شناسایی عوامل تاب آوری زنجیره تامین دارو با استفاده از روش دلفی فازی و ارائه مدل ریاضی جهت تخصیص بهینه سفارش
محورهای موضوعی :
مدیریت صنعتی
batool askaryan
1
(Department of Industrial Management،Centeral Tehran Branch,Islamic Azad University,Tehran,Iran)
mohammad ebrahim pourzarandi.
2
(Department of industrial Management,Central Tehran Branch,Islamic Azad University,Tehran,Iran (Corresponding Author),)
Jala Haghighat monfared
3
(گروه مدیریت صنعتی،واحد تهران مرکزی ،دانشگاه آزاد اسلامی،تهران،ایران (نویسنده مسؤول))
تاریخ دریافت : 1400/08/21
تاریخ پذیرش : 1401/03/05
تاریخ انتشار : 1401/04/01
کلید واژه:
دلفی فازی,
زنجیره تأمین,
دیمتل فازی,
چکیده مقاله :
اختلالات در زنجیره تامین دارو از سال 2011 تا کنون باعث کمبود صدها دارو شده است که از نظر پزشکی ضروری است هنگامی که یک اختلال رخ می دهد صنعت از نظر توانایی سازگاری محدود می شود لذا بهبود تصمیمات تاب آوری استراتژیک برای جلوگیری از کمبود های آینده مهم است ،دسترسی به دارو به عنوان یک حق انسانی یکی از اهداف سیستم های بهداشت و درمان است لذا زنجیره تامین دارو باید دارو را به مقدار و در زمان مناسب و کیفیت قابل قبول و هزینه بهینه فراهم کند،لدا هرگونه ریسک در زنجیره تامین دارو نه تنها میتواند منابع را هدر دهد بلکه می تواند زندگی بیماران را تهدید کند. در چنین شرایطی، مفهوم تاب‌آوری بروز یافته که نشان‌دهنده‌ی توانایی سازمان برای رویارویی با موارد غیرقابل‌پیش‌بینی است و اگر سازمان بتواند تأمین‌کننده با تاب‌آوری مناسب را شناسایی کند. سازمان مقاومت بیشتری در مقابل اختلالات دارد. لذا در این مقاله انتخاب تأمین‌کننده در زنجیره تأمین تاب آور مطرح گردیده است ابتدا با استفاده از مزالعات کتابخانه ای و روش دلفی عوامل تاب آوی صنعت دارو شناسایی شد سپس بر اساس این عوامل با استفاده از روش تحلیل شبکه تامین کنندگان رتبع بندی شدند و در نهایت مدل ریاضی جهت تخصیص بهینه سفارش به تامین کنندگان ارائه شد و- تقسیم بندی تامین کنندگان بعه استراتژیک،پشتیبان،جایگزین2-ایجاد منبع پشتیبان3-شناسایی جمعیتی که تحت تاثیر کمبود دارو هستند4-تقسیم بندی تامین کنندگان بر اساس ریسک بالا،متوسط و پایین5-افزایش شفافیت زنجیره تامین برای افزایش دانش مدیران برای درک تغییرات لازم می باشد.
چکیده انگلیسی:
The supply chain disruptions have caused hundreds of drug shortages since 2011, when a disruption occurs because the supply chain needs to be limited in terms of ability to adapt, so the supply chain needs to provide medicine to the amount and at the right time and acceptable quality, but any risk in the supply chain can not only waste resources but also threaten the lives of patients. In such situations, the perceived notion of an organization reflects the organization's ability to face unpredictable situations and if the organization can identify the supplier with suitable stakeholders. The organization has more resistance to disorders. so, in this paper, supplier selection in the تاب supply chain was introduced, first using library analysis and delphi method, the کنندگان factors were identified, and in the end, the mathematical model for optimal allocation of order to suppliers has been identified and then the mathematical model for the optimal allocation of order to suppliers has been developed, and the supply chain transparency is increased to increase managers ' knowledge to understand the necessary changes.
منابع و مأخذ:
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Szmelter-Jarosz, A. (2019). DEMATEL Method in Supplier Evaluation and Selection. Transport Economics and Logistics, 82, 129-142. https://doi.org/10.26881/etil.2019.82.11
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Wang, T.-K., Zhang, Q., Chong, H.-Y., & Wang, X. (2017). Integrated supplier selection framework in a resilient construction supply chain: An approach via analytic hierarchy process (AHP) and grey relational analysis (GRA). Sustainability, 9(2), 289.
Wilson, Prentice D. (2000). Managing a global supply chain whit durable arm’s-length supplier relationship. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000.
Zare, S., Shirvan, H. E., Hemmatjo, R., Faridan, M., Hajghani, M., & Dehaghi, B. F. (2018). Using the analytic network process method for prioritizing and weighing shift work disorders among the personnel of hospitals of Kerman University of Medical Sciences. Journal of circadian rhythms, 16.
Piprani, A. Z., Mohezar, S., & Jaafar, N. I. (2020). Supply chain integration and supply chain performance: The mediating role of supply chain resilience. Int J Supply Chain Manage, 9, 58-73.
