تحلیل محرک های ایجاد قابلیت ردیابی در زنجیره تامین مواد غذایی

نوع مقاله : پژوهشی

نویسنده

استادیار، گروه مدیریت صنعتی، دانشکده علوم اداری و اقتصاد، دانشگاه اراک، اراک، ایران

چکیده

سامانه ردیابی، مواد غذایی ناایمن و با کیفیت پایین را در مراحل تولید، پردازش و توزیع به حداقل می­رساند. برای پیاده­سازی سیستم ردیابی، باید محرک­هایی که قابلیت ردیابی در زنجیره­تامین مواد غذایی را فعال می­کنند، درک کرد. پژوهش حاضر تلاشی برای تحلیل محرک­های ایجاد قابلیت ردیابی در زنجیره­تامین مواد غذایی است. روش پژوهش حاضر، از نظر هدف کاربردی و از حیث گردآوری داده­ها، پیمایشی است. نمونه آماری پژوهش 20 خبره صنایع غذایی هستند. براساس مرور ادبیات و غربال­گری، 11 محرک ایجاد سیستم ردیابی مواد غذایی مشخص شدند. از پرسشنامه مدلسازی ساختاری تفسیری برای مقایسات زوجی توسط خبرگان استفاده شد. برای تجزیه­و­تحلیل    داده­های گردآوری­شده، از روش مدلسازی ساختاری تفسیری به منظور سطح­بندی محرک­های ایجاد قابلیت ردیابی در زنجیره­تامین مواد غذایی استفاده شد و براین اساس، محرک­ها در نه سطح اولویت­بندی شده اند. در مدل ساختاری، پذیرش صنعت 4.0 به­عنوان محرک تاثیرگذار شناسایی شده و محرک­های حمایت از بازار و مزیت رقابتی دارای اثرپذیری بالایی هستند. برای تحلیل تاثیرات متقابل بین محرک­ها نیز از ماتریس تاثیرات متقابل (MICMAC) استفاده شد. در ادامه، به­منظور برازش ساختار به­دست آمده، از روش مدلسازی معادلات ساختاری با استفاده از نرم­افزار Smart PLS 3.0 استفاده شده است. بدین منظور پرسشنامه­ای حاوی 33 سوال با طیف 5 گانه لیکرت طراحی گردید و به منظور تکمیل آن از 170 از کارکنان صنایع غذایی نظرخواهی گردید. روایی و پایایی پرسشنامه و همچنین برازش مدل نیز تائید شد. نتایج حاصل از این مطالعه می­تواند به عنوان چراغ راهی برای استقرار پایدار سامانه ردیابی در صنایع غذایی کشور مورد استفاده قرار گیرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Analysis of Drivers for Implementing Traceability Capability in the Food Supply Chain

نویسنده [English]

  • HamidReza Talaie
Department of Industrial Management, Faculty of Administrative Sciences and Economics, Arak University, Arak, Iran.
چکیده [English]

The traceability system minimizes unsafe and low-quality food products during production, processing, and distribution stages. To implement a traceability system, it is crucial to understand the drivers that enable traceability in the food supply chain. This study aims to analyze the drivers for implementing traceability capability in the food supply chain. The research method is applied in terms of purpose and survey-based in terms of data collection. The statistical sample consists of 20 food industry experts. Based on literature review and screening, 11 drivers for establishing a food traceability system were identified. A pairwise comparison questionnaire using interpretive structural modeling (ISM) was employed by the experts. To analyze the collected data, ISM was utilized to prioritize the drivers for implementing traceability capability in the food supply chain, resulting in a nine-level prioritization. In the structural model, the adoption of Industry 4.0 was identified as an influential driver, while market support and competitive advantage were highly influenced drivers. The MICMAC matrix was used to analyze the interrelationships between the drivers. Subsequently, the structural equation modeling (SEM) method using Smart PLS 3.0 software was employed to fit the obtained structure. A questionnaire containing 33 Likert scale questions was designed and completed by 170 food industry employees. The validity and reliability of the questionnaire, as well as the model fit, were confirmed. The results of this study can serve as a guideline for the sustainable implementation of the traceability system in the country's food industries.

