Evaluation of Supply Chain Performance Based on SCOR Criteria (Case Study: Zanjan Zinc Refinery Company)

Document Type : Research/ Original/ Regular Article

Authors

1 Industrial engineering faculty, Malek Ashtar University of Technology

2 Industrial Engineering, Zanjan University, Iran

Abstract

In today's era, the supply chain has taken a large share of human activities. In the field of SCM, to achieve better performance and focus on core capabilities, supplier performance monitoring is very important. On the one hand, the issue of globalization and transcontinental outsourcing, and on the other hand, the issue of viability has imposed high importance to choosing the right approach to the supplier based on the strategic importance and supply risk. Therefore, the success of organizations in the global competition environment is highly dependent on formulating the type of relationship with suppliers, including affiliation, cooperation, long-term rewards, and a multi-source approach. In this paper, by using adaptive neuro-fuzzy inference, the performance of the SC has been evaluated based on the components of reliability, responsiveness, agility, costs and efficiency of asset management in the case study of " Zanjan Zinc Refinery Company". The output of examining the indicators related to each component in this method will lead to predicting the performance of the supplier and formulating a strategy to achieve the desired situation. In addition, the comparison of clustering models showed that the fuzzy mean clustering method with the number of 4 clusters has the lowest prediction error. The results of this research make it possible to measure the performance of the SC from different perspectives of the SCOR model. One can also provide information that helps the system expert to analyze the gap between the expected and the current performance level in terms of the mentioned components.

Keywords

Main Subjects


Smiley face

  • Rosyidah, N. Khoirunnisa, U. Rofiatin, A. Asnah, A. Andiyan, and D. Sari, "Measurement of key performance indicator Green Supply Chain Management (GSCM) in palm industry with green SCOR model," Materials Today: Proceedings, vol. 63, pp. S326-S332, 2022.
  • Bandari, and A Estakhryan Haghighi, "Analysis of the leather whip effect using forecasting methods in the supply chain", 1st international and the 2nd national conference on management, ethics and business, 2019. (In Persian)
  • Ghadimi, A. Dargi, and C. Heavey, "Sustainable supplier performance scoring using audition check-list based fuzzy inference system: A case application in automotive spare part industry," Computers & Industrial Engineering, vol. 105, pp. 12-27, 2017.
  • Safaei, P. Ghasemi, F. Goodarzian, and M. Momenitabar, "Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm," Environmental Science and Pollution Research, vol. 29, no. 53, pp. 79754-79768, 2022.
  • M. Bassiouni, R. K. Chakrabortty, O. K. Hussain, and H. F. Rahman, "Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions," Expert Systems with Applications, vol. 211, p. 118604, 2023.
  • Vegter, J. van Hillegersberg, and M. Olthaar, "Performance measurement system for circular supply chain management," Sustainable Production and Consumption, 2023.
  • Saleheen and M. M. Habib, "Embedding attributes towards the supply chain performance measurement," Cleaner Logistics and Supply Chain, vol. 6, p. 100090, 2023.
  • A. Ebrahimi Jarjafaki, and S.M.T Fatemi Qomi, "Presenting a hybrid framework of neural networks for efficient forecasting in the supply chain", International Conference of the Iranian Association for Operations Research,. 2018, (In Persian).
  • Maestrini, D. Luzzini, P. Maccarrone, and F. Caniato, "Supply chain performance measurement systems: A systematic review and research agenda," International Journal of Production Economics, vol. 183, pp. 299-315, 2017.
  • Spieske and H. Birkel, "Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic," Computers & Industrial Engineering, vol. 158, p. 107452, 2021.
  • Sathyan, P. Parthiban, R. Dhanalakshmi, and M. Sachin, "An integrated Fuzzy MCDM approach for modelling and prioritising the enablers of responsiveness in automotive supply chain using Fuzzy DEMATEL, Fuzzy AHP and Fuzzy TOPSIS," Soft Computing, vol. 27, no. 1, pp. 257-277, 2023.
  • Bastas and K. Liyanage, "Sustainable supply chain quality management: A systematic review," Journal of cleaner production, vol. 181, pp. 726-744, 2018.
  • S. Nudurupati, U. S. Bititci, V. Kumar, and F. T. Chan, "State of the art literature review on performance measurement," Computers & Industrial Engineering, vol. 60, no. 2, pp. 279-290, 2011.
  • Esmaili, "Evaluation of supply chain performance using supply chain operations reference model (SCOR)-Fuzzy TOPSIS (Case study: Saipa Automobile Company)", Master's thesis, faculty of Islamic Azad University, Tehran Central Branch, Faculty of Management, 2014. (In Persian)
  • Mishra, A. Gunasekaran, T. Papadopoulos, and R. Dubey, "Supply chain performance measures and metrics: a bibliometric study," Benchmarking: An International Journal, vol. 25, no. 3, pp. 932-967, 2018.
  • R. Lima-Junior and L. C. R. Carpinetti, "Quantitative models for supply chain performance evaluation: A literature review," Computers & Industrial Engineering, vol. 113, pp. 333-346, 2017.
  • Q. N. Karimi, P, "A conceptual Model to Study the Effect of Enterprise Risks on Performance of IT Companies,", Journal of Information Technology Management, vol. 1, no. 2, pp. 119-, 2009. (In Persian).
  • L. Newbert, "Empirical research on the resource‐based view of the firm: an assessment and suggestions for future research," Strategic management journal, vol. 28, no. 2, pp. 121-146, 2007.
  • C. Bozarth, R. B. Handfield, and H. J. Weiss, Introduction to operations and supply chain management. Pearson Prentice Hall Upper Saddle River, NJ, 2008.
  • L. Handoko, R. Aryanto, and I. G. So, "The impact of enterprise resources system and supply chain practices on competitive advantage and firm performance: Case of Indonesian companies," Procedia Computer Science, vol. 72, pp. 122-128, 2015.
  • G. Enz and D. M. Lambert, "A supply chain management framework for services," Journal of Business Logistics, vol. 44, no. 1, pp. 11-36, 2023.
  • Deshpande, "Supply chain management dimensions, supply chain performance and organizational performance: An integrated framework," International Journal of Business and Management, vol. 7, no. 8, p. 2, 2012.
  • Shahbandarzadeh, F. Abadi, "Evaluation of supply chain performance with SCOR approach, Journal of Business Studies. vol. 14, no. 79, pp. 37-49, 2016. (In Persian)
  • Zamanian and M. Salehi, "Evaluating the Performance of the Service Supply Chain from a Strategic Viewpoint Based on the SCOR Approach and the Fuzzy Prioritization Technique," Iranian Journal Of Supply Chain Management, vol. 24, no. 75, pp. 75-85, 2022. (In Persian)
  • Danaei Fard, S.M. Alwani, and A. Azar. "Quantitative research methodology in management: a comprehensive approach". Safar Publishing House, Tehran, 1st edition, 2013. (In Persian).
  • -N. Wang, Y.-F. Huang, I.-F. Cheng, and V. T. Nguyen, "A multi-criteria decision-making (MCDM) approach using hybrid SCOR metrics, AHP, and TOPSIS for supplier evaluation and selection in the gas and oil industry," Processes, vol. 6, no. 12, p. 252, 2018.