with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Document Type : Research Article-en

Author

Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

Abstract

The main objective of this research is to create a comprehensive and adaptable framework for assessing performance in agricultural supply chains and develop two improving approaches. The most relevant performance measures are selected to assess the current status of services in agricultural supply chains (ASCs). The contribution of this research is related to the selection of key performance indicators (KPIs) and approaches for enhancing ASC performance. The proposed framework comprises performance measurement and a service selection process. Two approaches have been developed based on the selected KPIs of services in ASC to identify which services require improvement. The proposed approaches are robust and versatile tools for agricultural managers to strategize and enhance their supply chains. A case study is also presented from Iran. For this region, selection approaches prioritize agricultural services such as postproduction consulting, financial support, mechanization, business consulting, and input supply. The framework shows that these services should be improved in order to better meet the needs of the region under study.

Keywords

Main Subjects

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

  1. Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 195(1), 1-20. https://doi.org/10.1016/j.ejor.2008.04.016
  2. Akkerman, R., Farahani, P., & Grunow, M. (2010). Quality, safety and sustainability in food distribution: A review of quantitative operations management approaches and challenges. OR Spectrum, 32(4), 863-904. https://doi.org/10.1007/s00291-010-0223-2
  3. Allaoui, H., Guo, Y., Choudhary, A., & Bloemhof, J. (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computers & Operations Research, 89, 369-384. https://doi.org/10.1016/j.cor.2016.10.012
  4. Aramyan, C., Ondersteijn, O., van Kooten, O., & Lansink, A. O. (2006). Performance indicators in agri-food production chains. In Quantifying the agri-food supply chain (pp. 49–66). Springer. https://doi.org/10.1007/1-4020-4693-6_4
  5. Baltacioglu, T., Ada, E., Kaplan, M. D., Yurt, O., & Kaplan, Y. C. (2007). A new framework for service supply chains. The Service Industries Journal, 27(2), 105-124. https://doi.org/10.1080/02642060601122629
  6. Banaeian, N., Zangeneh, M., & Golinska-Dawson, P. (2022). Multi-criteria sustainability performance assessment of horticultural crops using DEA and ELECTRE IV methods. Renewable Agriculture and Food Systems, 37(6), 649-659. https://doi.org/10.1017/S1742170521000248
  7. Bhagwat, R., & Sharma, M. K. (2007). Performance measurement of supply chain management: A balanced scorecard approach. Computers and Industrial Engineering, 53(1), 43-62. https://doi.org/10.1016/j.cie.2007.04.001
  8. Bigliardi, B., & Bottani, E. (2010). Performance measurement in the food supply chain: A balanced scorecard approach. Facilities, 28(5/6), 249-260. https://doi.org/10.1108/02632771011031493
  9. Buyukozkan, G., Cifci, G., & Guleryuz, S. (2011). Strategic analysis of healthcare service quality using fuzzy AHP methodology. Expert Systems with Applications, 38(8), 9407-9424. https://doi.org/10.1016/j.eswa.2011.01.103
  10. Chang, H. H., Hung, C.-J., Wong, K. H., & Lee, C.-H. (2013). Using the balanced scorecard on supply chain integration performance—a case study of service businesses. Service Business, 7(4), 539-561. https://doi.org/10.1007/s11628-012-0164-2
  11. Chang, P.-T., Hung, K.-C., Lin, K.-P., & Chang, C.-H. (2006). A comparison of discrete algorithms for fuzzy weighted average. IEEE Transactions on Fuzzy Systems, 14(5), 663-675. https://doi.org/10.1109/TFUZZ.2006.879986
  12. Chang, P.-T., Lee, J.-H., Hung, K.-C., Tsai, J.-T., & Perng, C. (2009). Applying fuzzy weighted average approach to evaluate office layouts with Feng-Shui consideration. Mathematics and Computers in Simulation, 79(4), 1514-1537. https://doi.org/10.1016/j.matcom.2008.09.001
