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شیبانی نوقابی, ملیحه, کرباسی, علیرضا, محمدی, حسین. (1403). شناسایی و اولویت‌بندی ریسک‌های کسب و کار واحدهای تولیدی طیور. سامانه مدیریت نشریات علمی, 38(1), 33-52. doi: 10.22067/jead.2024.84685.1222
ملیحه شیبانی نوقابی; علیرضا کرباسی; حسین محمدی. "شناسایی و اولویت‌بندی ریسک‌های کسب و کار واحدهای تولیدی طیور". سامانه مدیریت نشریات علمی, 38, 1, 1403, 33-52. doi: 10.22067/jead.2024.84685.1222
شیبانی نوقابی, ملیحه, کرباسی, علیرضا, محمدی, حسین. (1403). 'شناسایی و اولویت‌بندی ریسک‌های کسب و کار واحدهای تولیدی طیور', سامانه مدیریت نشریات علمی, 38(1), pp. 33-52. doi: 10.22067/jead.2024.84685.1222
شیبانی نوقابی, ملیحه, کرباسی, علیرضا, محمدی, حسین. شناسایی و اولویت‌بندی ریسک‌های کسب و کار واحدهای تولیدی طیور. سامانه مدیریت نشریات علمی, 1403; 38(1): 33-52. doi: 10.22067/jead.2024.84685.1222

شناسایی و اولویت‌بندی ریسک‌های کسب و کار واحدهای تولیدی طیور

اقتصاد و توسعه کشاورزی
مقاله 3، دوره 38، شماره 1 - شماره پیاپی 62، فروردین 1403، صفحه 33-52    اصل مقاله (839.91 K)
نوع مقاله: مقالات پژوهشی
شناسه دیجیتال (DOI): 10.22067/jead.2024.84685.1222
نویسندگان
ملیحه شیبانی نوقابی؛ علیرضا کرباسی* ؛ حسین محمدی
گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه فردوسی مشهد، ایران
چکیده
امروزه کسب­‌و­کارهای صنعت طیور با چالش‌های متعددی رو به رو می‌باشند؛ زیرا کسب­‌و­کارهای این صنعت، تعدادی از فرآیندها، شیوه‌ها و ریسک‌های منحصر به فرد را باید همزمان مدیریت نمایند. بنابراین، ﺷﻨﺎﺳﺎﯾﯽ رﯾﺴﮏﻫﺎی کسب‌ و‌کار واحدهای تولیدی طیور، ﻣﯽﺗﻮاﻧﺪ ﻧﻘﺶ اﺛﺮﮔﺬاری در ﮐﺎﻫﺶ ﻣﯿﺰان آﺳﯿﺐﭘﺬﯾﺮی این کسب و کارها اﯾﻔﺎء نماید. با توجه به لـزوم افزایش بهـره‌وری صنعت طیور، یکـی از راهکارهای اساسـی، شناسایی ریسک و اندازه‌گیری ریسک‌های موجود این صنعت می‌باشد. شناسایی و کمی‌سازی ریسک می‌تواند هزینه‌ها را برای ذینفعان این صنعت کاهش دهد و کاهش ریسک منجر به برنامه‌ریزی بهتر برای تولید می‌شود. در این راستا، این مطالعه به شناسایی ریسک‌های کسب و کار واحدهای تولیدی طیور استان خراسان رضوی پرداخته شده است و از نظر هدف، کاربردی‌ و از حیث‌ ماهیت‌ و روش، توصیفی‌ پیمایشی‌ و بر پایه پژوهش‌های آمیخته، به‌‌‌‌صورت کیفی و کمی انجام شده است. جامعه آماری مطالعه، خبرگان صنعت طیور می‌باشند که 18 نفر با روش نمونه‌گیری گلوله برفی به‌عنوان نمونه پژوهش مورد بررسی قرار گرفتند. نتایج روش دلفی فازی پنج ضلعی نشان داد که پنج ریسک اصلی و 36 ریسک فرعی از 58 ریسک شناسایی شده جزء ریسک‌های کسب و کار واحدهای تولیدی طیور می‌باشند. همچنین نتایج نشان داد که نوسانات قیمت نهاده‌های دامی، قیمت‌گذاری دستوری، نوسانات نرخ ارز، تحریم‌ها، نوسانات قیمت مرغ و تأخیر در دسترسی به نهاده‌ها جزء مهم‌ترین ریسک‌های شناسایی شده می‌باشند. با توجه به نوسانات قیمت نهاده‌های دامی و نوسانات نرخ ارز پیشنهاد می‌شود به