Modeling the Key Factors Influencing Supply Chain Performance in the Distributor Sector

Document Type : Research/ Original/ Regular Article

Authors

1 Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Raja University, Qazvin, Iran

2 Department of Industrial Engineering, Faculty of Engineering, Raja University, Qazvin, Iran

Abstract

The supply chain within the distribution sector is confronted with a range of challenges, including demand volatility, supply disruptions, and a lack of information coordination. This study proposes a hybrid modeling framework designed to identify and analyze the key factors that influence supply chain performance in the distribution domain. The model utilizes system dynamics to examine the behavior of these factors over time. To begin, thirteen influential factors—encompassing both traditional and innovative dimensions such as resilience, profitability, responsiveness, and information sharing—were identified through a systematic literature review and expert-driven questionnaires. The relative importance of these factors was determined using two multi-criteria decision-making techniques: the CRITIC method and Simple Additive Weighting (SAW). A conceptual model was developed using Vensim software and subsequently simulated in MATLAB, incorporating real-world data from the detergent manufacturing industry. Optimization was performed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multi-objective evolutionary algorithm. The results indicate that resilience, with a final weight of 0.24, exerts the most substantial influence on supply chain performance. Furthermore, multiple regression analysis confirmed the statistically significant role of resilience in enhancing system stability and mitigating fluctuations. The proposed model was validated across various scenarios and demonstrates strong potential for generalization to other industries exhibiting similar supply chain configurations.

Keywords

Main Subjects

Smiley face

References

[1] J. D. Sterman, “Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill Education,” 2000. ISBN-13: 9780072389159
[2] T. J. Pettit, J. Fiksel, and K. L. Croxton, “Ensuring supply chain resilience: Development of a conceptual framework,” *J. Bus. Logist.*, vol. 31, no. 1, pp. 1–21, 2011, doi: 10.1002/j.2158-1592.2010.tb00125.x.
[3] U. Jüttner and S. Maklan, “Supply chain resilience in the global financial crisis: An empirical study,” *Supply Chain Manag.: Int. J.*, vol. 16, no. 4, pp. 246–259, 2011, doi: 10.1108/13598541111139062.
[4] D. Ivanov and A. Dolgui, “Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability,” *Int. J. Prod. Res.*, 2020, doi: 10.1080/00207543.2020.1750727.
[5] M. Emmerich and A. Deutz, “A tutorial on multiobjective optimization: Fundamentals and evolutionary methods,” *Nat. Comput.*, vol. 17, no. 3, pp. 585–609, 2018, doi: 10.1007/s11047-018-9685-y.
[6] A. Wieland and C. F. Durach, “Two perspectives on supply chain resilience,” *J. Bus. Logist.*, vol. 36, no. 3, pp. 253–270, 2021, doi: 10.1111/jbl.12271.
[7] R. Dokoohaki, S. Ebrahimi, and A. Askari Far, “Functional bottlenecks in a two-level wholesale–retail food distribution supply chain,” *Supply Chain Manag. J. (SCMJ)*, vol. 24, no. 76, 2022. [In Persian]. doi: 20.1001.1.20089198.1401.24.76.4.5.
[8] A. Amico et al., “Adapting to disruptions: Flexibility as a pillar of supply chain resilience,” arXiv:2304.05290, 2023, doi: 10.1126/sciadv.adj1194.
[9] J. A. Estrada-Garcia et al., “A multi-objective mixed-integer programming approach for supply chain disruption response with lead-time awareness,” arXiv:2308.02687, 2023, doi: 10.48550/arxiv 2308.02687.
[10] M. Faiz Elahi and M. Sharafi, “Development of a closed-loop supply chain mathematical model with fuzzy demand and supplier capacity constraints and its solution via metaheuristic algorithms,” *Supply Chain Manag. J. (SCMJ)*, vol. 25, no. 79, pp. 17–38, 2023. [In Persian]. doi: 20.1001.1.20089198.1402.25.79.2.6.
[11] M. Ebrahimpour, M. Moradi, and A. Fallahpour, “The impact of supply chain dynamics on the firm's sustainable performance with remanufacturing capability and supply chain resilience,” *J. Strateg. Manag. Stud.*, vol. 14, no. 54, pp. 97–117, 2023. [In Persian]. doi: 10.22034/SMSJ.2023.173202.
[12] H. Mollashahi, M. B. Fakhrzadeh et al., “Competition between supply chains based on resilience and sustainability indicators in the supply chain network design problem,” *Supply Chain Manag. J. (SCMJ)*, vol. 26, no. 82, pp. 77–93, 2024. [In Persian]. doi: 20.1001.1.20089198.1403.26.82.6.3.
[13] T. Lazebnik, “Evaluating supply chain resilience during pandemic using agent-based simulation,” arXiv:2405.08830, 2024, doi: 10.48550/arxiv.. 2405.08830.
[14] F. Karimi, J. Haghighat Monfared, and M. Keramati, “Evaluating the resilience and sustainability of the supply chain with the integrated approach of the theory of constraints, process approach and multi-criteria decision making (Case of study: Offshore sector of the oil industry),” *Ind. Manag. Perspect.*, vol. 14, no. 2, pp. 34–65, 2023. [In Persian]. doi: 10.48308/JIMP.14.2.34.
Supply Chain Management
Volume 27, Issue 87 - Serial Number 87
Serial number 87. Summer 2025
September 2025
Pages 83-101

Files

History

  • Receive Date: 26 May 2025
  • Revise Date: 02 July 2025
  • Accept Date: 02 September 2025
  • Publish Date: 21 September 2025

Share

How to cite

Statistics