Risk assessment for pharmaceutical industry in uncertain environment: An integrated multi-criteria decision-making approach
DOI:
https://doi.org/10.31181/dmame622023688Keywords:
Pharmaceutical industry, IF-AHP, Risk assessment, COVID-19, Healthcare, DelphiAbstract
The pharmaceutical industry is the backbone of the healthcare system for any country. However, this industry faces various risks, which hamper its efficient working in providing life-saving medicines/services to the people. In this context, the purpose of the study is to improve the resilience and performance of the pharmaceutical industry (PI) with the identification, and assessment of supply chain (SC) risks. A case illustration has also been presented in the Indian context. The study utilizes an extensive literature survey and the Delphi method for identifying, finalizing, and classifying the risks into seven categories. The Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to analyze and prioritize the risks to determine their criticality. The results show that the three most important risks are financial, supplier, and demand/customer/market. Within these risks, the three most critical sub-risks are found to be loss of customers, raw material (RM) issues, and bad reputation of the company, respectively. The study provides managers with an extensive list of PI risks for their consideration. The results also present the critical risks which need to be mitigated for enhanced performance and resilience of the industry. The study also emphasizes the importance of interconnection between various SC partners for better risk management.
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Adabre, M. A., Chan, A. P. C., Edwards, D. J., & Osei-Kyei, R. (2022). To build or not to build, that is the uncertainty: Fuzzy synthetic evaluation of risks for sustainable housing in developing economies. Cities, 125, 103644. https://doi.org/10.1016/j.cities.2022.103644
Afzali, M., Afzali, A., & Pourmohammadi, H. (2022). An interval-valued intuitionistic fuzzy-based CODAS for sustainable supplier selection. Soft Computing, 26(24), 13527–13541. https://doi.org/10.1007/s00500-022-07471-4
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
Ayyildiz, E. (2021). Interval valued intuitionistic fuzzy analytic hierarchy process-based green supply chain resilience evaluation methodology in post COVID-19 era. Environmental Science and Pollution Research, 30(15), 42476–42494. https://doi.org/10.1007/s11356-021-16972-y
Bagozzi, D., & Lindmeier, C. (2017). 1 in 10 medical products in developing countries is substandard or falsified. World Health Organization. https://www.who.int/news/item/28-11-2017-1-in-10-medical-products-in-developing-countries-is-substandard-or-falsified
Bartfai, T., & Lees, G. V. (2013). Why is pharma a special industry? The Future of Drug Discovery, 193–216. https://doi.org/10.1016/b978-0-12-407180-3.00007-6
Bignami, F., & Mattsson, P. (2019). Potential effects of increased openness in pharma: the original knowledge behind new drugs. Drug Discovery Today, 24(10), 1957–1962. https://doi.org/10.1016/j.drudis.2019.06.015
Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363–11368. https://doi.org/10.1016/j.eswa.2009.03.039
Breen, L. (2008). A Preliminary Examination of Risk in the Pharmaceutical Supply Chain (PSC) in the National Health Service (NHS). Journal of Service Science and Management, 01(02), 193–199. https://doi.org/10.4236/jssm.2008.12020
British Medical Association. (2020). Which, if any, groups of medicines are you experiencing shortages of, compared with the situation pre-pandemic? [Graph]. In Statista. https://www.statista.com/statistics/1114746/medicine-shortages-during-covid-19-pandemic-in-the-uk/
Büyüközkan, G., Göçer, F., & Karabulut, Y. (2019). A new group decision making approach with IF AHP and IF VIKOR for selecting hazardous waste carriers. Measurement, 134, 66–82. https://doi.org/10.1016/j.measurement.2018.10.041
Büyüközkan, G., & Güleryüz, S. (2016). A new integrated intuitionistic fuzzy group decision making approach for product development partner selection. Computers & Industrial Engineering, 102, 383–395. https://doi.org/10.1016/j.cie.2016.05.038
