An Integrated SCOR-MCDM Approach for Performance Evaluation in Pharmaceutical Distribution Network

Document Type : Research Paper

Authors

Department of Management, Faculty of Management and Economics, University of Guilan, Rasht, Iran

10.22059/ijms.2025.390256.677389

Abstract

The pharmaceutical supply chain plays a critical role in ensuring the timely and safe delivery of medicines to pharmacies, hospitals, and patients. However, the increasing complexity caused by diverse demand, stringent transportation and storage requirements, and the need for precise logistics pose significant challenges. This study aims to improve performance evaluation by providing an integrated SCOR-MCDM framework, combining the supply chain operations reference model (SCOR) with multi-criteria decision-making (MCDM) techniques. The novelty of this research lies in integrating the Decision Evaluation and Testing Laboratory (DEMATEL) method to identify causal relationships among performance criteria, with the Best-Worst Method (BWM) to prioritize them based on expert judgment in the pharmaceutical industry. The proposed approach provides a structured mechanism for evaluating the pharmaceutical distribution network, especially when real-time operational data is limited. The findings show that according to DEMATEL, "Supply stability," “Facility cost,” and “Information systems cost,” "Number of vehicles for distribution," “Delivery time,” and "Flexibility in product volume" have the greatest impact on the efficiency of the pharmaceutical distribution network. In addition, the results of the BWM also show that "Delivery time" is the most important from the expert’s point of view, which indicates that if decision makers want to improve the most important factor, namely “Delivery time,” they need to pay special attention to the factors affecting this factor to create an overall improvement. These insights provide practical implications for improving the efficiency and responsiveness of pharmaceutical supply chains. In addition, by addressing global challenges in pharmaceutical distribution network, the proposed model is also relevant beyond the Iranian context and can be effective in decision making in similar healthcare systems around the world.

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Abideen, A. Z., & Mohamad, F. B. (2020). Supply chain lead time reduction in a pharmaceutical production warehouse–A case study. International Journal of Pharmaceutical and Healthcare Marketing, 14(1), 61-88. https://doi.org/10.1108/IJPHM-02-2019-0005.
Abu Zwaida, T., Pham, C., & Beauregard, Y. (2021). Optimization of inventory management to prevent drug shortages in the hospital supply chain. Applied Sciences, 11(6), 2726. https://doi.org/10.3390/app11062726.
Achuora, J. O., Arasa, R. M., Nzioki, W., Ochiri, G., & Muangangi, P. (2013). Factors affecting distribution performance for pharmaceutical products in Kenya public sector. International Journal of Research in Social Sciences, 3(1), 118-139. http://erepo.usiu.ac.ke/11732/1359.
Agami, N., Saleh, M., & Rasmy, M. (2012). An innovative fuzzy logic based approach for supply chain performance management. IEEE Systems Journal, 8(2), 336-342. https://doi.org/10.1109/JSYST.2012.2219913.
Akbarpour, M., Torabi, S. A., & Ghavamifar, A. (2020). Designing an integrated pharmaceutical relief chain network under demand uncertainty. Transportation Research Part E: Logistics and Transportation Review, 136, 101867. https://doi.org/10.1016/j.tre.2020.101867.
Akram, W., Joshi, R., Haider, T., Sharma, P., Jain, V., Garud, N., & Narwaria, N. S. (2024). Blockchain technology: A potential tool for the management of pharma supply chain. Research in Social and Administrative Pharmacy. https://doi.org/10.1016/j.sapharm.2024.02.014.
Al-Saa'da, R. J., Taleb, Y. K. A., Al Abdallat, M. E., Al-Mahasneh, R. A. A., Nimer, N. A., & Al-Weshah, G. A. (2013). Supply chain management and its effect on health care service quality: Quantitative evidence from Jordanian private hospitals. Journal of Management and Strategy, 4(2), 42. https://doi.org/10.5430/jms.v4n2p42.
Anderson, S. W., & Dekker, H. C. (2009). Strategic cost management in supply chains, part 1: Structural cost management. Accounting Horizons, 23(2), 201-220. https://doi.org/10.2308/acch.2009.23.2.201.
