Decision-making model: Determination of logistics service providers selection criteria

Aleksandrs Kotlars, Valerijs Skribans

Article ID: 4345
Vol 8, Issue 6, 2024

VIEWS - 315 (Abstract) 189 (PDF)

Abstract


Outsourcing logistics operations is a common trend as businesses prioritize core activities. Establishing a sustainable partnership between businesses and logistics service providers requires a systematic approach. This study is needed to develop a more effective and adaptive framework for logistics service provider selection by integrating diverse criteria and decision-making methodologies, ultimately enhancing the precision and sustainability of procurement processes. This study advocate for leveraging industry-based knowledge in procurement, emphasizing the need to define decision-making elements. The research analyzes nearly 300 logistics procurement projects, using a neural network-based methodology to propose a model that aids businesses in identifying optimal criteria for evaluating logistics service providers based on extensive industry knowledge. The goal of this study is to develop and test a practical model that would support businesses in choosing most suitable criteria for selection of logistics service providers based on cumulative market patterns. The results of this study are as follows. It introduces novel elements by gathering and systematizing unique market data using developed data processing methodology. It innovatively classifies decision-making elements, allocating them into distinct groups for use as features in a neural network. The study further contributes by developing and training a predictive model based on a prepared dataset, addressing pre-defined goals, expectations related to green logistics, and specific requirements in the tendering process for selecting logistics service providers. Study is concluded by summarizing suggestions for future research in area of adopting neural networks for selection of logistics service providers.


Keywords


decision-making; logistics providers; neural networks; model

Full Text:

PDF


References


Andersson D., Norrman A. (2002). Procurement of logistics services—A minutes work or a multi-year project. European Journal of Purchasing & Supply Management, 8, 3–14. doi: 10.1016/S0969-701201)00018-1

Bansal, A., Kumar, P., & Issar, S. (2014). 3PL selection: A multi-criteria decision-making approach. 2013 IEEE International Conference on Industrial Engineering and Engineering Management. https://doi.org/10.1109/ieem.2013.6962557

Barker, J. M., Gibson, A. R., Hofer, A. R., et al. (2021). A competitive dynamics perspective on the diversification of third-party logistics providers’ service portfolios. Transportation Research Part E: Logistics and Transportation Review, 146, 102219. https://doi.org/10.1016/j.tre.2020.102219

Bonab, S. R., Haseli, G., Rajabzadeh, H., et al. (2023). Sustainable resilient supplier selection for IoT implementation based on the integrated BWM and TRUST under spherical fuzzy sets. Decision Making: Applications in Management and Engineering, 6(1), 153–185. https://doi.org/10.31181/dmame12012023b

Brekalo, L., & Albers, S. (2016). Effective logistics alliance design and management. International Journal of Physical Distribution & Logistics Management, 46(2), 212–240. doi: 10.1108/ijpdlm-08-2014-0201

Cooper, O. (2012). The Analytic Network Process Applied in Supply Chain Decisions, in Ethics, and in World Peace. Doctoral thesis, University of Pittsburgh. United States. p.193

Hwang, B., Chang, T. (2015). 3PL selection criteria and their correlations of external environmental factors—An empirical study of Taiwan IC industry. 2015 International Conference on Logistics, Informatics and Service Sciences (LISS). https://doi.org/10.1109/liss.2015.7369643

Jharkharia, S., & Shankar, R. (2007). Selection of logistics service provider: An analytic network process (ANP) approach. Omega, 35(3), 274–289. https://doi.org/10.1016/j.omega.2005.06.005

Kanal, A. (2020). Unit-6 Decision Making Approach. IGNOU. Available online: https://egyankosh.ac.in//handle/123456789/53912 (accessed on 5 March 2023).