Büyüközkan, G., Havle, C. A., & Feyzioğlu, O. (2020). A new digital service quality model and its strategic analysis in aviation industry using interval-valued intuitionistic fuzzy AHP. Journal of Air Transport Management, 86, 101817. https://doi.org/10.1016/j.jairtraman.2020.101817
Carlos, J., Gómez, O., & Torres, K. (2020). Operational Risk Management in the Pharmaceutical Supply Chain Using Ontologies and Fuzzy QFD. Procedia Manufacturing, 51, 1673–1679. https://doi.org/10.1016/j.promfg.2020.10.233
Cashless India. (2021). http://cashlessindia.gov.in/
Chaira, T. (2019). Fuzzy/Intuitionistic Fuzzy Set Theory. In Fuzzy Set and Its Extension (pp. 1–40). Wiley. https://doi.org/10.1002/9781119544203.ch1
Chen, X., Fang, Y., Chai, J., & Xu, Z. (2022). Does Intuitionistic Fuzzy Analytic Hierarchy Process Work Better Than Analytic Hierarchy Process? International Journal of Fuzzy Systems, 24(2), 909–924. https://doi.org/10.1007/s40815-021-01163-1
Cheraghali, A. M. (2013). Impacts of international sanctions on Iranian pharmaceutical market. DARU Journal of Pharmaceutical Sciences, 21(1), 64. https://doi.org/10.1186/2008-2231-21-64
Cherian, J. J., Rahi, M., Singh, S., Reddy, S. E., Gupta, Y. K., Katoch, V. M., Kumar, V., Selvaraj, S., Das, P., Gangakhedkar, R. R., Dinda, A. K., Sarkar, S., Vaghela, P. D., & Bhargava, B. (2021). India’s Road to Independence in Manufacturing Active Pharmaceutical Ingredients: Focus on Essential Medicines. Economies, 9(2), 71. https://doi.org/10.3390/economies9020071
Cowan, N. (2015). George Miller’s magical number of immediate memory in retrospect: Observations on the faltering progression of science. Psychological Review, 122(3), 536–541. https://doi.org/10.1037/a0039035
Dalkey, N., & Helmer, O. (1963). An Experimental Application of the Delphi Method to the Use of Experts Author ( s ): Management Science, 9(3), 458–467.
Dani, S. (2009). Supply Chain Risk. In Springer.
Demir, E., & Koca, G. (2021). Green Supplier Selection Using Intuitionistic Fuzzy AHP and TOPSIS Methods: A Case Study from the Paper Mills (pp. 666–673). https://doi.org/10.1007/978-3-030-51156-2_77
Dengler, S., Lahriri, S., Trunzer, E., & Vogel-Heuser, B. (2021). Applied machine learning for a zero defect tolerance system in the automated assembly of pharmaceutical devices. Decision Support Systems, 113540. https://doi.org/10.1016/j.dss.2021.113540
Dogan, O., Deveci, M., Canıtez, F., & Kahraman, C. (2020). A corridor selection for locating autonomous vehicles using an interval-valued intuitionistic fuzzy AHP and TOPSIS method. Soft Computing, 24(12), 8937–8953. https://doi.org/10.1007/s00500-019-04421-5
El Mokrini, A., Dafaoui, E., Berrado, A., & El Mhamedi, A. (2016). An approach to risk Assessment for Outsourcing Logistics: Case of Pharmaceutical Industry. IFAC-PapersOnLine, 49(12), 1239–1244. https://doi.org/10.1016/j.ifacol.2016.07.681
El Mokrini, A., Kafa, N., Dafaoui, E., El Mhamedi, A., & Berrado, A. (2016). Evaluating outsourcing risks in the pharmaceutical supply chain: Case of a multi-criteria combined fuzzy AHP-PROMETHEE approach. IFAC-PapersOnLine, 49(28), 114–119. https://doi.org/10.1016/j.ifacol.2016.11.020
Elleuch, H., Hachicha, W., & Chabchoub, H. (2014). A combined approach for supply chain risk management: description and application to a real hospital pharmaceutical case study. Journal of Risk Research, 17(5), 641–663. https://doi.org/10.1080/13669877.2013.815653
Enyinda, C I. (2018). Modeling enterprise risk management in operations and supply chain: A pharmaceutical firm context. Operations and Supply Chain Management, 11(1), 1–12. https://www-scopus-com-iitr.new.knimbus.com/inward/record.uri?eid=2-s2.0-85056854252&partnerID=40&md5=155d2451c6d4c7c56623fff3aa75efb0
Enyinda, Chris I., Mbah, C. H. N., & Ogbuehi, A. (2010). An empirical analysis of risk mitigation in the pharmaceutical industry supply chain: A developing-country perspective. Thunderbird International Business Review, 52(1), 45–54. https://doi.org/10.1002/tie.20309
EvaluatePharma. (2020). World Preview 2020, Outlook to 2026. EvaluatePharma, June, 1–39.
