-Date Three-Layer Criteria Framework Integrating Environmental, Social, and Governance Metrics. International Journal of Production Economics, 109615. https://doi.org/10.1016/j.ijpe.2025.109615

Sarkar, A., Görçün, Ö. F., Ecer, F., Senapati, T., & Küçükönder, H. (2025). Evaluating the financial credibility of third-party logistic providers through a novel frank operators-driven group decision-making model with dual hesitant linguistic q-rung orthopair fuzzy information. Engineering Applications of Artificial Intelligence, 139, 109483. https://doi.org/10.1016/j.engappai.2024.109483

Sarwar, M., Akram, M., Gulzar, W., & Deveci, M. (2024). Group decision making method for third-party logistics management: An interval rough cloud optimization model. Journal of Industrial Information Integration, 41, 100658. https://doi.org/10.1016/j.jii.2024.100658

Nila, B., & Roy, J. (2023). A new hybrid MCDM framework for third-party logistics provider selection under sustainability perspectives. Expert systems with applications, 234, 121009. https://doi.org/10.1016/j.eswa.2023.121009

Wang, C.-N., & Dang, T.-T. (2024). Third-party logistics provider selection in the Industry 4.0 era by using a fuzzy AHP and fuzzy MARCOS methodology. IEEE Access, 12, 67291-67313. https://doi.org/10.1109/ACCESS.2024.3392892

Yang, C., Wang, Q., Pan, M., Hu, J., Peng, W., Zhang, J., & Zhang, L. (2022). A linguistic Pythagorean hesitant fuzzy MULTIMOORA method for third-party reverse logistics provider selection of electric vehicle power battery recycling. Expert systems with applications, 198, 116808. https://doi.org/10.1016/j.eswa.2022.116808

Ulutaş, A., Topal, A., Görçün, Ö. F., & Ecer, F. (2024). Evaluation of third-party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI-MACONT model. Expert systems with applications, 241, 122680. https://doi.org/10.1016/j.eswa.2023.122680

Liu, A., Ji, X., Lu, H., & Liu, H. (2019). The selection of 3PRLs on self-service mobile recycling machine: Interval-valued pythagorean hesitant fuzzy best-worst multi-criteria group decision-making. Journal of Cleaner Production, 230, 734-750. https://doi.org/10.1016/j.jclepro.2019.04.257

Pamucar, D., Chatterjee, K., & Zavadskas, E. K. (2019). Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers. Computers & Industrial Engineering, 127, 383-407. https://doi.org/10.1016/j.cie.2018.10.023

Bošković, S., Jovčić, S., Simic, V., Švadlenka, L., Dobrodolac, M., & Bacanin, N. (2023). A New Criteria Importance Assessment (CIMAS) Method in Multi-Criteria Group Decision-Making: Criteria Evaluation for Supplier Selection. Facta Universitatis, Series: Mechanical Engineering. https://doi.org/10.22190/FUME230730050B

Biswas, S., Bhattacharjee, S., Biswas, B., Mitra, K., & Khawas, N. (2027). An expert opinion-based soft computing framework for comparing nanotechnologies used in agriculture. Spectrum of Operational Research, 1-39. https://doi.org/10.31181/sor4156

Xv, J. (2025). A Fuzzy Decision Support System for the Effect Evaluation of GAI Application in HRM. Journal of Intelligent Decision Making and Granular Computing, 1(1), 266-285. https://doi.org/10.31181/jidmgc11202524

Aytekin, A., & Korucuk, S. (2024). Assessing the innovation capacity of manufacturing firms in Ordu Province: A multi-criteria evaluation using CIMAS. J. Intell Manag. Decis, 3(4), 224-230. https://doi.org/10.56578/jimd030403

Bouraima, M. B., Jovčić, S., Švadlenka, L., Simic, V., Badi, I., & Maraka, N. D. (2024). An integrated multi-criteria approach to formulate and assess healthcare referral system strategies in developing countries. Healthcare Analytics, 5, 100315. https://doi.org/10.1016/j.health.2024.100315

Kara, K., Yalçın, G. C., Çetinkaya, A., Simic, V., & Pamucar, D. (2024). A single-valued neutrosophic CIMAS-CRITIC-RBNAR decision support model for the financial performance analysis: A study of technology companies. Socio-Economic Planning Sciences, 92, 101851. https://doi.org/10.1016/j.seps.2024.101851

Švadlenka, L., Bajec, P., Pivtorak, H., Bošković, S., Jovčić, S., & Dobrodolac, M. (2024). Risk Prioritization from the Crowd-Shipping Provider's Perspective using the CIMAS Method. Spectrum of Engineering and Management Sciences, 2(1), 234-246. https://doi.org/10.31181/sems21202430s

Kuteyi, D., & Winkler, H. (2022). Logistics challenges in sub-Saharan Africa and opportunities for digitalization. Sustainability, 14(4), 2399. https://doi.org/10.3390/su14042399

Achola, V. O. (2024). Logistics Outsourcing Practices in Africa: A Systematic Literature Review. Logistics Research, 17(1). https://doi.org/10.23773/2024_6

Mageto, J., Prinsloo, G., & Luke, R. (2018). Logistics outsourcing and performance of manufacturing small and medium-sized enterprises in Nairobi. The Southern African Journal of Entrepreneurship and Small Business Management, 10(1), 1-11. https://doi.org/10.4102/sajesbm.v10i1.162

Sarkar, A., & Goswami, S. S. (2026). A Review of the Application of MCDM Methods in Business Analytics. Applied Decision Analytics, 2(1), 150-180. https://ada-journal.org/index.php/ada/article/view/14

Jin, J., Yang, X., Du, H., & Pamucar, D. (2026). A Novel Interval-Valued Intuitionistic Fuzzy Distance Measure Incorporating Min/Max Interaction Terms. Journal of Contemporary Decision Science, 2(1), 84-96. https://www.cds-journal.org/index.php/cds/article/view/10

Published

2026-04-21

How to Cite

Badi, I., Bouraima, M. B., & Kiprotich Kiptum, C. (2026). Evaluating the Barriers to Logistics Outsourcing through a Fuzzy Multi-Criteria Decision-Making Model. Spectrum of Mechanical Engineering and Operational Research, 3(1), 65-78. https://doi.org/10.31181/smeor31202653