تخصیص ارز و کنترل آن توسط سیاست‌های دولت در جهت کاهش نوسانات مذکور اقدام شود و یا به سمت متنوع نمودن مواد خوراکی طیور و فرمول‌بندی جیره جدید خوراک طیور پیش رفت. همچنین برای جلوگیری از نوسانات قیمت مرغ و یا تخم مرغ، پیشنهاد می‌شود که خرید قراردادی این محصولات توسط شرکت پشتیبانی امور دام با نرخ مصوب انجام گیرد و یا هوشمندسازی شبکه توزیع برای جلوگیری از این نوسانات انجام شود. در بازار طیور بهتر است برای هر کیلوگرم مرغ تنها یک قیمت عادلانه با توجه به هزینه‌های تولید مرغداران تعیین شود و یا دخالت دولت در بازار نباشد و دولت اجازه دهد قیمت براساس مکانیزم تقاضا و عرضه صورت گیرد.
کلیدواژه‌ها
دلفی فازی پنج ضلعی؛ شناسایی؛ صنعت طیور؛ مدیریت ریسک
مراجع
  1. Abimbola, O.A., Omowunmi, A.T., & Abayomi, S.O. (2013). Risk coping behaviour of small scale poultry farmers in Ogun State, Nigeria. Asian Journal of Animal and Veterinary Advances, 8(6), 786-795.
  2. Adelaja, A., & George, J. (2019). Effects of conflict on agriculture: evidence from the Boko Haram Insurgency. World Development, 117, 184–195.
  3. Adeyonu, A.G., Otunaiya, A.O., Oyawoye, E.O., & Okeniyi, F.A. (2021). Risk perceptions and risk management strategies among poultry farmers in south-west Nigeria. Cogent Social Sciences, 7(1), https://doi.org/10.1080/23311886.2021.1891719
  4. Agricultural statistics. (2022). The second volume. Ministry of agricultural, planning and economic deputy, information and communication technology center. (In Persian). Avaiable at: https://www.maj.ir/Index.aspx?page_=form&lang=1&PageID=11583&tempname=amar&sub=65&methodName=ShowModuleContent
  5. Aimin, H. (2010). Uncertainty, risk aversion and risk management in agricultural. Journal of Agriculture and Science Procedia, 1, 152-156.
  6. Ansari zadeh, A., Baversad, B., & Ahangari, A. (2009). Cooperative and private poultry enterprises in Ramhormoz Township: A case study. Co-Operation and Agriculture, 20(206-207), 95-109. (In Persian)
  7. Arabsalehi, M., Moayedfar, R., & Safari Bideskan, S. (2012). The effect of environment risk, corporate strategy and capital structure on performance of listed companies in Tehran stock exchange. Financial Accounting Research, 4(3), 47-70. (In Persian)
  8. Badraoui, I., Van der Vorst, J.G., & Boulaksil, Y. (2020). Horizontal logistics collaboration: An exploratory study in Morocco’s agri-food supply chains. International Journal of Logistics Research and Applications, 23(1), 85–102.
  9. Banjoko, I.K., Falola, A., Babatunde, F.B., & Atolagbe, R. (2014). Assessment of risks and uncertainties in poultry farming in Kwara State, Nigeria. Science Technology and Arts Research Journal, 3(4), 64-70.