Faghih-Roohi, S., Akcay, A., Zhang, Y., Shekarian, E., & de Jong, E. (2020). A group risk assessment approach for the selection of pharmaceutical product shipping lanes. International Journal of Production Economics, 229, 107774. https://doi.org/10.1016/j.ijpe.2020.107774
Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101–114. https://doi.org/10.1016/j.ijpe.2015.01.003
Far, T. S. (2019). Maximum Pressure: US economic sanctions harm Iranians’ right to health. https://www.hrw.org/report/2019/10/29/maximum-pressure/us-economic-sanctions-harm-iranians-right-health
Forghani, A., Sadjadi, S. J., & Farhang Moghadam, B. (2018). A supplier selection model in pharmaceutical supply chain using PCA, Z-TOPSIS and MILP: A case study. PLOS ONE, 13(8), e0201604. https://doi.org/10.1371/journal.pone.0201604
Fox, E. R., Sweet, B. V., & Jensen, V. (2014). Drug Shortages: A Complex Health Care Crisis. Mayo Clinic Proceedings, 89(3), 361–373. https://doi.org/10.1016/j.mayocp.2013.11.014
Gocer, F., & Sener, N. (2022). Spherical fuzzy extension of AHP‐ARAS methods integrated with modified k‐means clustering for logistics hub location problem. Expert Systems, 39(2). https://doi.org/10.1111/exsy.12886
Govindan, K., Diabat, A., & Madan Shankar, K. (2015). Analyzing the drivers of green manufacturing with fuzzy approach. Journal of Cleaner Production, 96, 182–193. https://doi.org/10.1016/j.jclepro.2014.02.054
Handfield, R. B., & McCormack, K. P. (2007). Supply Chain Risk Management: Minimizing Disruptions in Global Sourcing. CRC Press.
Hesarsorkh, A. H., Ashayeri, J., & Naeini, A. B. (2021). Pharmaceutical R&D project portfolio selection and scheduling under uncertainty: A robust possibilistic optimization approach. Computers & Industrial Engineering, 155, 107114. https://doi.org/10.1016/j.cie.2021.107114
Hirschhorn, F. (2019). Reflections on the application of the Delphi method: lessons from a case in public transport research. International Journal of Social Research Methodology, 22(3), 309–322. https://doi.org/10.1080/13645579.2018.1543841
Huq, F., Pawar, K. S., & Rogers, H. (2016). Supply chain configuration conundrum: how does the pharmaceutical industry mitigate disturbance factors? Production Planning and Control, 27(14), 1206–1220. https://doi.org/10.1080/09537287.2016.1193911
IBEF. (2019). Pharmaceuticals. In India Brand Equity Foundation. https://www.ibef.org/download/Pharmaceuticals-June-2019.pdf
IBEF. (2020). Indian Pharmaceuticals Industry Analysis. In Indian Brand Equity Foundation. https://www.ibef.org/archives/industry/indian-pharmaceuticals-industry-analysis-reports/indian-pharmaceuticals-industry-analysis-january-2020
IBEF. (2021). Pharmaceuticals. https://www.ibef.org/download/Pharmaceuticals-March-2021.pdf
Ilbahar, E., Kahraman, C., & Cebi, S. (2022). Risk assessment of renewable energy investments: A modified failure mode and effect analysis based on prospect theory and intuitionistic fuzzy AHP. Energy, 239, 121907. https://doi.org/10.1016/j.energy.2021.121907
IQVIA. (2020). Revenue of the worldwide pharmaceutical market from 2001 to 2019 (in billion U.S. dollars) [Graph]. In Statista. https://www.statista.com/statistics/263102/pharmaceutical-market-worldwide-revenue-since-2001/
Ismael, O. A., & Ahmed, M. I. (2020). Using Quality Risk Management in Pharmaceutical Industries: A Case Study. Calitatea, 21(178), 106–113.