کلیدواژه‌ها [English]

  • Supply Chain Management
  • Traceability Capability
  • Interpretive Structural Modeling
  • Structural Equation Modeling
  • Food Supply Chain

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[1]   T. K. Dasaklis, T. G. Voutsinas, G. T. Tsoulfas, and F. Casino, “A systematic literature review of blockchain-enabled supply chain traceability implementations,” Sustainability, vol. 14, no. 4, p. 2439, 2022, doi: https://doi.org/10.3390/su14042439.
[2]   L. Dong, P. Jiang, and F. Xu, “Impact of traceability technology adoption in food supply chain networks,” Manage Sci, vol. 69, no. 3, pp. 1518–1535, 2023, doi: https://doi.org/10.1287/mnsc.2022.4440.
[3]   D. Kafetzopoulos, S. Margariti, C. Stylios, E. Arvaniti, and P. Kafetzopoulos, “Managing the traceability system for food supply chain performance,” Int. J. Product. Perform. Manag, vol. 73, no. 2, pp. 563–582, 2023, doi: https://doi.org/10.1108/IJPPM-12-2021-0690.
[4]   X. Zhou, M. Pullman, and Z. Xu, “The impact of food supply chain traceability on sustainability performance,” Oper. Manag. Res., vol. 15, no. 1, pp. 93–115, 2022, doi: https://doi.org/10.1007/s12063-021-00189-w.
[5]   N. Gupta, G. Soni, S. Mittal, I. Mukherjee, B. Ramtiyal, and D. Kumar, “Evaluating traceability technology adoption in food supply chain: a game theoretic approach,” Sustainability, vol. 15, no. 2, p. 898, 2023, doi: https://doi.org/10.3390/su15020898.
[6]   A. Srivastava and K. Dashora, “A Fuzzy ISM approach for modeling electronic traceability in agri-food supply chain in India,” Ann Oper Res, vol. 315, no. 2, pp. 2115–2133, 2022, doi: https://doi.org/10.1007/s10479-021-04072-6.
[7]   G. Chiaraluce, D. Bentivoglio, A. Finco, M. Fiore, F. Contò, and A. Galati, “Exploring the role of blockchain technology in modern high-value food supply chains: Global trends and future research directions,” Agric. food econ., vol. 12, no. 1, p. 6, 2024, doi: https://doi.org/10.1186/s40100-024-00301-1.
[8]   A. Haleem, S. Khan, and M. I. Khan, “Traceability implementation in food supply chain: A grey-DEMATEL approach,” Inf. Process. Agric., vol. 6, no. 3, pp. 335–348, 2019, doi: https://doi.org/10.1016/j.inpa.2019.01.003.
[9]   M. N. Faisal and F. Talib, “Implementing traceability in Indian food-supply chains: An interpretive structural modeling approach,” J. Foodserv. Bus. Res., vol. 19, no. 2, pp. 171–196, 2016, doi: https://doi.org/10.1080/15378020.2016.1159894.
[10] J. Razmi, R. Tavakoli Moghadam, F. Jolai, and B. Yari, “Design of a conceptual reference model for implementation of traceability in a supply chain based upon structured modeling approach,” IMJ, vol. 2, no. 2, pp. 19–60, 2010, doi: https://dorl.net/dor/20.1001.1.20085885.1389.2.2.2.6. [In Persian].
[11] M. E. Latino, M. Menegoli, M. Lazoi, and A. Corallo, “Voluntary traceability in food supply chain: a framework leading its implementation in Agriculture 4.0,” Technol Forecast Soc Change, vol. 178, p. 121564, 2022, doi: https://doi.org/10.1016/j.techfore.2022.121564.
[12] G. M. Razak, L. C. Hendry, and M. Stevenson, “Supply chain traceability: A review of the benefits and its relationship with supply chain resilience,” PROD PLAN CONTROL, vol. 34, no. 11, pp. 1114–1134, 2023, doi: https://doi.org/10.1080/09537287.2021.1983661.