Aytekin, A., Görçün, Ö. F., Ecer, F., Pamucar, D., & Karamaşa, Ç. (2023). Evaluation of the pharmaceutical distribution and warehousing companies through an integrated Fermatean fuzzy entropy-WASPAS approach. Kybernetes, 52(11), 5561-5592. https://doi.org/10.1108/K-04-2022-0508.
Bam, L., McLaren, Z., Coetzee, E., & Von Leipzig, K. (2017). Reducing stock-outs of essential tuberculosis medicines: A system dynamics modelling approach to supply chain management. Health Policy and Planning, 32(8), 1127-1134. https://doi.org/10.1093/heapol/czx057.
Bigdeli, M., Laing, R., Tomson, G., & Babar, Z.-U.-D. (2015). Medicines and universal health coverage: Challenges and opportunities. Journal of Pharmaceutical Policy and Practice. https://doi.org/10.1186/s40545-015-0028-4.
Bilal, A. I., Bititci, U. S., & Fenta, T. G. (2024). Effective supply chain strategies in addressing demand and supply uncertainty: A case study of ethiopian pharmaceutical supply services. Pharmacy, 12(5), 132. https://doi.org/10.3390/pharmacy12050132.
Birhanu, Y., Gizaw, T., Teshome, D., Boche, B., & Gudeta, T. (2022). The mediating effect of information sharing on pharmaceutical supply chain integration and operational performance in Ethiopia: An analytical cross-sectional study. Journal of Pharmaceutical Policy and Practice, 15(1), 44. https://doi.org/10.1186/s40545-022-00440-0.
Briscoe, C. J., & Hage, D. S. (2009). Factors affecting the stability of drugs and drug metabolites in biological matrices. Bioanalysis, 1(1), 205-220. https://doi.org/10.4155/bio.09.20.
Cheraghali, A. M. (2013). Impacts of international sanctions on Iranian pharmaceutical market. DARU Journal of Pharmaceutical Sciences, 21, 1-3. https://doi.org/10.1186/2008-2231-21-64.
Cometto, G., Zaheer-Ud-Din Babar, A. B., Hedman, L., & Campbell, J. (2014). PtD) Global Conference on Human Resources in Supply Chain Management. Journal of Pharmaceutical Policy and Practice, 7(1), 11. http://www.joppp.org/supplements/7/S1.
Divsalar, M., Ahmadi, M., & Nemati, Y. (2020). A SCOR-based model to evaluate LARG supply chain performance using a hybrid MADM method. IEEE Transactions on Engineering Management, 69(4), 1101-1120. https://doi.org/10.1109/TEM.2020.2974030.
Dixit, A., Routroy, S., & Dubey, S. K. (2020a). Measuring performance of government-supported drug warehouses using DEA to improve quality of drug distribution. Journal of Advances in Management Research, 17(4), 567-581. https://doi.org/10.1108/JAMR-12-2019-0227.
Dixit, A., Routroy, S., & Dubey, S. K. (2020b). A strategy to improve resource utilization: Case study of generic drug distribution in Rajasthan. Materials Today: Proceedings, 28, 562-567. https://doi.org/10.1016/j.matpr.2019.12.219.
Dolatabad, A. H., Mahdiraji, H. A., Babgohari, A. Z., Garza-Reyes, J. A., & Ai, A. (2022). Analyzing the key performance indicators of circular supply chains by hybrid fuzzy cognitive mapping and Fuzzy DEMATEL: Evidence from healthcare sector. Environment, Development and Sustainability, 1-27. https://doi.org/10.1007/s10668-022-02535-9.
El Mokrini, A., Benabbou, L., & Berrado, A. (2018). Multi-criteria distribution network redesign-case of the public sector pharmaceutical supply chain in Morocco. In The Supply Chain Forum: An International Journal. https://doi.org/10.1080/16258312.2018.1433436.
Feibert, D. C., Sørup, C. M., & Jacobsen, P. (2016). Using the Analytic Network Process (ANP) to assess the distribution of pharmaceuticals in hospitals–a comparative case study of a Danish and American hospital. In The 5th World Conference on Production and Operations Management. https://orbit.dtu.dk/en/publications/0db1fac0-6b99-4efe-858a-7967c2653db7.