  10. Belhadi, A., Kamble, S.S., Mani, V., Benkhati, I., & Touriki, F.E. (2021). An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance. Annals of Operations Research, https://doi.org/10.1007/s10479-021-04366-9
  11. Beykzadeh, S., Ghahremanzadeh, M., & Mahmoodi, A. (2020). The evaluation of price volatility of beef and chicken and livestock’s major inputs in Iran. Journal of Animal Science Research, 30(3), 85-103. (In Persian)
  12. Blackhurst, J., Scheibe, K., & Johnson, D. (2008). Supplier risk assessment and monitoring for automotive industry. International Journal of Physical Distribution & Logistics Management, 38, 143-165.
  13. Blos, M.F., Hoeflich, S.L., Dias, E.M., & Wee, H.-M. (2015). A note on supply chain risk classification: discussion and proposal. International Journal of Production Research, 54(5), 1568-1569.
  14. Cavinato, J.L. (2004). Supply chain logistics risks: From the back room to the board room. International Journal of Physical Distribution & Logistics Management, 34(5), 383-387.
  15. Chakraborty, A., Mondal, S.P., Alam, S., Ahmadian, A., Senu, N., De, D., & Salahshour, S. (2019). The pentagonal fuzzy number: its different representations, properties, ranking, defuzzification and application in game problems. Symmetry, 11(2), 248.
  16. Chandrasekaran, N., & Raghuram, G. (2014). Agribusiness supply chain management. CRC Press.
  17. Chen, C.-W., Wang, J.-H., Wang, J.C., & Shen, Z.-H. (2018). Developing indicators for sustainable campuses in Taiwan using Fuzzy Delphi Method and analytic hierarchy process. Journal of Cleaner Production, 193, 661–671.
  18. Dias, G.C., Hernandez, C.T., & Oliveira, U.R. (2020). Supply chain risk management and risk ranking in the automotive industry. Gestão & Produção, 27(1), e3800. https://doi.org/10.1590/0104-530X3800-20
  19. Ebong, V.O., & Awatt, N.K. (2023). Analysis of risk management in poultry production enterprises in Akwa Ibom State. International Journal of Innovative Agriculture & Biology Research, 11(1), 49-59.
  20. )2022(. Production quantities of Meat, chicken by country 2022. Accessed via https://www.fao.org/faostat/en/#data/QCL/visualize
  21. (2022). Available at: https://www.fao.org/faostat/en/#compare
  22. Fathi, F., & Ghorbanian, E. (2021). Risk management of Iran’s corn import. Journal of Agricultural Economics and Development, 35(2), 179-191. (In Persian with English abstract). https://doi.org/10.22067/jead.2021.69209.1027
  23. Food & Agriculture Organization. (2019). Meat market Review. Overview of global meat market developments in 2018.
  24. Gava, O., Bartolini, F., Brunori, G., & Galli, F. (2014). Sustainability of local versus global bread supply chains: a literature review. (eds). Proceedings of Conference “Feeding the Planet and Greening Agriculture: Challenges and opportunities for the bio-economy.25-27 June, 2014 Alghero, Italy.
  25. Ghahremanzadeh, M., Faraji, S., & Pishbahar, E. (2020). The transmission world price and exchange rate to domestic prices of livestock’s major imported inputs in Iran. Agricultural Economics, 14(2), 23-52. (In Persian). https://doi.org/10.22034/iaes.2020.134731.1780
  26. Gray, R.S. (2020). Agriculture, transportation, and the COVID-19 Crisis. Canadian Journal of Agricultural Economics/Revue Canadienne D’agroeconomie, 68(2), 239-243. https://doi.org/10.1111/cjag.12235
  27. Greening, S.S., Mulqueen, K., Rawdon, T.G., French, N.P., & Gates, M.C. (2020). Estimating the level of disease risk and biosecurity on commercial poultry farms in New Zealand. New Zealand Veterinary Journal, 68(5), 261-271.
  28. Gunduz, M., & Elsherbeny, H.A. (2020). Operational framework for managing construction- contract administration practitioners’ perspective through modified Delphi method. Journal of Construction Engineering and Management, 146(3), 04019110. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001768
  29. Guo, Y. (2011). Research on knowledge-oriented supply chain risk management system model. Journal of Management and Strategy, 2(2), 72-77.
