Evaluation of Rice Mechanization Supply Chain Indicators with SCOR Model Approach

Document Type : Research Article

Authors

• M. Zangeneh
• N. Banaeian

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

https://doi.org/10.22067/jam.2023.82005.1163

Abstract

Introduction
Agricultural mechanization plays a crucial role in driving agricultural development and is considered one of the most capital-intensive inputs in the agricultural sector. Effective supply chain management is a crucial necessity for improving the quality of agricultural machinery and reducing operational expenses in agricultural mechanization. This is imperative for the advancement of agricultural mechanization. The present research aims to identify the primary structures of the supply chain for rice agricultural machinery in the provinces of Gilan and Mazandaran in Iran. The study also examined the important functional aspects of the chain members, including manufacturers, importers, retailers, and farmers who serve as the ultimate consumers of the chain's products. Furthermore, the research delved into the processes that govern the supply chain.
Materials and Methods
Measuring supply chain performance involves utilizing the Supply Chain Operations Reference model (SCOR) with five distinct dimensions: cost (12 questions), responsiveness (22 questions), flexibility (16 questions), assets (13 questions), and reliability (30 questions). The study data were analyzed using SPSS software. Additionally, latent variables were generated at each level of the hierarchy by using the variables from the aforementioned model. The normality of the variables was assessed using the Kolmogorov-Smirnov test. The evaluation of normal variables was conducted through a one-sample t-test, while abnormal variables were evaluated with a one-sample Wilcoxon test. Furthermore, descriptive analysis of the expectations and constraints of manufacturers and importers regarding rice machines was carried out.
Results and Discussion
The Wilcoxon test results indicate the impact of commitment, cost management, and communication on the average test value. The variables of normal distribution such as human resource management, quality management, strategic organization, flexibility, responsiveness, performance, and reliability in stores, exhibit significant deviation from the mean value. The majority of store managers and agricultural rice machinery dealers lack formal education in the field of agricultural machinery. Including individuals with educational backgrounds in agricultural machinery at various stages of the supply chain will likely improve the dissemination of information throughout the chain. Employing dependable techniques for transmitting accurate information regarding consumers' quality requirements can assist suppliers in manufacturing or importing superior-quality machinery. This approach not only minimizes uncertainty in the supply chain and streamlines inventory management but also reduces the lead time for meeting consumer demands.
Conclusion
The continuous demand for rice agricultural machinery in Iran has resulted in the bullwhip effect phenomenon being perceived as a less significant challenge in the supply chain. Currently, local manufacturing enterprises have relatively limited knowledge regarding the market and technical needs of rice farmers compared to their foreign counterparts. It is advisable for manufacturing companies to broaden their comprehension of consumer behavior and needs by diversifying their market evaluation techniques.

Keywords

• Importers
• Manufacturers
• Retailers
• Supply chain

Main Subjects

• Agricultural systems engineering (greenhouse, fish farming, mushroom production)