Keers, B., & Van Fenema, P. C. (2015). Towards Alliance Performance Management in Service Logistics. Journal of Organization Design, 4(1), 12. https://doi.org/10.7146/jod.18194

Knemeyer, A. M., Corsi, T. M., & Murphy, P. R. (2003). Logistics outsourcing relationships: customer perspectives. Journal of Business Logistics, 24(1), 77–109. Portico. https://doi.org/10.1002/j.2158-1592.2003.tb00033.x

Moosavi, S. M. S., Seifbarghy, M., & Molana, S. M. H. (2023). Flexible fuzzy-robust optimization method in closed-loop supply chain network problem modeling for the engine oil industry. Decision Making: Applications in Management and Engineering, 6(2), 461–502. https://doi.org/10.31181/dmame622023569

Perçin, S., & Min, H. (2013). A hybrid quality function deployment and fuzzy decision-making methodology for the optimal selection of third-party logistics service providers. International Journal of Logistics Research and Applications, 16(5), 380–397. https://doi.org/10.1080/13675567.2013.815696

Political Science. (2017). Essay on Decision-Making. Available online: https://www.politicalsciencenotes.com/essay/public-administration/essay-on-decision-making-top-7-essays-management-public-administration/13710 (accessed on 1 November 2021).

Pomerol, J. C., Adam, F. (2004). Practical Decision Making from the Legacy of Herbert Simon to Decision Support Systems. University College Cork, Ireland. Available online: http://samuellearning.org/decisionmaking/handout%202.pdf (accessed on 1 November 2021).

Pomponi, F., Fratocchi, L., Tafuri, S. R., Palumbo, M. (2013). Horizontal Collaboration in Logistics: A Comprehensive Framework. Research in Logistics & Production, 3, 243–254.

Ramanathan, R. (2003). An Introduction to Data Envelopment Analysis. Tools for Performance Measurement. New Delhi, India: Sage Publications. 203p.

Rattanawiboonsom, V. (2014). Effective Criteria for Selecting Third-Party logistics Providers: The Case of Thai Automotive Industry. World Review of Business Research, 3, 196–205.

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009

Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001

Roszkowska, E. (2011). Multi-criteria decision-making models by applying the TOPSIS method to crisp and interval data. Katowice, Poland: The University of Economics in Katowice. pp. 200–231.

Saaty, T. L. (2010). The Analytic Hierarchy and Analytic Network Measurement Processes: The Measurement of Intangibles.

Saaty, T. L., Vargas, L. (2006). The Analytic Network Process. University of Pittsburgh, United States.

Sammut-Bonnici, T. (2015). Strategic Decision Making. Available online: https://www.academia.edu/40117307/Strategic_Decision_Making_Wilson_D_2015_Strategic_Decision_Making_In_Wiley_Encyclopedia_of_Management_Volume_12_Strategic_Management_eds_C_L_Cooper_J_McGee_and_T_Sammut_Bonnici_ (accessed on 1 November 2021).

Skribans, V. (2023). Road freight service tender documents monitoring in Europe between 2019 and 2023 [Data set]. Mendeley Data. https://doi.org/10.17632/5H5Y5FYX38.1

Tzeng, G. H., Huang, J. J. (2011). Multi attribute decision making. Methods and applications. Boca Raton, FL, US: CRC Press. 350p.

Wang, J., Wang, M., Liu, F., et al. (2015). Multistakeholder Strategic Third‐Party Logistics Provider Selection: A Real Case in China. Transportation Journal, 54(3), 312–338. Portico. https://doi.org/10.5325/transportationj.54.3.0312

Wilding, R., & Juriado, R. (2004). Customer perceptions on logistics outsourcing in the European consumer goods industry. International Journal of Physical Distribution & Logistics Management, 34(8), 628–644. https://doi.org/10.1108/09600030410557767

Zhang, G., Shang, J., & Li, W. (2011). An information granulation entropy-based model for third-party logistics providers evaluation. International Journal of Production Research, 50(1), 177–190. https://doi.org/10.1080/00207543.2011.571453

Zhu, J. (2014). Quantitative Models for Performance Evaluation and Benchmarking. Data Envelopment Analysis with Spreadsheets, 3 ed. United States: Springer. p. 420




DOI: https://doi.org/10.24294/jipd.v8i6.4345

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Aleksandrs Kotlars, Valerijs Skribans

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.