Value Chains Analysis: Application of Fuzzy Cognitive Map in Pharmaceutical Industry

Subject Areas : Decision Analysis

Meysam Kharaghani ¹ (Ph. D. candidate, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Mahdi Homayounfar ² (Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Mohammad Taleghani ³ (Associate Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)

Received: 2023-09-29 Accepted : 2023-12-17 Published : 2024-02-04

Keywords: Value Chain, IoT, Medicine, Pharmaceutical Industry, FCM,

Abstract :

This study aims to investigate the factors influencing value chain (VC) in pharmaceutical industry, as a very important sector in Iran. The research method is descriptive-analytical in term of method. The required data have been collected from the experts of a public joint stock company. Conducting the research, first the literature of VC is reviewed to identify the initial factors affecting on VC of pharmaceutical companies. In this Phase, 34 factors were identified in 8 categories including; institutional, industry, political, economic, social, technological, legal and environmental factors. In the next step, a primal evaluation of the initial factors by 14 experts of the research, resulted to the 20 more important factors which were used in the modelling process. Due to the need for fuzzy logic regarding subjective judgments in cause-effect relationships between factors, the fuzzy cognitive mapping (FCM) method in FCM expert software is used to visualize the relationships among these factors. The results show that technological capabilities, government policies, company resilience, financial strength, medicine price, sustainable waste management, cost of raw materials, production technology, cost of energy, R&D, operational efficiency, recycling capabilities, transportation cost, VC governance (coordination/ partnerships/ integration), consumer behavior and social (market) trends, environmental concerns about waste disposal internet of things (IoT) and connected devices, non-value-adding activities, import limitations and skilled human resources, respectively are the most influencing factors on pharmaceutical VC.

References:

1. Asian Development Bank (ADB), 2014. Bangladesh: Agribusiness Development Project (Project Completion Report No. 33224; 2190-BAN (SF)). Asian Development Bank.

2. Ayele, A., Erchafo, T., Bashe, A., Tesfayohannes, s. (2022). Value chain analysis of wheat in Duna district, Hadiya zone, Southern Ethiopia, Heliyon, 7 (7), e07597. https://doi.org/10.1016/j.heliyon.2021.e07597.


3. Belton, B., Rosen, L., Middleton, L., Ghazali, S., Mamun, A.A., Shieh, J., et al. (2021). COVID-19 impacts and adaptations in Asia and Africa’s aquatic food value chains. Marine Policy, 129, 104523. https://doi.org/10.1016/j.marpol.2021.104523.

1. Borrero-Domínguez, C., Escobar-Rodríguez, T. (2023). Decision support systems in crowdfunding: A fuzzy cognitive maps (FCM) approach. Decision Support Systems, 173, 114000. https://doi.org/10.1016/j.dss.2023.114000


2. Cattaneo, O., Gereffi, G., & Staritz, C. (2010). Global value chains in a postcrisis world: A development perspective. World Bank Publications.

6. Eisenreich, A., Füller, J., Stuchtey, M., Gimenez-Jimenez, D. (2022). Toward a circular value chain: Impact of the circular economy on a company's value chain processes. Journal of Cleaner Production, 378, 134375. https://doi.org/10.1016/j.jclepro.2022.134375.


7. Felix, G., Nápoles, G., Falcon, R., Froelich, W., Vanhoof, K. & Bello, R. (2017). A review on methods and software for fuzzy cognitive maps. Artificial Intelligence Review, 52, 1707-1737. https://doi.org/10.1007/s10462-017-9575-1


8. Ferreira, F. A. F., Ferreira, J. J. M., Fernandes, C. I. M. A. S., Meidute- Kavaliauskiene, I., Jalal, M. S. (2017). Enhancing Knowledge and Strategic Planning of Bank Customer Loyalty Using Fuzzy Cognitive Maps. Technological and Economic Development of Economy, 6, 860-876. https://doi.org/10.3846/20294913.2016.1213200

1. Hainzer, K., Best, T. and Brown, P.H. (2019). Local value chain interventions: a systematic review. Journal of Agribusiness in Developing and Emerging Economies, 9(4), 369-390. https://doi.org/10.1108/JADEE-11-2018-0153


2. Hannibal, M., & Knight, G. (2018). Additive manufacturing and the global factory: Disruptive technologies and the location of international business. International Business Review, 27 (6), 1116–1127. https://doi.org/10.1016/j.ibusrev.2018.04.003


3. Iranban, S.J. (2019). The Effect of Supply Chain Integration on Operational Efficiency and Value Creation. Journal of System Management, 5(2), 107-132.