Jaberidoost, M., Nikfar, S., Abdollahiasl, A., & Dinarvand, R. (2013). Pharmaceutical supply chain risks: a systematic review. DARU Journal of Pharmaceutical Sciences, 21(1), 69. https://doi.org/10.1186/2008-2231-21-69
Jaberidoost, M., Olfat, L., Hosseini, A., Kebriaeezadeh, A., Abdollahi, M., Alaeddini, M., & Dinarvand, R. (2015). Pharmaceutical supply chain risk assessment in Iran using analytic hierarchy process (AHP) and simple additive weighting (SAW) methods. Journal of Pharmaceutical Policy and Practice, 8(1), 1–10. https://doi.org/10.1186/s40545-015-0029-3
Johnston, R., Wolter, D., & Tataru, A. (2020). Commercial pharma forecasts are surprisingly inaccurate: Here are five ways to make them better. IQVIA. https://www.iqvia.com/blogs/2020/02/commercial-pharma-forecasts-are-surprisingly-inaccurate-here-are-5-ways-to-make-them-better
Kezar, A., & Maxey, D. (2016). The Delphi technique: an untapped approach of participatory research. International Journal of Social Research Methodology, 19(2), 143–160. https://doi.org/10.1080/13645579.2014.936737
Komazec, N., Mladenović, M., & Dabižljević, S. (2018). ETIOLOGY OF THE NOTION OF EVENT IN TERMS OF DECISION-MAKING AND DETERMINATION OF ORGANIZATIONAL SYSTEM RISK CONDITIONS. Decision Making: Applications in Management and Engineering, 1(1), 165–184. https://doi.org/10.31181/dmame1801165k
KT, R., & Sarmah, S. P. (2021). Impact of supply risk management on firm performance: a case of the Indian electronics industry. International Journal of Productivity and Performance Management, 70(6), 1419–1445. https://doi.org/10.1108/IJPPM-04-2019-0205
Kumar, A., Zavadskas, E. K., Mangla, S. K., Agrawal, V., Sharma, K., & Gupta, D. (2019). When risks need attention: adoption of green supply chain initiatives in the pharmaceutical industry. International Journal of Production Research, 57(11), 3554–3576. https://doi.org/10.1080/00207543.2018.1543969
Kutlu Gündoğdu, F., & Kahraman, C. (2020). A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft Computing, 24(6), 4607–4621. https://doi.org/10.1007/s00500-019-04222-w
Laínez, J. M. M., Schaefer, E., & Reklaitis, G. V. V. (2012). Challenges and opportunities in enterprise-wide optimization in the pharmaceutical industry. Computers & Chemical Engineering, 47, 19–28. https://doi.org/10.1016/j.compchemeng.2012.07.002
Lau, H., Tsang, Y. P., Nakandala, D., & Lee, C. K. M. (2021). Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology. Industrial Management & Data Systems, 121(7), 1684–1703. https://doi.org/10.1108/IMDS-04-2020-0199
Lawrence, J.-M., Ibne Hossain, N. U., Jaradat, R., & Hamilton, M. (2020). Leveraging a Bayesian network approach to model and analyze supplier vulnerability to severe weather risk: A case study of the U.S. pharmaceutical supply chain following Hurricane Maria. International Journal of Disaster Risk Reduction, 49, 101607. https://doi.org/10.1016/j.ijdrr.2020.101607
Liu, Y., Wang, S., Liu, Q., Liu, D., Yang, Y., Dan, Y., & Wu, W. (2022). Failure Risk Assessment of Coal Gasifier Based on the Integration of Bayesian Network and Trapezoidal Intuitionistic Fuzzy Number-Based Similarity Aggregation Method (TpIFN-SAM). Processes, 10(9), 1863. https://doi.org/10.3390/pr10091863
Lotfi, M., Asgharizadeh, E., Hisam Omar, A., Hosseinzadeh, M., & Amoozad Mahdiraji, H. (2021). Measuring Staff Satisfaction in Transportation System using AHP Method under Uncertainty. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 29(06), 875–889. https://doi.org/10.1142/S0218488521500392
Mackey, T. K., & Nayyar, G. (2017). A review of existing and emerging digital technologies to combat the global trade in fake medicines. Expert Opinion on Drug Safety, 16(5), 587–602. https://doi.org/10.1080/14740338.2017.1313227
Mateljak, Ž., & Mihanović, D. (2016). Operational planning level of development in production enterprises in the machine building industry and its impact on the effectiveness of production. Economic Research-Ekonomska Istraživanja, 29(1), 325–342. https://doi.org/10.1080/1331677X.2016.1168041