[13] X. Zhou, H. Lu, and Z. Xu, “A balance of economic advancement and social needs via improving supply chain traceability for future food sustainability: an empirical study from China,” PROD PLAN CONTROL, pp. 1–21, 2023, doi: https://doi.org/10.1080/09537287.2023.2240751.
[14] A. Susanty, N. B. Puspitasari, Z. F. Rosyada, M. A. Pratama, and E. Kurniawan, “Design of blockchain-based halal traceability system applications for halal chicken meat-based food supply chain,” Int. J. Inf. Technol., vol. 16, no. 3, pp. 1449–1473, 2024, doi: https://doi.org/10.1007/s41870-023-01650-8.
[15] S. S. Kamble, A. Gunasekaran, and S. A. Gawankar, “Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications,” Int J Prod Econ, vol. 219, pp. 179–194, 2020, doi: https://doi.org/10.1016/j.ijpe.2019.05.022.
[16] H. Talaie, “Analyzing the Factors Affecting the Financial Sustainability of the Food Industry Supply Chain during the Pandemic,” Industrial Innovations: Requirements and Strategies (JII), vol. 1, no. 3, pp. 197–216, 2023, doi: https://doi.org/10.61186/jii.1.3.197. [In Persian].
[17] E. Abdollahzadeh, “Application of food traceability systems in the seafood supply chain and sturgeon products: increasing safety and quality, reducing smuggling and fraud,” Sturgeon Extention Journal (SEJ), vol. 4, no. 7, pp. 38–47, 2022, [Online]. Available: https://sej.areeo.ac.ir/article_126561.html [In Persian].
[18] A. Hassoun et al., “Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies,” Crit Rev Food Sci Nutr, vol. 64, no. 3, pp. 873–889, 2024, doi: https://doi.org/10.1080/10408398.2022.2110033.
[19] R. Gupta and R. Shankar, “Managing food security using blockchain-enabled traceability system,” Benchmarking: An International Journal (BIJ), vol. 31, no. 1, pp. 53–74, 2024, doi: https://doi.org/10.1108/BIJ-01-2022-0029.
[20] P. Blaettchen, A. P. Calmon, and G. Hall, “Traceability technology adoption in supply chain networks,” Manage Sci, 2024, doi: https://doi.org/10.1287/mnsc.2022.01759.
[21] K. M. Karlsen, B. Dreyer, P. Olsen, and E. O. Elvevoll, “Literature review: Does a common theoretical framework to implement food traceability exist?” Food Control, vol. 32, no. 2, pp. 409–417, 2013, doi: https://doi.org/10.1016/j.foodcont.2012.12.011.
[22] A. Regattieri, M. Gamberi, and R. Manzini, “Traceability of food products: General framework and experimental evidence,” J Food Eng, vol. 81, no. 2, pp. 347–356, 2007, doi: Traceability of food products: General framework and experimental evidence.
[23] S. Thangamayan, K. Pradhan, G. B. Loganathan, S. Sitender, S. Sivamani, and M. Tesema, “[Retracted] Blockchain‐Based Secure Traceable Scheme for Food Supply Chain,” J Food Qual, vol. 2023, no. 1, p. 4728840, 2023, doi: https://doi.org/10.1155/2023/4728840.
[24] R. Yahyayi and M. Kavoosi-Kalashami, “Evaluation of the effective drivers in the use of blockchain technology in the rice supply chain,” Agricultural Market and Economics (AME), vol. 1, no. 2, pp. 89–100, Jan. 2024, doi: 10.61186/ame.1.2.89. [In Persian].
[25] E. Hedayati, M. Zeinalnezhad, and S. Samiallah, “Identify and Rank Barriers to the Use of Blockchain Technology in the Sustainable Supply Chain of the Food Industry,” Supply Chain Management (SCMJ), vol. 26, no. 82, pp. 17–42, 2024, [Online]. Available: https://scmj.ihu.ac.ir/article_208846.html [In Persian].