  13. Cho, D. W., Lee, Y. H., Ahn, S. H., & Hwang, M. K. (2012). A framework for measuring the performance of service supply chain management. Computers & Industrial Engineering, 62(3), 801-818. https://doi.org/10.1016/j.cie.2011.12.023
  14. Despoudi, S., Spanaki, K., Rodriguez-Espindola, O., & Zamani, E. D. (2021). Sustainability in Agricultural 4.0 supply chains. In Agricultural supply chains and Industry 4.0 (pp. 95-113). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-72770-3_6
  15. Dong, W. M., & Wong, F. S. (1987). Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets and Systems, 21(2), 183-199. https://doi.org/10.1016/0165-0114(87)90018-4
  16. Ellram, L. M., Tate, W. L., & Billington, C. (2007). Services supply management: The next frontier for improved organizational performance. California Management Review, 49(4), 44-66. https://doi.org/10.2307/41166404
  17. Evangelista, S. S., Aro, J. L., Selerio, E., & Pascual, R. (2023). An integrated Fermatean fuzzy multi-attribute evaluation of digital technologies for circular public sector supply chains. International Journal of Computational Intelligence Systems, 16, Article 122. https://doi.org/10.1007/s44196-023-00294-7
  18. Ganeshkumar, C., Pachayappan, M., & Madanmohan, G. (2017). Agri-food supply chain management: Literature review. Intelligent Information Management, 9(2), 68-96. https://doi.org/10.4236/iim.2017.92004
  19. Ghazinoori, S., Olfat, L., Soofi, J., & Ahadi, R. (2020). Investigating the organic agricultural products supply chain in Iran. Journal of Agricultural Science and Technology, 20, 71-85.
  20. Graham, M., Kaboli, D., Sridharan, M., & Taleghani, S. (2012). Creating value and sustainability in agricultural supply chains. MIT Sloan School of Management.
  21. Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 337-347. https://doi.org/10.1016/j.ijpe.2003.08.003
  22. Jifroudi, S., Teimoury, E., & Barzinpour, F. (2020). Designing and planning a rice supply chain: A case study for Iran farmlands. Decision Science Letters, 9(2), 163-180. https://doi.org/10.5267/j.dsl.2020.1.001
  23. Joshi, R., Banwet, D. K., Shankar, R., & Gandhi, J. (2012). Performance improvement of cold chain in an emerging economy. Production Planning & Control, 23(11), 817-836. https://doi.org/10.1080/09537287.2011.591611
  24. Kazemi, M. J., & Samouei, P. (2024). A new bi-level mathematical model for government-farmer interaction regarding food security and environmental damages of pesticides and fertilizers: Case study of rice supply chain in Iran. Computers and Electronics in Agriculture, 219, Article 108771. https://doi.org/10.1016/j.compag.2024.108771
  25. Liou, T. S., & Wang, M. J. J. (1992). Fuzzy weighted average: An improved algorithm. Fuzzy Sets and Systems, 49(1), 105-118. https://doi.org/10.1016/0165-0114(92)90228-C
  26. Mangla, S. K., Luthra, S., Rich, N., Kumar, D., Rana, N. P., & Dwivedi, Y. K. (2018). Enablers to implement sustainable initiatives in agri-food supply chains. International Journal of Production Economics, 203, 379-393. https://doi.org/10.1016/j.ijpe.2018.07.012
  27. Manzini, R., & Accorsi, R. (2013). The new conceptual framework for food supply chain assessment. Journal of Food Engineering, 115(2), 251-263. https://doi.org/10.1016/j.jfoodeng.2012.10.026
  28. Mapes, J., New, C., & Szwejczewski, M. (1997). Performance trade-offs in manufacturing plants. International Journal of Operations & Production Management, 17(9), 1020-1033. https://doi.org/10.1108/01443579710174642
  29. Morkūnas, M., Rudienė, E., & Ostenda, A. (2022). Can climate-smart agriculture help to assure food security through short supply chains? A systematic bibliometric and bibliographic literature review. Business, Management and Economics Engineering, 20(2), 207-223. https://doi.org/10.3846/bmee.2022.17101
  30. Oubrahim, I., Sefiani, N., & Happonen, A. (2022). Supply chain performance evaluation models: A literature review. Acta Logistica, 9(2), 207-221. https://doi.org/10.22306/al.v9i2.298