Mazer-Amirshahi, M., Pourmand, A., Singer, S., Pines, J. M., & van den Anker, J. (2014). Critical Drug Shortages: Implications for Emergency Medicine. Academic Emergency Medicine, 21(6), 704–711. https://doi.org/10.1111/acem.12389
Mehralian, G., Rajabzadeh Gatari, A., Morakabati, M., & Vatanpour, H. (2012). Developing a suitable model for supplier selection based on supply chain risks: an empirical study from Iranian pharmaceutical companies. Iranian Journal of Pharmaceutical Research : IJPR, 11(1), 209–219. https://doi.org/10.22037/ijpr.2012.1077
Merkuryeva, G., Valberga, A., & Smirnov, A. (2019). Demand forecasting in pharmaceutical supply chains: A case study. Procedia Computer Science, 149, 3–10. https://doi.org/10.1016/j.procs.2019.01.100
Moktadir, M. A., Ali, S. M., Mangla, S. K., Sharmy, T. A., Luthra, S., Mishra, N., & Garza-Reyes, J. A. (2018). Decision modeling of risks in pharmaceutical supply chains. Industrial Management & Data Systems, 118(7), 1388–1412. https://doi.org/10.1108/IMDS-10-2017-0465
Mou, Q., Xu, Z., & Liao, H. (2017). A graph based group decision making approach with intuitionistic fuzzy preference relations. Computers & Industrial Engineering, 110, 138–150. https://doi.org/10.1016/j.cie.2017.05.033
Nguyen, X. H., Le, T. A., Nguyen, A. T., Pham, T. T. H., & Tran, T. H. (2021). Supply chain risk, integration, risk resilience and firm performance in global supply chain: Evidence from Vietnam pharmaceutical industry. Uncertain Supply Chain Management, 9(4), 779–796. https://doi.org/10.5267/j.uscm.2021.8.010
O’Connor, T., Yang, X., Tian, G., Chatterjee, S., & Lee, S. (2017). 2-Quality risk management for pharmaceutical manufacturing: The role of process modeling and simulations. In Predictive Modeling of Pharmaceutical Unit Operations (pp. 15–37). Elsevier Ltd. https://doi.org/10.1016/B978-0-08-100154-7.00002-8
Onar, S. C., Oztaysi, B., Otay, İ., & Kahraman, C. (2015). Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy, 90, 274–285. https://doi.org/10.1016/j.energy.2015.06.086
Ortiz-Barrios, M., Gul, M., Yucesan, M., Alfaro-Sarmiento, I., Navarro-Jiménez, E., & Jiménez-Delgado, G. (2022). A fuzzy hybrid decision-making framework for increasing the hospital disaster preparedness: The colombian case. International Journal of Disaster Risk Reduction, 72, 102831. https://doi.org/10.1016/j.ijdrr.2022.102831
Ortiz-Barrios, M., Silvera-Natera, E., Petrillo, A., Gul, M., & Yucesan, M. (2022). A multicriteria approach to integrating occupational safety & health performance and industry systems productivity in the context of aging workforce: A case study. Safety Science, 152, 105764. https://doi.org/10.1016/j.ssci.2022.105764
Otay, İ., Oztaysi, B., Cevik Onar, S., & Kahraman, C. (2017). Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems, 133, 90–106. https://doi.org/10.1016/j.knosys.2017.06.028
Ouabouch, L., & Amri, M. (2013). Analysing Supply Chain Risk Factors : A Probability-Impact Matrix Applied to Pharmaceutical Industry. Journal of Logistics Management, 2(2), 35–40. https://doi.org/10.5923/j.logistics.20130202.01
Ouyang, X., & Guo, F. (2018). Intuitionistic fuzzy analytical hierarchical processes for selecting the paradigms of mangroves in municipal wastewater treatment. Chemosphere, 197, 634–642. https://doi.org/10.1016/j.chemosphere.2017.12.102
Paul, S., Kabir, G., Ali, S. M., & Zhang, G. (2020). Examining transportation disruption risk in supply chains: A case study from Bangladeshi pharmaceutical industry. Research in Transportation Business & Management, 37(May), 100485. https://doi.org/10.1016/j.rtbm.2020.100485
Perçin, S. (2022). Circular supplier selection using interval-valued intuitionistic fuzzy sets. Environment, Development and Sustainability, 24(4), 5551–5581. https://doi.org/10.1007/s10668-021-01671-y
PSI. (2020). Counterfeit incidents concerning pharmaceuticals worldwide in 2019, by region* [Graph]. In Statista. https://www.statista.com/statistics/253152/share-of-worldwide-counterfeiting-incidents-in-by-region/
PwC. (2020). Impact of the COVID-19 outbreak on digital payments.