[26] M. Nasiri Galeh and S. Sahraei, “Investigating the Impact of Blockchain Implementation in the Supply Chain of Dairy Products in Iran,” Supply Chain Management (SCMJ), vol. 26, no. 82, pp. 95–102, 2024, doi: DOR: 20.1001.1.20089198.1403.26.82.7.4. [In Persian].
[27] A. Rahimi, G. Taghizadeh, and S. Mahmoudabadi, “Proposing an interpretive structural model of barriers to using blockchain technology in the food supply,” Research in Production and Operations Management (jpom), vol. 13, no. 1, pp. 79–104, 2022, doi: 10.22108/jpom.2022.131836.1412. [In Persian].
[28] L. Rezaee and R. Babazadeh, “Investigating the relationships between the influencing indicators of blockchain in the food industry,” Research in Production and Operations Management (jpom), vol. 11, no. 3, pp. 95–116, 2020, doi: 10.22108/jpom.2021.123858.1279. [In Persian].
[29] A. Patidar, M. Sharma, and R. Agrawal, “Prioritizing drivers to creating traceability in the food supply chain,” Procedia CIRP, vol. 98, pp. 690–695, 2021, doi: https://doi.org/10.1016/j.procir.2021.01.176.
[30] W. J. Potter, An analysis of thinking and research about qualitative methods. Routledge, 2013. doi: https://doi.org/10.4324/9780203811863.
[31] S. Shoar and N. Chileshe, “Exploring the Causes of Design Changes in Building Construction Projects: An Interpretive Structural Modeling Approach,” Sustainability, vol. 13, no. 17, p. 9578, 2021, doi: https://doi.org/10.3390/su13179578.
[32] A. Dixit, P. Suvadarshini, and D. V. Pagare, “Analysis of barriers to organic farming adoption in developing countries: a grey-DEMATEL and ISM approach,” J Agribus Dev Emerg Econ, vol. 14, no. 3, pp. 470–495, 2024, doi: https://doi.org/10.1108/JADEE-06-2022-0111.
[33] A. Arantes and L. M. D. F. Ferreira, “Development of delay mitigation measures in construction projects: A combined interpretative structural modeling and MICMAC analysis approach,” Prod. Plan. Control., vol. 35, no. 10, pp. 1164–1179, 2024, doi: https://doi.org/10.1080/09537287.2022.2163934.
[34] N. Mkedder and M. Das, “Metaverse integration challenges: An in-depth ISM and MICMAC analysis,” J. Retail. Consum. Serv., vol. 77, p. 103684, 2024, doi: https://doi.org/10.1016/j.jretconser.2023.103684.
[35] M. A. Palsodkar, M. R. Nagare, R. B. Pansare, and V. S. Narwane, “An adoption framework for agile new product development using hybrid RBWM-ISM-Fuzzy MICMAC approach,” J. Model. Manag., 2024, doi: https://doi.org/10.1108/JM2-11-2023-0262.
[36] J. F. Hair Jr, M. Sarstedt, L. Hopkins, and V. G. Kuppelwieser, “Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research,” Eur. Bus. Rev., vol. 26, no. 2, pp. 106–121, 2014, doi: https://doi.org/10.1108/EBR-10-2013-0128.
[37] J. F. Hair Jr et al., “An introduction to structural equation modeling,” Partial least squares structural equation modeling (PLS-SEM) using R: a workbook, pp. 1–29, 2021, doi: https://doi.org/10.1007/978-3-030-80519-7_1.
[38] N. Safaie, S. Yousefpour, and S. Mohammadi, “A systematic review on previous research of radio frequency identification systems in supply chain,” Supply Chain Management (SCMJ), vol. 23, no. 71, pp. 15-32. https://dorl.net/dor/20.1001.1.20089198.1400.23.71.2.6 [In Persian].
دوره 26، شماره 84 - شماره پیاپی 84
شماره پیا پی 84 پاییز 1403
آذر 1403
صفحه 45-60
  • تاریخ دریافت: 18 مرداد 1403
  • تاریخ بازنگری: 07 مهر 1403
  • تاریخ پذیرش: 29 آبان 1403
  • تاریخ انتشار: 20 آذر 1403