George, S., & Elrashid, S. (2023). Inventory management and pharmaceutical supply chain performance of hospital pharmacies in Bahrain: A structural equation modeling approach. Sage Open, 13(1), 21582440221149717. https://doi.org/10.1177/21582440221149717.
Ghatari, A. R., Mehralian, G., Zarenezhad, F., & Rasekh, H. R. (2013). Developing a model for agile supply: An empirical study from Iranian pharmaceutical supply chain. Iranian Journal Of Pharmaceutical Research: IJPR, 12(Suppl), 193. https://doi.org/10.22037/ijpr.2013.1287.
Goodarzian, F., Taleizadeh, A. A., Ghasemi, P., & Abraham, A. (2021). An integrated sustainable medical supply chain network during COVID-19. Engineering Applications of Artificial Intelligence, 100, 104188. https://doi.org/10.1016/j.engappai.2021.104188.
Gorcun, O. F., Senthil, S., & Küçükönder, H. (2021). Evaluation of tanker vehicle selection using a novel hybrid fuzzy MCDM technique. https://doi.org/10.31181/DMAME210402140G.
Hossain, M. K., & Thakur, V. (2021). Benchmarking health-care supply chain by implementing Industry 4.0: A fuzzy-AHP-DEMATEL approach. Benchmarking: An International Journal, 28(2), 556-581. https://doi.org/10.1108/BIJ-05-2020-0268.
Hosseini, A., Johansson, S., & Westberg, M. (2017). Design and performance evaluation of a healthcare distribution network towards maintaining a direct-to-customer policy. International Journal of Advanced Logistics, 6(2), 68-79. https://doi.org/10.1080/2287108X.2018.1472965.
Huan, S. H., Sheoran, S. K., & Wang, G. (2004). A review and analysis of supply chain operations reference (SCOR) model. Supply Chain Management: An International Journal, 9(1), 23-29. https://doi.org/10.1108/13598540410517557?u3Dr.
Karmaker, C. L., & Ahmed, T. (2020). Modeling performance indicators of resilient pharmaceutical supply chain. Modern Supply Chain Research and Applications, 2(3), 179-205. https://doi.org/10.1108/MSCRA-04-2020-0006.
Khan, S. A., Gupta, H., Gunasekaran, A., Mubarik, M. S., & Lawal, J. (2023). A hybrid multi‐criteria decision‐making approach to evaluate interrelationships and impacts of supply chain performance factors on pharmaceutical industry. Journal of Multi‐Criteria Decision Analysis, 30(1-2), 62-90. https://doi.org/10.1002/mcda.1800.
Kheybari, S., Rezaie, F. M., & Rezaei, J. (2019). Measuring the importance of decision-making criteria in biofuel production technology selection. IEEE Transactions on Engineering Management, 68(2), 483-497. https://doi.org/10.1109/TEM.2019.2908037.
Michaelides, K., Prasanna, M., Badhan, R., Mohammed, A. U. R., Walters, A., Howard, M. K., & Al-Khattawi, A. (2023). Single administration vaccines: delivery challenges, in vivo performance, and translational considerations. Expert Review of Vaccines, 22(1), 579-595. https://doi.org/10.1080/14760584.2023.2229431.
Kumar, R., & Das, D. (2023). A performance evaluation framework of public health distribution network for essential medicines: Lessons from the select Indian states. International Transactions in Operational Research, 30(1), 421-452. https://doi.org/10.1111/itor.13040.
Kumar, A., Shukla, O. J., & Pandey, S. M. (2024). An integrated fuzzy DEMATEL-BWM method to analyse critical factors for sustainable vaccine supply chain in low-middle income nations. International Journal of Multicriteria Decision Making, 10(1), 70-101. https://doi.org/10.1504/IJMCDM.2024.143268.
Lal, A., Lim, C., Almeida, G., & Fitzgerald, J. (2022). Minimizing COVID-19 disruption: Ensuring the supply of essential health products for health emergencies and routine health services. The Lancet Regional Health–Americas, 6. https://doi.org/10.1016/j.lana.2021.100129.
Leblebicioğlu, B., & Keskin, A. (2021). Evaluation of supplier selection criteria with fuzzy DEMATEL method: An application on the pharmacy industry. Avrupa Bilim ve Teknoloji Dergisi(23), 236-242. https://doi.org/10.31590/ejosat.864116.