Davoudabadi, R., Mousavi, S. M., & Sharifi, E. (2020). An integrated weighting and ranking model based on entropy, DEA and PCA considering two aggregation approaches for resilient supplier selection problem. Journal of Computational Science, 40, 101074.
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Azadeh, A., Abdollahi, M., Farahani, M. H., & Soufi, H. R. (2014). Green-resilient supplier selection: an integrated approach. In International IEEE Conference, Istanbul. july 26(Vol. 28).
Yusoff, M., Fkrudin, A., Hashim, A., Muhamadm, N., Hamat, W., & Norina, W. (2021). Application of Fuzzy Delphi Technique to Identify the Elements for Designing and Developing the e-PBM PI-Poli Module. Asian Journal of University Education, 17(1), 292-304.
Haghighat, A. (2017). Presenting Prioterizing Model of Factors Affecting On Open Innovation by Using Dematel Method. Roshd-E-Fanavari, 13(51), 8-15. https://www.sid.ir/en/journal/ViewPaper.aspx?id=551460
Hosseini, S., & Barker, K. (2016). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics, 180, 68-87.
Tsai, H. C., Lee, A. S., Lee, H. N., Chen, C. N., & Liu, Y. C. (2020). An application of the fuzzy Delphi method and fuzzy AHP on the discussion of training indicators for the regional competition, Taiwan national skills competition, in the trade of joinery. Sustainability, 12(10), 4290.
Kamalahmadi, M., & Mellat-Parast, M. (2016). Developing a resilient supply chain through supplier flexibility and reliability assessment. International Journal of Production Research, 54(1), 302-321.
Khaki, GH. (2013). Research Methodology (dissertation writing approach). Tehran, Fozhan publishing.
Khedry, H., Jamali, G., Ghorbanpour, A. (2020). A Mixed Approach for Evaluation Preventive Maintenance Performance Based on Anti-Fragility Factors. Journal of Production and Operations Management, 11(3), 73-94. doi: 10.22108/jpom.2021.124605.1287
Karimmian, Z., Ghodsypour, S. H., & Gheidar-Kheljani, J. (2018). Supplier Selection Problem Considering Relationships between Suppliers and Supply Disruption Risk in complex products. Journal of Production and Operations Management, 8(2), 135-150.
Lima Junior, F. R., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 21,194-209.
Namdar, J., Li, X., Sawhney, R., & Pradhan, N. (2018). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research, 56(6), 2339-2360.
Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework. Journal of business logistics, 31(1), 1-21.
Pramanik, D., Haldar, A., Mondal, S. C., Naskar, S. K., & Ray, A. (2017). Resilient supplier selection using AHP-TOPSIS-QFD under a fuzzy environment. International Journal of Management Science and Engineering Management, 12(1), 45-54.
Qian, L. (2014). Market-based supplier selection with price, delivery time, and service level dependent demand. International Journal of Production Economics, 147, 697-706.
Shirinfar, M., & Haleh, H. (2011). Supplier Selection and Evaluation by Fuzzy Multi-Criteria Decision Making Methodology. IUST, 22(4), 271-280. http://ijiepr/iust/ac/ir/article-1-370-en/html. 17.
Szmelter-Jarosz, A. (2019). DEMATEL Method in Supplier Evaluation and Selection. Transport Economics and Logistics, 82, 129-142. https://doi.org/10.26881/etil.2019.82.11
Saaty, T.L. (1970). The Analytical Hierarchy Process. Mcgraw Hill, New York.
Torabi, S., M. Baghersad, & S. Mansouri. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review.79, 22-48.
Tavana, M/, Kaviani, M. A., Di Caprio, D., & Rahpeyma, B. (2016). A two-stage data envelopment analysis model for measuring performance in three-level supply chains. Measurement, 78, 322-333.
Vecchi, A & Valisi, V. (2021). Supply chain resilience. Retrieved at https://www.thebci.org/static/e02a3e5f-82e5-4ff1-b8bc61de9657e9c8/BCI-0007h-Supply-Chain-Resilience-ReportLow-Singles.pdf.
Wang, T.-K., Zhang, Q., Chong, H.-Y., & Wang, X. (2017). Integrated supplier selection framework in a resilient construction supply chain: An approach via analytic hierarchy process (AHP) and grey relational analysis (GRA). Sustainability, 9(2), 289.
Wilson, Prentice D. (2000). Managing a global supply chain whit durable arm’s-length supplier relationship. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000.
Zare, S., Shirvan, H. E., Hemmatjo, R., Faridan, M., Hajghani, M., & Dehaghi, B. F. (2018). Using the analytic network process method for prioritizing and weighing shift work disorders among the personnel of hospitals of Kerman University of Medical Sciences. Journal of circadian rhythms, 16.
Piprani, A. Z., Mohezar, S., & Jaafar, N. I. (2020). Supply chain integration and supply chain performance: The mediating role of supply chain resilience. Int J Supply Chain Manage, 9, 58-73.
Davoudabadi, R., Mousavi, S. M., & Sharifi, E. (2020). An integrated weighting and ranking model based on entropy, DEA and PCA considering two aggregation approaches for resilient supplier selection problem. Journal of Computational Science, 40, 101074.