  30. Hashemi Nejad, A., Abdeshahi, A., Ghanian, M., & Khosravipour, B. (2020). Analyzing factors affecting wheat production risk in Iran. Journal of Agricultural Economics and Development, 33(4), 329-338. (In Persian with English abstract). https://doi.org/10.22067/jead2.v33i3.66850
  31. Hasheminezhad, A., Ghanian, M., Abdeshahi, A., & Khosravipour, B. (2018). Assessment of wheat production related risks in the bread supply chain of Khuzestan Province. Iranian Journal of Agricultural Economics and Development Research, 49(3), 439-459. (In Persian). https://doi.org/10.22059/ijaedr.2018.239968.668477
  32. Hasheminezhad, A., Ghanian, M., Abdeshahi, A., & Khosravipour, B. (2021). Application of bread supply chain framework to explain risk management strategies of bread production and consumption in Khuzestan province. Journal of Food Processing and Preservation, 12(2), 99-114. (In Persian). https://doi.org/10.22069/ejfpp.2021.16838.1557
  33. Hossein Zad, J., & Hasanzadeh Honarvar, F. (2016). Impact of exchange rate changes on prices and consumption of main inputs under the livestock and pultry sector. Master Thesis of Agricultural Economics, Faculty of Agricultural, University of Tabriz. (In Persian)
  34. Hossein Zad, J., & Rashid Ghalam, M. (2017). Exchange rates impacts on poultry husbandry inputs prices. Iranian Journal of Agricultural Economics and Development Research, 48(1), 1-8. (In Persian)
  35. Hsu, Y.-L., Lee, C.-H., & Kreng, V.B. (2010). The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Applications, 37(1), 419–425.
  36. Hudnurkar, M., Deshpande, S., & Rathod, U., & Jakhar, S. (2017). Supply chain risk classification schemes: A literature review. Operations and Supply Chain Management: An International Journal, 10(4), 182-199.
  37. Iheke, O.R., & Igbelina, C.A. (2016). Risks management in poultry production in ikeduru local government area of imo state, Nigeria. Nigerian Journal of Agriculture, Food and Environment, 12(1), 67-74.
  38. Jalali, M. (2020). Estimation of import demand function of main livestock inputs and the effect of embargo on it, Second Accounting and Management Conference. (In Persian)
  39. Javdan, E., Rajabi, E., & Baghestany, A.A. (2023). Exchange rate pass-through to the price of Imported Soybean Meal and Maize. Agricultural Economics and Development. (In Persian). https://doi.org/10.30490/aead.2023.359780.1459
  40. Kaminskyi, A., & Nehrey, M. (2019). Investment risk measurement for agricultural ETF. Advances in Economics, Business and Management Research, 95, 6th International Conference on Strategies, Models and Technologies of Economic Systems Management.
  41. Karami, A.A., & Mohammadi Tamari, Z. (2017). Identifying and prioritizing supply chain’s risks in agricultural farms in Mazandaran Province. Agricultural Economics, 11(3), 1-24. (In Persian). https://doi.org/10.22034/iaes.2017.26476
  42. Kern, D., Moser, R., Hartmann, E., & Moder, M. (2012). Supply risk management: model development and empirical analysis. International Journal of Physical Distribution & Logistics Management, 42(1), 60-82.
  43. Krishnan, R., Agarwal, R., Bajada, C., & Arshinder, K. (2020). Redesigning a food supply chain for environmental sustainability– an analysis of resource use and recovery. Journal of Cleaner Production, 242. https://doi.org/10.1016/j.jclepro.2019.118374
  44. Li, T. (2012). Risk assessment in the supply chain management based on Fuzzy AHP model. Progress in Applied Mathematics, 4(1), 9-13.
  45. Mahdiyar, A., Mohandes, S.R., Durdyev, S., Tabatabaee, S., & Ismail, S. (2020). Barriers to green roof installation: An integrated fuzzy-based MCDM approach. Journal of Cleaner Production, 269, 122365. https://doi.org/10.1016/j.jclepro.2020.122365
  46. Mahdiyar, A., Tabatabaee, S., Abdullah, A., & Marto, A. (2018). Identifying and assessing the critical criteria affecting decision-making for green roof type selection. Sustainable Cities and Society, 39, 772–783.
