Third party logistics (3PL) selection for cold chain management: a fuzzy AHP and fuzzy TOPSIS approach | Annals of Operations Research Skip to main content

Advertisement

Springer Nature Link
Log in

Third party logistics (3PL) selection for cold chain management: a fuzzy AHP and fuzzy TOPSIS approach

  • Multiple Objective Optimization
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Managing value chain of perishable food items or pharmaceutical drugs is known as cold chain management. In India, approximately 30% fruits and vegetables get wasted due to lack of effective cold chain management. Logistic providers play a crucial role in making cold chains more effective. Based on literature review, ten criteria are selected for the third party logistics (3PL) selection process. Some of these criteria are transportation and warehousing cost, logistic infrastructure and warehousing facilities, customer service and reliability, network management, etc. This study illustrates a hybrid approach for selection of 3 PL for cold chain management under fuzzy environment. A hybrid model of Fuzzy AHP and Fuzzy TOPSIS is proposed in this paper for the selection of an appropriate 3PL in order to outsource logistics activities of perishable products. Fuzzy AHP is used to rank different criteria for 3PL selection, then Fuzzy TOPSIS is used to select the best 3 PL based on performance. The results imply that logistic providers should focus on practices such as automation of processes and innovation in cold chain processes to become more competitive.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Agrawal, S., Singh, R. K., & Murtaza, Q. (2016). Outsourcing decisions in reverse logistics: Sustainable balanced scorecard and graph theoretic approach. Resources, Conservation and Recycling, 108, 41–53.

    Article  Google Scholar 

  • Aguezzoul, A. (2008). A preliminary analysis on third-party logistics selection. In 7th International Meeting for Research in Logistics AVIGNON (pp. 24–26).

  • Akdemir, S. (2008). Designing of cold stores and choosing of cooling system elements. Journal of Applied Science, 8(5), 788–794.

    Article  Google Scholar 

  • Bhatnagar, R., Sohal, A. S., & Millen, R. (1999). Third party logistics services: A Singapore perspective. International Journal of Physical Distribution and Logistics Management, 29(9), 569–587.

    Article  Google Scholar 

  • Bogataj, M., Bogataj, L., & Vodopivec, R. (2005). Stability of perishable goods in cold logistics chains. International Journal of Production Economics, 93–94(8), 345–35.

    Article  Google Scholar 

  • Bolumole, Y. (2001). The supply chain role of third-party logistics providers. International Journal of Logistics Management, 12(2), 87–102.

    Article  Google Scholar 

  • Bowersox, D. J., & Closs, D. J. (1996). Logistical management: The integrated supply chain process. New York: McGraw-Hill Companies.

    Google Scholar 

  • Brandenburg, M., & Rebs, T. (2015). Sustainable supply chain management: A modeling perspective. Annals of Operations Research, 229(1), 213–252.

    Article  Google Scholar 

  • Büyüközkan, G., Kahraman, & Ruan, D. (2004). A fuzzy multi-criteria decision approach for software development strategy selection. International Journal of General Systems, 33(2–3), 259–280.

    Article  Google Scholar 

  • Buyukozkan, G., Ruan, D., & Feyzioglu, O. (2007). Evaluating e-Learning web site quality in a fuzzy environment. International Journal of Intelligent Systems, 22(5), 567–586.

    Article  Google Scholar 

  • Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35(3), 1367–1378.

    Article  Google Scholar 

  • Chan, F. T. S., & Chan, H. K. (2004). Development of the supplier selection model—a case study in the advanced technology industry. Journal of Engineering Manufacture, 218, 1807–1823.

    Article  Google Scholar 

  • Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets & Systems, 114(1), 1–9.

    Article  Google Scholar 

  • Chen, M.-F., Tzeng, G.-H., & Ding, C.-G. (2008). Combining fuzzy AHP with MDS in identifying the preference similarity of alternatives. Applied Soft Computing, 8(1), 110–117.

    Article  Google Scholar 

  • Cho, J. J.-K., Ozment, J., & Sink, H. (2008). Logistics capability, logistics outsourcing and firm performance in an E-commerce market. International Journal of Physical Distribution & Logistics Management, 38(5), 336–359.

    Article  Google Scholar 

  • Chow, H. K. H., Choy, K. L., Lee, W. B., & Chan, F. T. S. (2005). Design of a knowledge based logistics strategy system. Expert Systems with Applications, 29(2), 272–290.