Lee, M., Yoon, Y., Ryu, G. H., Bok, H. S., Yoon, K., Park, S., & Lee, K.-S. (2018). Innovative distribution priorities for the medical devices industry in the fourth industrial revolution. International Neurourology Journal, 22(Suppl 2), S83. https://doi.org/10.5213/inj.1836152.076.
Lima-Junior, F. R., & Carpinetti, L. C. R. (2020). An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR® metrics. Computers & Industrial Engineering, 139, 106191. https://doi.org/10.1016/j.cie.2019.106191.
Malefaki, S., Markatos, D., Filippatos, A., & Pantelakis, S. (2025). A Comparative analysis of multi-criteria decision-making methods and normalization techniques in holistic sustainability assessment for engineering applications. Aerospace, 12(2), 100. https://doi.org/10.3390/aerospace12020100.
Mehralian, G., Nazari, J. A., Zarei, L., & Rasekh, H. R. (2016). The effects of corporate social responsibility on organizational performance in the Iranian pharmaceutical industry: The mediating role of TQM. Journal of Cleaner Production, 135, 689-698. https://doi.org/10.1016/j.jclepro.2016.06.116.
Mezouar, H., El Afia, A., Chiheb, R., & Ouzayd, F. (2016). Proposal of a modeling approach and a set of KPI to the drug supply chain within the hospital. In The 2016 3rd International Conference on Logistics Operations Management (GOL). https://doi.org/10.1109/GOL.2016.7731691.
Mitchell, M. J., Billingsley, M. M., Haley, R. M., Wechsler, M. E., Peppas, N. A., & Langer, R. (2021). Engineering precision nanoparticles for drug delivery. Nature Reviews Drug Discovery, 20(2), 101-124. https://doi.org/10.1038/s41573-020-0090-8.
Nguyen, T. T. H. (2024). Measuring Supply Chain Performance Using the SCOR Model. In The Operations Research Forum. https://doi.org/10.1007/s43069-024-00314-y.
Ntais, C., Talias, M. A., Fanourgiakis, J., & Kontodimopoulos, N. (2024). Managing pharmaceutical costs in health systems: A review of affordability, accessibility and sustainability strategies. Journal of Market Access & Health Policy, 12(4), 403-414. https://doi.org/10.3390/jmahp12040031.
Olawale, A. (2024). Optimizing pharmaceutical supply chain management for new drug launches: best practices and technologies. https://easychair.org/publications/preprint/ppqx.
Olyaaeemanesh, A., Jaafaripooyan, E., Abdollahiasl, A., Davari, M., Mousavi, S. M., & Delpasand, M. (2021). Pharmaceutical subsidy policy in Iran: A qualitative stakeholder analysis. Health Research Policy and Systems, 19, 1-17. https://doi.org/10.1186/s12961-021-00762-6.
Ortíz-Barrios, M., Jaramillo-Rueda, N., Gul, M., Yucesan, M., Jiménez-Delgado, G., & Alfaro-Saíz, J.-J. (2023). A fuzzy hybrid MCDM approach for assessing the emergency department performance during the COVID-19 outbreak. International Journal of Environmental Research and Public Health, 20(5), 4591. https://doi.org/10.3390/ijerph20054591.
Prabhuram, T., Rajmohan, M., Tan, Y., & Robert Johnson, R. (2020). Performance evaluation of Omni channel distribution network configurations using multi criteria decision making techniques. Annals of Operations Research, 288, 435-456. https://doi.org/10.1007/s10479-020-03533-8.
Redshaw, C. H., Stahl-Timmins, W. M., Fleming, L. E., Davidson, I., & Depledge, M. H. (2013). Potential changes in disease patterns and pharmaceutical use in response to climate change. Journal of Toxicology and Environmental Health, Part B, 16(5), 285-320. https://doi.org/10.1080/10937404.2013.802265.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009.
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130. https://doi.org/10.1016/j.omega.2015.12.001.