Rajagopal, V., Shanmugam, P. V., & Nandre, R. (2022). Quantifying reputation risk using a fuzzy cognitive map: a case of a pharmaceutical supply chain. Journal of Advances in Management Research, 19(1), 78–105. https://doi.org/10.1108/JAMR-08-2020-0203
Rangel, D. A., De Oliveira, T. K., & Leite, M. S. A. (2015). Supply chain risk classification: Discussion and proposal. International Journal of Production Research, 53(22), 6868–6887. https://doi.org/10.1080/00207543.2014.910620
Rowe, G., & Wright, G. (2001). Expert Opinions in Forecasting: The Role of the Delphi Technique. In Principles of Forecasting (pp. 125–144). https://doi.org/10.1007/978-0-306-47630-3_7
Saaty, T. L. (1988). What is the Analytic Hierarchy Process? In Mathematical Models for Decision Support (pp. 109–121). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-83555-1_5
Sahay, A., & Jaikumar, S. (2016). Does pharmaceutical price regulation result in greater access to essential medicines? Study of the impact of drug price control order on sales volume of drugs in India. https://web.iima.ac.in/assets/snippets/workingpaperpdf/2217512512016-02-01.pdf
Sample, I. (2019). Fake drugs kill more than 250,000 children a year, doctors warn. The Guardian. https://www.theguardian.com/science/2019/mar/11/fake-drugs-kill-more-than-250000-children-a-year-doctors-warn
Sanaei, S., Chambost, V., & Stuart, P. R. (2018). Systematic assessment of triticale-based biorefinery strategies: sustainability assessment using multi-criteria decision-making (MCDM). Biofuels, Bioproducts and Biorefining, 12, S73–S86. https://doi.org/10.1002/bbb.1482
Saxena, N., Thomas, I., Gope, P., Burnap, P., & Kumar, N. (2020). PharmaCrypt: Blockchain for Critical Pharmaceutical Industry to Counterfeit Drugs. Computer, 53(7), 29–44. https://doi.org/10.1109/MC.2020.2989238
Sharma, A., Kumar, D., & Arora, N. (2022). Supply chain risk factor assessment of Indian pharmaceutical industry for performance improvement. International Journal of Productivity and Performance Management. https://doi.org/10.1108/IJPPM-01-2022-0035
Shayganmehr, M., Kumar, A., Garza-Reyes, J. A., & Zavadskas, E. K. (2022). A framework for assessing trust in e-government services under uncertain environment. Information Technology & People. https://doi.org/10.1108/ITP-01-2021-0096
Silva, J., Araujo, C., & Marques, L. (2020). Siloed Perceptions in Pharmaceutical Supply Chain Risk Management: A Brazilian Perspective. Latin American Business Review, 21(3), 223–254. https://doi.org/10.1080/10978526.2020.1731315
Stanković, M., Gladović, P., & Popović, V. (2019). Determining the importance of the criteria of traffic accessibility using fuzzy AHP and rough AHP method. Decision Making: Applications in Management and Engineering, 2(1), 86–104. https://doi.org/10.31181/dmame1901086s
Taherkhani, N., Sepehri, M. M., Shafaghi, S., & Khatibi, T. (2019). Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method. BMC Medical Informatics and Decision Making, 19(1), 182. https://doi.org/10.1186/s12911-019-0892-y
Tavana, M., Zareinejad, M., Di Caprio, D., & Kaviani, M. A. (2016). An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics. Applied Soft Computing, 40, 544–557. https://doi.org/10.1016/j.asoc.2015.12.005
Torasa, C., & Mekhum, W. (2020). Supply chain risk factors and corporate repute in pharma industry of Thailand. Systematic Reviews in Pharmacy, 11(4), 94–101. https://doi.org/10.31838/srp.2020.4.16
Torreya Partners. (2017). Worldwide forecast of pharmaceutical sector growth between 2017 and 2030, by country [Graph]. In Statista. https://www.statista.com/statistics/783145/pharmaceutical-sector-growth-forecast-worldwide-by-country/
Truong, H. Q., & Hara, Y. (2018). Supply chain risk management: Manufacturing- and service-oriented firms. Journal of Manufacturing Technology Management, 29(2), 218–239. https://doi.org/10.1108/JMTM-07-2017-0145