    Article  Google Scholar 

  • Choy, K. L., Chow, H. K. H., Tan, K. H., Chan, C. K., Mok, E. C. M., & Wang, Q. (2008). Leveraging the supply chain flexibility of third party logistics-hybrid knowledge based system approach. Expert Systems with Applications, 35(4), 1998–2016.

    Article  Google Scholar 

  • Choy, K. L., Lee, C. L., So, S. C. K., Lau, H., Kwonk, S. K., & Leung, D. (2007). Managing uncertainty in logistics service supply chain. International Journal of Risk Assessment and Management, 7(1), 19–43.

    Article  Google Scholar 

  • Christopher, M. (2011). Logistic & supply chain management (4th ed.). Cambridge: Pearson Education Limited.

    Google Scholar 

  • Chu, T. C. (2002). Facility location selection using fuzzy TOPSIS under group decisions. International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 10(6), 687–701.

    Article  Google Scholar 

  • Coulomb, D. (2008). Refrigeration and cold chain serving the global food industry and creating a better future: Two key IIR challenges for improved health and environment. Trend in Food Science & Technology, 19(8), 413–417.

    Article  Google Scholar 

  • Dagdeviren, M., & Yuksel, I. (2008). Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management. Information Sciences, 178(6), 1717–1733.

    Article  Google Scholar 

  • Dapiran, P., Lieb, R., Millen, R., & Sohal, A. (1996). Third party logistics services usage by large Australian firms. International Journal of Physical Distribution and Logistics Management, 26(10), 36–45.

    Article  Google Scholar 

  • Delfmann, W., Albers, S., & Gehring, M. (2002). The impact of electronic commerce on logistics service providers. International Journal of Physical Distribution & Logistics Management, 32(3), 203–222.

    Article  Google Scholar 

  • Diabat, A., Khreishah, A., Kannan, A., Panikar, V., & Gunasekaran, A. (2013). Benchmarking the interactions among barriers in third-party logistics implementation. Benchmarking: An International Journal, 20(6), 805–824.

    Article  Google Scholar 

  • Dubois, D., & Prade, H. (1980). Fuzzy sets and systems: Theory and applications. New York: Academic Press.

    Google Scholar 

  • Efendigil, T., Önüt, S., & Kongar, E. (2008). A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Computers & Industrial Engineering, 54(2), 269–287.

    Article  Google Scholar 

  • Feng, C. M., & Wang, R. T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6, 133–142.

    Article  Google Scholar 

  • Flint, D. J., Larsson, E., Gammelgaard, B., & Mentzer, J. T. (2005). Logistics innovation: A customer value-oriented social process. Journal of Business Logistics, 26(1), 113–147.

    Article  Google Scholar 

  • Fu, P., & Yin, H. (2012). Logistics enterprise evaluation model based on fuzzy clustering analysis. Physics Procedia, 24(C), 1583–1587.

    Article  Google Scholar 

  • Göl, H., & Çatay, B. (2007). Third party logistics provider selection: Insights from Turkish automobile company. Supply Chain Management: An International Journal, 12(6), 379–384.

    Article  Google Scholar 

  • Govindan, K., & Murugesan, P. (2011). Selection of third-party reverse logistics provider using fuzzy extent analysis. Benchmarking: An International Journal, 18(1), 149–167.

    Article  Google Scholar 

  • Gumus, A.-T. (2009). Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert Systems with Applications, 36(2), 4067–4074.

    Article  Google Scholar 

  • Gunasekaran, A., & Ngai, E. W. T. (2003). The successful management of a small logistics company. International Journal of Physical Distribution & Logistics Management, 33(9), 825–842.

    Article  Google Scholar 

  • Gunasekaran, A., & Ngai, E. W. T. (2004). Information systems in supply chain integration and management. European Journal of Operational Research, 159(2), 269–295.

    Article  Google Scholar 

  • Guo, L., Ma, Y., Sun, D., & Wang, P. (2007). Effects of controlled freezing-point storage at \(0^\circ \)C on quality of green bean as compared with cold and room-temperature storages. Journal of Food Engineering, 86(1), 25–29.

    Article  Google Scholar 

  • Gupta, R., Sachdeva, A., & Bhardwaj, A., (2010). Selection of 3PL service provider using integrated fuzzy delphi and fuzzy TOPSIS. In Proceedings World Congress on Engineering and Computer Science, 20–22 October, San Francisco.

  • Hamprecht, F., Corsten, D., Noll, M., & Meier, E. (2005). Controlling the sustainability of food supply chains. Supply Chain Management: An International Journal, 10(1), 7–10.