©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

References

1. Azizi, O., Zargham Brujeni, H., Ziaei, M., & Taghavi Fard, M. T. (2022). Value creation framework through integrated electronic supply chain of tourism services. Two Quarterly Journal of Tourism Social Studies, 10(19), 91-114. https://doi.org/10.52547/journalitor.36273.10.19.91
2. Ballou, R. H., Gilbert, S. M., & Mukherjee A. (2000). New managerial challenges from supply chain opportunities. IEEE Engineering Management Review, 28(3), 7-16. https://doi.org/10.1016/S0019-8501(99)00107-8
3. Beamon, B. M. (1999). Measuring supply chain performance. International Journal of operations & Production Management 19(3), 275-292. https://doi.org/10.1108/01443579910249714
4. Bhagwat, R., & Sharma, M. K. (2007). Performance measurement of supply chain management: A balanced SCOR ecard approach. Computers & Industrial Engineering 53(1), 43-62. https://doi.org/10.1016/j.cie.2007.04.001
5. Ellram, L. M., Zsidisin, G. A., Siferd, S. P., & Stanly, M. J. (2002). The impact of purchasing and supply management activities on corporate success. Journal of Supply Chain Mangement, 38, 4-7. https://doi.org/10.1111/j.1745-493X.2002.tb00116.x
6. Faisal, M. N., Banwet, D. K., & Shankar, R. (2007). Information risk management in supply chains: an assessment and mitigation framework. Journal of Enterprise Information Management 20(6), 677-699. https://doi.org/10.1108/17410390710830727
7. Giannakis, M. (2011). Management of service supply chains with a service oriented reference model: the case of management consulting source. Supply Chain Management: An International Journal 16(5), 346-361. https://doi.org/10.1108/13598541111155857
8. Grifell-Tatje, E., & Lovell, C. A. K. (2014). Productivity, price recovery, capacity constraints and their financial consequences. Journal of Productivity Analysis, 41, 3-17. https://doi.org/10.1007/s11123-013-0373-8
9. Grover, S., Agrawal, V. P., & Khan, I. A. (2006). Role of human factors in SCOR: a graph theoretic approach. Benchmarking: An International Journal 13(4), 447-468. https://doi.org/10.1108/14635770610676290
10. Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications. International Journal of Production Research, 45(12), 2819-2840. https://doi.org/10.1080/00207540600806513
11. Gunasekaran, , Patel, C., & Tirtiroglu, E. (2001). Performance Measures and Metrics in a Supply Chain Environment. International Journal of Operations & Production Management, 21, 71-87. https://doi.org/10.1108/01443570110358468
12. 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
13. Joshi, R., Banwet, D. K., Shankar, R., & Ganhdi, J. (2012). Performance improvement of cold chain in an emerging economy. Production Planning and Control, 23(10-11), 817-836. https://doi.org/10.1080/09537287.2011.642187
14. Kalantari, K. (2008). Data processing and analysis in socio-economic research. Tehran, Farhang Saba. (In Persian).
15. Manzini, R., & Accorsi, R. (2013). The new conceptual framework for food supply chain assessment. Journal of Food Engineering, 115, 251-263. https://doi.org/10.1016/j.jfoodeng.2012.10.026
16. Milind Kumar, S., & Rajat, B. (2007). An integrated BSC‐AHP approach for supply chain management evaluation. Measuring Business Excellence, 11(3), 57-68. https://doi.org/10.1108/13683040710820755
17. Olfat, L., BamdadSoofi, J., Amiri, M., & Azbari, M. E. (2012). A model for supply chain performance evaluation using by network data envelopment analysis model (Case of: Supply chain of pharmaceutical companies in Tehran Stock Exchange. Industrial Management Studies, 10(26), 9-34.
18. Parker, C. (2000). Performance measurement. Work Study, 49, 63-66. https://doi.org/10.1108/00438020010311197
19. Rao, R. V., & Padmanabhan, K. K. (2007). Rapid prototypeing process selection using graph theory and matrix approach. Journal of Materials Processing Technology, 194(1-3), 81-88. https://doi.org/10.1016/j.jmatprotec.2007.04.003
20. Shafiee, M., Hosseinzadeh Lotfi, F., & Saleh, H. (2014). Supply chain performance evaluation with data envelopment analysis and balanced ecard approach. Applied Mathematical Modelling, 38(21), 5092-5112. https://doi.org/10.1016/j.apm.2014.03.023
21. Shahbandarzadeh, H., & Abadi, F. (2016). Evaluation of supply chain performance with supply chain operational reference model approach, case study of Iran-Sadra Marine Industries Company. Commercial Surveys, 79(14), 37-49. (In Persian)
22. Shojaee, H. S. (2016). Assessing Effective Factors on Improving Supply Chain Performance Using Analytical Hierarchy Process in Food Industries. Journal of Value Chain Management, 1(2), 1-16. (In Persian)
23. Singbo, A. G., Lansink, A. O., & Emvalomatis, G. (2014). Estimating farmers' productive and marketing inefficiency: an application to vegetable producers in Benin. Journal of Productivity Analysis, 42, 157-169. https://doi.org/10.1007/s11123-014-0391-1
24. Swinnen, J. F. M., & Vranken, L. (2010). Reforms and agricultural productivity in Central and Eastern Europe and the Former Soviet Republics: 1989-2005. Journal of Productivity Analysis, 33, 241-258. https://doi.org/10.1007/s11123-009-0162-6
25. Yang, C. L. (2010). Improving supplier performance using a comprehensive scheme. Production Planning and Control, 21(7), 653-663. https://doi.org/10.1080/09537280903517655
26. Zanganeh, M., Banaeian, N., Payman, S. H., & Khani, M. (2019). Performance Evaluation of Rice Farm Machinery Dealers using SCOR Model and DEA Method. Iranian Journal of Biosystems Engineering, 50(2), 281-292. https://doi:10.22059/ijbse.2018.262708.665079