  47. Manuj, I., & Mentzer, J.T. (2008). Global supply chain risk management strategies. International Journal of Physical Distribution & Logistics Management, 38(3), 192-223.
  48. Mary, A.A., & Sangeetha, S. (2016). Application of Fuzzy Linguistic SAW and TOPSIS multiple criteria group decision making method using Pentagonal Fuzzy Number for supplier selection International Journal of Mathematics and its Applications, 55, 7.
  49. Miri, M., Sharifzadeh, M., Abdullahzadeh, G., & Abedi Sarostani, A. (2017). Investigating the supply chain in the agricultural sector (case study: production and cultivation of strawberries in Ramyan city, Golestan province). Journal of Studies in Entrepreneurship and Sustainable Agricultural Development, 4(3), 89-104. (In Persian). https://doi.org/10.22069/jead.2017.13541.1275
  50. Mohandes, S.R., & Zhang, X. (2019). Towards the development of a comprehensive hybrid fuzzy-based occupational risk assessment model for construction workers. Safety Science, 115(6), 294–309.
  51. Mohandes, S.R., Sadeghi, H., Fazeli, A., Mahdiyar, A., Hosseini, M. R., Arashpour, M., & Zayed, T. (2022). Causal analysis of accidents on construction sites: A hybrid Fuzzy Delphi and DEMATEL approach. Safety Science, 151(10). https://doi.org/1016/j.ssci.2022.105730
  52. Mortezaei, A. (2016). Identifying and categorizing the obstacles and challenges of production and competitiveness in food chain enterprises and evaluating the law for removing obstacles to competitive production and improving the country's financial system. Tehran Chamber of Commerce, Industries, Mines and Agriculture. (In Persian)
  53. Moslehi, H.R. (2020). A collection of world experience publications in agriculture and natural resources; comparing the situation of chicken production in Iran with other countries. Deputy of Agricultural Education and Promotion, Deputy of Science and Technology. (In Persian).
  54. Murrja, A., Ndreca, P., Maloku, S., & Meço, M. (2023). Analysis of production risk in intensive chicken farms – The case of Kosovo. Folia Oeconomica Stetinensia, 3(2), 294-310.
  55. Murrja, A., Ndregjoni, A., Kapaj, I., Maloku, S., & Kapaj, A. (2022). Financial risk analysis in the intensive poultry growth in the republic of Kosovo. International Journal of Economics and Finance Studies, 14(3), 366-387.
  56. Mustafavi, S.M. (2012). Challenges of Iran's poultry industry and solutions to deal with them. Deputy of Economic Research, strategic report: 148. Report code: 04-8-91-10. (In Persian)
  57. Norrman, A., & Jansson, U. (2004). Ericsson’s proactive supply chain risk management approach after a serious sub‐ supplier accident. International Journal of Physical Distribution & Logistics Management, 34(5), 434-456.
  58. Obike, K.C., Amusa, T.A., & Olowolafe, H.B. (2017). Risk management and determinants of farm output among small scale poultry farmers in Ekiti State, Nigeria. Journal of Tropical Agriculture, Food, Environment and Extension, 16(2), 9-16.
  59. Panda, A., & Pal, M. (2015). A study on pentagonal fuzzy number and its corresponding matrices. Pacific Science Review B: Humanities and Social Sciences, 1(3), 131–139.
  60. Pathinathan, T., & Mike Dison, E. (2018). Defuzzification for Pentagonal fuzzy numbers. International Journal of Current Advanced Research, 7(1), 86-90.
  61. Pfohl, H.C., Köhler, H., & Thomas, D. (2010). State of the art in supply chain risk management research: empirical and conceptual findings and a roadmap for the implementation in practice. Logistics Research, 2(1), 33-44.