    Article  Google Scholar 

  • Ho, W., He, T., Lee, C. K. M., & Emrouznejad, A. (2012). Strategic logistic outsourcing: An integrated QFD and fuzzy AHP approach. Expert Systems with Applications, 39(12), 10841–10850.

    Article  Google Scholar 

  • Hsieh, T.-Y., Lu, S.-T., & Tzeng, G.-H. (2004). Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International Journal of Project Management, 22(7), 573–584.

    Article  Google Scholar 

  • Işiklar, G., Alptekin, E., & Büyüközkan, G. (2007). Application of a hybrid intelligent decision support model in logistic outsourcing. Computer and Operations Research, 34(12), 3701–3714.

    Article  Google Scholar 

  • James, S. J., & James, C. (2010). The food cold-chain and climate change. Food Research International, 43(7), 1944–1956.

    Article  Google Scholar 

  • Jharkharia, S., & Shankar, R. (2007). Selection of logistics service provider: An analytic network process (ANP) approach. Omega, 35(3), 2744–2789.

    Article  Google Scholar 

  • Jol, S., Kassianenko, A., Wszol, K., & Oggel, J. (2007). The cold chain, one link in Canada’s food safety initiatives. Food Control, 18(6), 713–715.

    Article  Google Scholar 

  • Kaufmann, A., & Gupta, M. M. (1991). Introduction to fuzzy arithmetic: Theory and applications. Van Nostrand Reinhold Co.

  • Kim, G., Park, C. S., & Yoon, K. P. (1997). Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement. International Journal of Production Economics, 50(1), 23–33.

    Article  Google Scholar 

  • Kumar, M., Vrat, P., & Shankar, R. (2006). A multi objective 3PL allocation problem for fish distribution. International Journal of Physical Distribution and Logistics Management, 36(9), 702–715.

    Article  Google Scholar 

  • Kumar, P., & Singh, R. K. (2012). A fuzzy AHP and TOPSIS methodology to evaluate global 3PL. Journal of Modelling in Management, 7(3), 287–303.

    Article  Google Scholar 

  • Kumar, R., Reintz, H. W., Simunovic, J., Sandeep, K. P., & Franzon, P. D. (2009). Overview of RFID technology and its applications in the food industry. Journal of Food Science, 74(8), 101–106.

    Article  Google Scholar 

  • Kuo, J. C., & Chen, M. C. (2010). Developing an advanced multi-temperature joint distribution system for the food cold chain. Food Control, 21(4), 559–566.

    Article  Google Scholar 

  • Large, R. O., Kramer, N., & Hartmann, R. K. (2011). Customer-specific adaptation by providers and their perception of 3PL-relationship success. International Journal of Physical Distribution & Logistics Management, 41(9), 822–838.

    Article  Google Scholar 

  • Leahy, S. E., Murphy, P. R., & Poist, R. F. (1995). Determinants of successful logistical relationships: A third party provider perspective. Transportation Journal, 35(2), 5–13.

    Google Scholar 

  • Li, X., & Chandra, C. (2007). Efficient knowledge integration to support a complex supply network management. International Journal of Manufacture Technology Management, 10(1), 1–18.

    Article  Google Scholar 

  • Manoj, U. V., Gupta, J. N. D., Gupta, S. K., & Sriskandarajah, C. (2008). Supply chain scheduling: Just-in-time environment. Annals of Operations Research, 161(1), 53–86.

    Article  Google Scholar 

  • Manzini, R., & Accorsi, R. (2012). The new conceptual framework for food supply chain assessment. Journal of Food Engineering, 115, 251–263.

    Article  Google Scholar 

  • McGinnis, M., Kochunny, C., & Ackerman, K. (1995). Third party logistics choice. International Journal of Logistics Management, 6(2), 93–102.

    Article  Google Scholar 

  • Melnyk, A. S., Davis, E. W., Spekman, R. E., & Sandor, J. (2010). Outcome-driven supply chains. MIT Sloan Management Review, 51(2), 33–38.

    Google Scholar 

  • Menon, M., McGinnis, M., & Ackerman, K. (1998). Selection criteria for providers of third-party logistics services: An exploratory study. Journal of Business Logistics, 19(1), 121–137.

    Google Scholar 

  • Miller, T., Peters, E., Gupta, V., & Bode, O. (2013). A logistics deployment decision support system at Pfizer. Annals of Operations Research, 203(1), 81–99.