  31. Ramos, E., Coles, P. S., Chavez, M., & Hazen, B. (2022). Measuring agri-food supply chain performance: Insights from the Peruvian kiwicha industry. Benchmarking: An International Journal, 29(5), 1484-1512. https://doi.org/10.1108/BIJ-10-2020-0544
  32. Rehman, A. U., Al-Zabidi, A., AlKahtani, M., Umer, U., & Usmani, Y. S. (2020). Assessment of supply chain agility to foster sustainability: Fuzzy-DSS for a Saudi manufacturing organization. Processes, 8(5), Article 577. https://doi.org/10.3390/pr8050577
  33. Relich, M., & Pawlewski, P. (2017). A fuzzy weighted average approach for selecting portfolio of new product development projects. Neurocomputing, 231, 19-27. https://doi.org/10.1016/j.neucom.2016.05.104
  34. Routroy, S., & Behera, A. (2017). Agriculture supply chain: A systematic review of literature and implications for future research. Journal of Agribusiness in Developing and Emerging Economies, 7(3), 275-302. https://doi.org/10.1108/JADEE-05-2015-0016
  35. Sengupta, K., Heiser, D., & Koll, L. (2006). Manufacturing and service supply chain performance: A comparative analysis. Journal of Supply Chain Management, 42(4), 4-15. https://doi.org/10.1111/j.1745-493X.2006.04204002.x
  36. Singh, N., Biswas, R., & Banerjee, M. (2023). A systematic review to identify obstacles in the agricultural supply chain and future directions. Journal of Agribusiness in Developing and Emerging Economies. Advance online publication. https://doi.org/10.1108/JADEE-12-2022-0262
  37. Thumrongvut, P., Sethanan, K., Pitakaso, R., Jamrus, T., & Golinska-Dawson, P. (2022). Application of Industry 3.5 approach for planning of more sustainable supply chain operations for tourism service providers. International Journal of Logistics Research and Applications. Advance online publication. https://doi.org/10.1080/13675567.2022.2074750
  38. Trivellas, P., Malindretos, G., & Reklitis, P. (2020). Implications of green logistics management on sustainable business and supply chain performance: Evidence from a survey in the Greek agri-food sector. Sustainability, 12(24), Article 10515. https://doi.org/10.3390/su122410515
  39. Ulutas, A., Shukla, N., Kiridena, S., & Gibson, P. (2016). A utility-driven approach to supplier evaluation and selection: Empirical validation of an integrated solution framework. International Journal of Production Research, 54(5), 1554-1567. https://doi.org/10.1080/00207543.2015.1098787
  40. Uysal, F. (2012). An integrated model for sustainable performance measurement in supply chain. Procedia Engineering, 62, 689-694. https://doi.org/10.1016/j.proeng.2012.08.110
  41. van der Vorst, J. G. A. J., Peeters, L., & Bloemhof, J. M. (2013). Sustainability assessment framework for food supply chain logistics: Empirical findings from Dutch food industry. International Journal on Food System Dynamics, 4(2), 130-139. https://doi.org/10.18461/ijfsd.v4i2.424
  42. Vorst, J. G. A. J. (2005). Performance measurement in agri-food supply chain networks. In C. J. Ondersteijn (Ed.), Quantifying the agri-food supply chain (pp. 13–24). Springer. https://doi.org/10.1007/1-4020-4693-6_2
  43. Wu, X. Q., Pu, F., Shao, S. H., & Fang, J. N. (2004). Trapezoidal fuzzy AHP for the comprehensive evaluation of highway network programming schemes in Yangtze River Delta. Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, 15-19 June 2004 (pp. 4114-4118). IEEE. https://doi.org/10.1109/WCICA.2004.1343728
  44. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  45. Zangeneh, M., Nielsen, P., Akram, A., & Keyhani, A. (2014). A performance evaluation system for agricultural services in agricultural supply chain. Management and Production Engineering Review, 5(3), 70-80. https://doi.org/10.2478/mper-2014-0029
  46. Zheng, G., Zhu, N., Tian, Z., Chen, Y., & Sun, B. (2012). Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Safety Science, 50(2), 228-239. https://doi.org/10.1016/j.ssci.2011.08.065
  47. Zimmermann, H. J. (2001). Fuzzy set theory—and its applications (4th ed.). Springer. https://doi.org/10.1007/978-94-010-0646-0
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