  62. Pishbahar, E., Abdolkarimsaleh, K., & Dashti, G. (2016). Calculate the optimal hedge ratio for corn imported input of Iran poultry industry. Journal of Animal Science Research, 26(1), 167-174. (In Persian)
  63. Pourmokhtar, E., Moghaddasi, R., Mohammadi Nejad, A., & Hosseini, S.S. (2022). Application of Quantile regression in the analysis of the fluctuations in the price of chicken meat in Iran. Agricultural Economics Research, 13(4), 175-191. (In Persian). https://doi.org/10.30495/jae.2021.14884.1761
  64. Purwaningsih, R., Arief, A., Handayani, N.U., Rahmawati, D., & Mustikasari, A. (2018). Market risk assessment on poultry industry using Monte Carlo simulation. IOP Conference Series: Materials Science and Engineering,  https://doi.org/10.1088/1757-899X/403/1/012044
  65. Rahmani, R., & Torkamani, J. (2010). The impacts of price and output uncertainty on chicken and beef meats in Fars Province. Agricultural Economics, 4(1), 51-79. (In Persian)
  66. Rangel, D.A., De Oliveira, T.K., & Leite, M.S.A. (2014). Supply chain risk classification: discussion and proposal. International Journal of Production Research, 53(22), 6868-6887.
  67. Salami, H., Ghahremanzadeh, M., Hosseini, S.S., & Yazdani, S. (2010). Revenue insurance, a policy tool for reducing production risk and price fluctuation in broiler production sector. Agricultural Economics, 3(4), 1-30. (In Persian)
  68. Sepahpanah, M., Yaghoubifarani, A., & Mohammadi, Y. (2020). A study on agribusiness supply chain risk vulnerability (greenhouse owners in Hamadan Province). Iranian Journal of Agricultural Economics and Development Research, 51(1), 109-131. (In Persian). https://doi.org/10.22059/ijaedr.2019.282587.668766
  69. Shah, S.A.A., Solangi, Y.A., & Ikram, M. (2019). Analysis of barriers to the adoption of cleaner energy technologies in Pakistan using Modified Delphi and Fuzzy Analytical Hierarchy Process. Journal of Cleaner Production, 235, 1037–1050.
  70. Shahraki, A., Ghorbani, M., & Asgharpour Masouleh, A. (2021). Integrating risk assessment and management and performance measurement in agricultural supply chain using agent-based simulation approach (A Case Study). Agricultural Economics, 15(3), 21-54. (In Persian). https://doi.org/10.22034/iaes.2021.534404.1851
  71. Taheri Reykandeh, E., & Rafiee, H. (2024). Modeling return and volatility spillovers between the Inputs market of Livestock and Poultry Industry in Iran. Agricultural Economics and Development. (In Persian). https://doi.org/10.30490/aead.2024.356654.1396
  72. Tummala, R., & Schoenherr, T. (2011). Assessing and managing risks using the Supply Chain Risk Management Process (SCRMP). Supply Chain Management: An International Journal, 16(6), 474-483.
  73. Vajdi, F., Ghahremanzadeh, M., & Hosseinzad, J. (2018). Risk spillover effect of exchange rate on chicken market and its major inputs in Iran. Journal of Agricultural Economics and Development, 32(3), 213-225. (In Persian). https://doi.org/10.22067/jead2.v32i3.70821
  74. Yan, X., Hui, S., & Wangmei, Y. )2009(. Research on the source and management of supply chain risk. Logistics Engineering and Management, 31(4), 58-61.
  75. Zaghari, M., Honarbakhsh, S., Charkhkar, S., & Safari-asl, R. (2016). Determination of parameters for ranking the mortality risk in poultry production farms for poultry insurance. Journal of Veterinary Research, 71(3), 335-350. (In Persian). https://doi.org/10.22059/jvr.2016.58741
  76. Zaporozhtseva, L.A., Sabetova, T.V., & Tkacheva, J.V. (2018). Developing and testing model for investment risk assessment in agriculture. Advances in Engineering Research, 151, International Conference on Smart Solutions for Agriculture.

 

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