    Article  Google Scholar 

  • Min, S., Roath, A. S., Daugherty, P. J., Genchev, S. E., Haozhe, C., Arndt, A. D., et al. (2005). Supply chain collaboration: What’s happening? The International Journal of Logistics Management, 16(2), 237–256.

    Article  Google Scholar 

  • Moberg, C. R., & Speh, T. W. (2004). Third-party warehousing selection: A comparison of national and regional firms. American Journal of Business, 19(2), 71–76.

    Article  Google Scholar 

  • Montanari, R. (2008). Cold chain tracking: A managerial perspective. Trends in Food Science & Technology, 19, 425–431.

    Article  Google Scholar 

  • Ovca, A., & Jevšnik, M. (2008). Maintaining a cold chain from purchase to the home and at home: Consumer opinions. Food Control, 20(2), 167–172.

    Article  Google Scholar 

  • Pan, N.-F. (2008). Fuzzy AHP approach for selecting the suitable bridge construction method. Automation in Construction, 17(8), 958–965.

    Article  Google Scholar 

  • Perçin, S. (2009). Evaluation of third-party logistics (3PL) providers by using a two-phase AHP and TOPSIS methodology. Benchmarking: An International Journal, 16(5), 588–604.

    Article  Google Scholar 

  • Perego, A., Perotti, S., & Mangiaracina, R. (2011). ICT for logistics and freight transportation: A literature review and research agenda. International Journal of Physical Distribution & Logistics Management, 41(5), 457–483.

    Article  Google Scholar 

  • Porter, E. M., & Kramer, R. M. (2006). Strategy and society: The link between competitive advantage and corporate social responsibility. Harvard Business Review, 84(12), 78–92.

    Google Scholar 

  • PWC. (2007). Farm to retail—overview of India’s retail sector. In Indo-US Economic Summit (pp. 12–26). www.pwc.com/extweb/pwcpublications.nsf/docid/50e7c984c1feebe5ca25739f0023b7e7/$file/buidingstongpartnership.pdf.

  • Qureshi, M. N., Kumar, D., & Kumar, P. (2008). An integrated model to identify and classify the key criteria and their role in the assessment of 3PL service providers. Asia Pacific Journal of Marketing and Logistics, 20(2), 227–249.

    Article  Google Scholar 

  • Rijswijk, W. V., & Frewer, L. J. (2008). Consumer perceptions of food quality and safety and their relation to traceability. British Food Journal, 110(10), 1034–46.

    Article  Google Scholar 

  • Sangam, V. K. (2004). Global logistics outsourcing trends: Challenges in managing 3PL relationship. Palmerston: Massey University.

    Google Scholar 

  • Singh, A. K., Subramanian, N., Pawar, K. S., & Bai, R. (2016). Cold chain configuration design: Location-allocation decision-making using coordination, value deterioration, and big data approximation. Annals of Operations Research. doi:10.1007/s10479-016-2332-z.

  • Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26.

    Article  Google Scholar 

  • Shabani, A., Saen, R. F., & Torabipour, S. M. R. (2012). A new benchmarking approach in cold chain. Applied Mathematical Modelling, 36(1), 212–224.

    Article  Google Scholar 

  • Singh, R. K., & Sharma, M. K. (2015). Selecting competitive supply chain using fuzzy AHP and extent analysis. Journal of Industrial and Production Engineering. doi:10.1080/21681015.2014.999723 (In press).

  • Singh, R. K., & Sharma, M. K. (2014). Prioritizing the alternatives for flexibility in supply chains. Production Planning and Control, 25(2), 176–192.

    Article  Google Scholar 

  • Singh, R. K. (2011). Analyzing the interaction of factors for success of total quality management in SMEs. Asian Journal on Quality, 12(1), 6–19.

    Article  Google Scholar 

  • Singh, R. K., Garg, S. K., & Deshmukh, S. G. (2008). Implementation of information technology: Evidences from Indian SMEs. International Journal of Enterprise Network Management, 2(3), 248–267.

    Article  Google Scholar 

  • Singh, R. K., Kumar, R., & Shankar, R. (2012). Supply chain management in SMEs: A case study. International Journal of Manufacturing Research, 7(2), 165–180.

    Article  Google Scholar 

  • Soh, H. (2010). A decision model for evaluating third party logistics provider using fuzzy analytic hierarchy process. African Journal of Business Management, 4(3), 339–349.

    Google Scholar 

  • Sohail, M. S., & Al-Abdali, O. S. (2005). The usage of third party logistics in Saudi Arabia: Current position and future prospects. International Journal of Physical Distribution & Logistics Management, 35(9), 637–653.