4. Izadi, M., Noorossana, R., Izadbakhsh, H., Saati, S., & Khalilzadeh, M. (2020). Z-Cognitive Map: An Integrated Cognitive Maps and Z-Numbers Approach under Cognitive Information. Journal of System Management, 6(2), 81-102. https://doi: 10.30495/jsm.2020.67723


5. Jassbi, A., Jafari, M., Mahdavi Mazdeh, M., & Maleki, M. (2021). Identifying the Challenges and Discussing about Current Situation of the Iranian Pharmaceutical Industry Based on a Systematic Analysis of Experts Opinion. Journal of System Management, 7(1), 1-20. https://doi: 10.30495/jsm.2021.1931181.1478


6. Juergensen, J., Guimon, J., & Narula, R. (2020). European SMEs amidst the COVID-19 crisis: Assessing impact and policy responses. Journal of Industrial and Business Economics, 47 (3), 499–510. https://doi.org/10.1007/s40812-020-00169-4

15. Khorana, S., Escaith, H., Ali, S., Kumari, S., Do, Q. (2022). The changing contours of global value chains post-COVID: Evidence from the Commonwealth. Journal of Business Research, 153, 75-86, https://doi.org/10.1016/j.jbusres.2022.07.044.


16. Kokkinos, K., Karayannis, V. & Moustakas, K. (2020). Circular bio-economy via energy transition supported by Fuzzy Cognitive Map modeling towards sustainable low-carbon environment. Science of the Total Environment, 721,137754. https://doi.org/10.1016/j.scitotenv.2020.137754


17. Lambert, D. M., & Cooper, M. C. (2000). Issues in supply chain management. Industrial Marketing Management, 29(1), 65-83. http://doi.org/10.1016/S0019-8501 (99)00113-3.


18. Low, G., Dalhaus, T., Meuwissen, M. P. M. (2023). Mixed farming and agroforestry systems: A systematic review on value chain implications, Agricultural Systems, 206, 103606. https://doi.org/10.1016/j.agsy.103606.

1. OECD. (2021). Global Value Chains: Efficiency and Risks in the Context of COVID-19. Retrieved from https://www.oecd.org/coronavirus


2. Özesmi, U., Özesmi, S. L. (2004). Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176 (1), 43-64. https://doi.org/10.1016/j.ecolmodel.2003.10.027


3. Pananond, P., Gereffi, G., & Pedersen, T. (2020). An integrative typology of global strategy and global value chains: The management and organization of cross-border activities. Global Strategy Journal, 10(3), 421–443. https://doi.org/10.1002/gsj.1388

22. Porter, M. E. (1985). Competitive Advantage: Creating and Sustaining Superior Performance. Free Press.

1. Sayyari, M., CheraghAli, M., & Saeidi, P. (2023). Strategic International Business Innovation:A New Approach in Development of Iran's Pharmaceutical Industry). Journal of System Management, 9(4), 73-84. https://doi: 10.30495/jsm.2023.1980921.1782


2. Sengupta, S., Dreyer, H. (2023). Realizing zero-waste value chains through digital twin-driven S&OP: A case of grocery retail. Computers in Industry, 148, 103890. https://doi.org/10.1016/j.compind.2023.103890


3. Strange, R., & Zucchella, A. (2017). Industry 4.0, global value chains and international business. Multinational Business Review, 25 (3), 174-184. https://doi.org/10.1108/MBR-05-2017-0028


4. Stylios, C. D., Groumpos, P. P. (2004). Modeling complex systems using fuzzy cognitive maps, IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, 34(1), 155–162. https://doi.org.10.1109/TSMCA.2003.818878


5. Tavakkol, P., Nahavandi, B., & Homayounfar, M. (2023a). Analyzing the Drivers of Bullwhip Effect in Pharmaceutical Industry’s Supply Chain. Journal of System Management, 9(1), 97-117. https://doi:10.30495/jsm.2022.1966147.1691


6. Tavakol, P., Nahavandi, B., & Homayounfar, M. (2023). A dynamics approach for modeling inventory fluctuations of the pharmaceutical supply chain in covid 19 pandemic. Journal of Optimization in Industrial Engineering, 16(1), 105-118. https://doi: 10.22094/joie.2023.1966193.1981


7. Villalba, R., Venus, T.E., Sauer, J. (2023). The ecosystem approach to agricultural value chain finance: A framework for rural credit. World Development, 164, 106177. https://doi.org/10.1016/j.worlddev.2022.106177.


8. Yaman, D., Polat, S. (2009). A fuzzy cognitive map approach for effect-based operations: an illustrative case. Information Sciences, 179(4), 382–403. https://doi.org/10.1016/j.ins.2008.10.013


9. Vanhoenshoven, F., Nápoles, G., Froelich, W., Salmeron, J.L. & Vanhoof, K. (2020). Pseudoinverse learning of Fuzzy Cognitive Maps for multivariate time series forecasting. Applied Soft Computing, 95,106461. https://doi.org/10.1016/j.asoc.2020.106461