Rouhani-Tazangi, M. R., Khoei, M. A., Pamucar, D., & Feghhi, B. (2023). Evaluation of key indicators affecting the performance of healthcare supply chain agility.  In The Supply Chain Forum: An International Journal. https://doi.org/10.1080/16258312.2023.2171239.
Sahu, K., & Sahu, A. K. (2019). Performance measurement of medicines delivery of pharmaceutical companies under chain of sustainable procurement. International Journal of Social Ecology and Sustainable Development (IJSESD), 10(3), 116-128. https://doi.org/10.4018/IJSESD.2019070108.
Sharma, S., & Modgil, S. (2020). TQM, SCM and operational performance: An empirical study of Indian pharmaceutical industry. Business Process Management Journal, 26(1), 331-370. https://doi.org/10.1108/BPMJ-01-2018-0005.
Si, S.-L., You, X.-Y., Liu, H.-C., & Zhang, P. (2018). DEMATEL technique: A systematic review of the state‐of‐the‐art literature on methodologies and applications. Mathematical Problems in Engineering, 2018(1), 3696457. https://doi.org/10.1155/2018/3696457.
Stankevičienė, J., & Sviderskė, T. (2010). Developing a performance measurement system integrating economic value added and the balanced scorecard in pharmaceutical company. Business and Management, 2010, 13-14. https://doi.org/10.3846/bm.2010.033.
Su, J.-M., Lee, S.-C., Tsai, S.-B., & Lu, T.-L. (2016). A comprehensive survey of the relationship between self-efficacy and performance for the governmental auditors. SpringerPlus, 5, 1-13. https://doi.org/10.1186/s40064-016-2104-x.
Supeekit, T., Somboonwiwat, T., & Kritchanchai, D. (2016). DEMATEL-modified ANP to evaluate internal hospital supply chain performance. Computers & Industrial Engineering, 102, 318-330. https://doi.org/10.1016/j.cie.2016.07.019.
Taherdoost, H., & Madanchian, M. (2023). Multi-criteria decision making (MCDM) methods and concepts. Encyclopedia, 3(1), 77-87. https://doi.org/10.3390/encyclopedia3010006.
Tsai, C.-J., & Shyr, W.-J. (2022). Using the DEMATEL method to explore influencing factors for video communication and visual perceptions in social media. Sustainability, 14(22), 15164. https://doi.org/10.3390/su142215164.
Uzir, M. U. H., Al Halbusi, H., Thurasamy, R., Hock, R. L. T., Aljaberi, M. A., Hasan, N., & Hamid, M. (2021). The effects of service quality, perceived value and trust in home delivery service personnel on customer satisfaction: Evidence from a developing country. Journal of Retailing and Consumer Services, 63, 102721. https://doi.org/10.1016/j.jretconser.2021.102721.
Wilson, N. W., Couper, I. D., De Vries, E., Reid, S., Fish, T., & Marais, B. J. (2009). A critical review of interventions to redress the inequitable distribution of healthcare professionals to rural and remote areas. Rural and Remote Health, 9(2), 1-21. https://doi.org/10.22605/RRH1060.
Yang, X., Strauss, A. K., Currie, C. S., & Eglese, R. (2016). Choice-based demand management and vehicle routing in e-fulfillment. Transportation Science, 50(2), 473-488. https://doi.org/10.1287/trsc.2014.0549.
Yu, W., Zhao, G., Liu, Q., & Song, Y. (2021). Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: An organizational information processing theory perspective. Technological Forecasting and Social Change, 163, 120417. https://doi.org/10.1016/j.techfore.2020.120417.
Zhou, H., Benton Jr, W., Schilling, D. A., & Milligan, G. W. (2011). Supply chain integration and the SCOR model. Journal of Business Logistics, 32(4), 332-344. https://doi.org/10.1111/j.0000-0000.2011.01029.x.
Zhou, J., Wang, Q., Tsai, S.-B., Xue, Y., & Dong, W. (2016). How to evaluate the job satisfaction of development personnel. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(11), 2809-2816. https://doi.org/10.1109/TSMC.2016.2519860.  
Ziaee, M., Shee, H. K., & Sohal, A. (2023). Big data analytics in Australian pharmaceutical supply chain. Industrial Management & Data Systems, 123(5), 1310-1335. https://doi.org/10.1108/IMDS-05-2022-0309