    Article  Google Scholar 

  • Spencer, M. S., Rogers, D. S., & Daugherty, P. J. (1994). JIT systems and external logistics suppliers. International Journal of Operations and Production Management, 14(6), 60–74.

    Article  Google Scholar 

  • Stam, A., & Duarte, A. P. S. (2003). On multiplicative priority ratings method for the AHP. European Journal of Operational Research, 145, 92–108.

    Article  Google Scholar 

  • Sun, C. C. (2010). Performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37, 7745–7754.

    Article  Google Scholar 

  • Thakkar, J., Deshmukh, S. G., Gupta, A. D., & 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, 6(1), 32–46.

    Article  Google Scholar 

  • Tirado, M. C., Clarke, R., Jaykus, L. A., McQuatters-Gollop, A., & Frank, J. M. (2010). Climate change and food safety: A review. Food Research International, 43(7), 1745–1765.

    Article  Google Scholar 

  • Tolga, E., Demircan, M. L., & Kahraman, C. (2005). Operating system selection using fuzzy replacement analysis and analytic hierarchy process. International Journal of Production Economics, 97(1), 89–117.

    Article  Google Scholar 

  • Torfi, F. A., Farahani, R. Z., & Rezapour, S. (2009). Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives. Applied Soft Computing, 10(2), 520–528.

    Article  Google Scholar 

  • Torfi, F., Farahani, R. Z., & Rezapour, S. (2010). Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives. Applied Soft Computing, 10(2), 520–528.

  • Troyer, C., & Cooper, R. (1995). Smart moves in supply chain integration. Transportation and Distribution, 36(9), 55–62.

    Google Scholar 

  • UNCTAD. (2006). Report of the Expert Meeting on ICT Solutions to Facilitate Trade at Border Crossings and Ports, United Nations Conference on Trade and Development, Geneva.

  • Vaidyanathan, G. (2005). A framework for evaluating third party logistics. Communications of ACM, 48(1), 89–94.

    Article  Google Scholar 

  • Vorst, J. G., & Van der, A. G. (2000). Effective food supply chains generating, modeling and evaluating supply chain scenarios. Dutch: Wageningen University.

    Google Scholar 

  • Wolf, C., & Seuring, S. (2010). Environmental impacts as buying criteria for third party logistical services. International Journal of Physical Distribution & Logistics Management, 40(1/2), 84–102.

    Article  Google Scholar 

  • Yan, G. E. (2009). Evaluation on competitiveness of TPL enterprises based on AHP and genetic algorithm. In Proceedings of the 2nd IEEE International Conference on Computer Science and Information Technology.

  • Ying, W., & Dayong, S. (2005). Multi-agent framework for third party logistics in E-commerce. Expert Systems with Applications, 29, 431–436.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–356.

    Article  Google Scholar 

  • Zang, J. (2009). The research of 3PLs provider selection based on rough set and PSO. In Proceeding of the IITA International Conference on Services Science, Management and Engineering.

  • Zhou, G., Min, H., Xu, C., & Cao, Z. (2008). Evaluating the comparative efficiency of Chinese third party logistics provider using data envelopment analysis. International Journal of Physical Distribution and Logistics Management, 34(4), 262–279.

    Article  Google Scholar 

  • Zokaee, S., Jabbarzadeh, A, Fahimnia, B, Sadjadi, S. J. (2014). Robust supply chain network design: an optimization model with real world application. In Annals of Operations Research, November (pp. 1–30).

Download references

Acknowledgements

Authors would like to express their sincere thanks to the Chief Editor of the journal and unanimous reviewers for valuable comments and suggestions to enhance quality and content of the paper. Authors also acknowledge the support of Mr. Ankit Bansal and Mr. Sidhant Issar, UG students in doing this research work.

Author information

Authors and Affiliations

  1. Management Development Institute, Gurgaon, India

    Rajesh Kr. Singh

  2. School of Business and Public Administration, California State University, Bakersfield, USA

    Angappa Gunasekaran

  3. Delhi Technological University, Delhi, India

    Pravin Kumar

Authors
  1. Rajesh Kr. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Angappa Gunasekaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pravin Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajesh Kr. Singh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, R.K., Gunasekaran, A. & Kumar, P. Third party logistics (3PL) selection for cold chain management: a fuzzy AHP and fuzzy TOPSIS approach. Ann Oper Res 267, 531–553 (2018). https://doi.org/10.1007/s10479-017-2591-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-017-2591-3

Keywords

Search

Navigation