Tseng, M., Lim, M., & Wong, W. P. (2015). Sustainable supply chain management:A closed-loop network hierarchical approach. Industrial Management & Data Systems, 115(3), 436–461. https://doi.org/10.1108/IMDS-10-2014-0319
Tumsekcali, E., Ayyildiz, E., & Taskin, A. (2021). Interval valued intuitionistic fuzzy AHP-WASPAS based public transportation service quality evaluation by a new extension of SERVQUAL Model: P-SERVQUAL 4.0. Expert Systems with Applications, 186, 115757. https://doi.org/10.1016/j.eswa.2021.115757
Uddin, M. (2021). Blockchain Medledger: Hyperledger fabric enabled drug traceability system for counterfeit drugs in pharmaceutical industry. International Journal of Pharmaceutics, 597, 120235. https://doi.org/10.1016/j.ijpharm.2021.120235
Varndell, W., Fry, M., Lutze, M., & Elliott, D. (2020). Use of the Delphi method to generate guidance in emergency nursing practice: A systematic review. International Emergency Nursing, 100867. https://doi.org/10.1016/j.ienj.2020.100867
Vesković, S., Stević, Ž., Stojić, G., Vasiljević, M., & Milinković, S. (2018). Evaluation of the railway management model by using a new integrated model DELPHI-SWARA-MABAC. Decision Making: Applications in Management and Engineering, 1(2). https://doi.org/10.31181/dmame1802034v
Vishwakarma, V., Prakash, C., & Barua, M. K. (2016). A fuzzy-based multi criteria decision making approach for supply chain risk assessment in Indian pharmaceutical industry. International Journal of Logistics Systems and Management, 25(2), 245. https://doi.org/10.1504/IJLSM.2016.078915
Wu, Y., Chu, H., & Xu, C. (2021). Risk assessment of wind-photovoltaic-hydrogen storage projects using an improved fuzzy synthetic evaluation approach based on cloud model: A case study in China. Journal of Energy Storage, 38, 102580. https://doi.org/10.1016/j.est.2021.102580
Xu, F., Gao, K., Xiao, B., Liu, J., & Wu, Z. (2022). Risk assessment for the integrated energy system using a hesitant fuzzy multi-criteria decision-making framework. Energy Reports, 8, 7892–7907. https://doi.org/10.1016/j.egyr.2022.06.014
Xu, Z. (2007a). Intuitionistic preference relations and their application in group decision making. Information Sciences, 177(11), 2363–2379. https://doi.org/10.1016/j.ins.2006.12.019
Xu, Z. (2007b). Intuitionistic Fuzzy Aggregation Operators. IEEE Transactions on Fuzzy Systems, 15(6), 1179–1187. https://doi.org/10.1109/TFUZZ.2006.890678
Xu, Z., Member, S., & Liao, H. (2014). Intuitionistic Fuzzy Analytic Hierarchy Process. 22(4), 749–761.
Yadav, A. K., & Kumar, D. (2022). A LAG-based framework to overcome the challenges of the sustainable vaccine supply chain: an integrated BWM–MARCOS approach. Journal of Humanitarian Logistics and Supply Chain Management. https://doi.org/10.1108/JHLSCM-09-2021-0091
Yang, C., Wang, Y., Hu, X., Chen, Y., Qian, L., Li, F., Gu, W., Liu, Q., Wang, D., & Chai, X. (2021). Improving Hospital Based Medical Procurement Decisions with Health Technology Assessment and Multi-Criteria Decision Analysis. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 58, 004695802110229. https://doi.org/10.1177/00469580211022911
Yanginlar, G., & Gül, S. (2022). An EFQM-Based Self-Assessment Method for Railway Transportation Service Quality: An Application with Intuitionistic Fuzzy AHP. Ege Akademik Bakis (Ege Academic Review). https://doi.org/10.21121/eab.1008669
Yu, D., & Xu, Z. (2020). Intuitionistic fuzzy two-sided matching model and its application to personnel-position matching problems. Journal of the Operational Research Society, 71(2), 312–321. https://doi.org/10.1080/01605682.2018.1546662
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zhao, li, Huo, B., Sun, L., & Zhao, X. (2013). The impact of supply chain risk on supply chain integration and company performance: a global investigation. Supply Chain Management: An International Journal, 18(2), 115–131. https://doi.org/10.1108/13598541311318773
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