Evaluating the Vulnerability of Forestry Supply Chains Through Fuzzy Cognitive Map

Year 2025, Volume: 11 Issue: 1, 30 - 41

Mirac Murat , Ertuğrul Ayyıldız , Umut Asan

https://doi.org/10.33904/ejfe.1517968

Abstract

It is crucial to address the significant role of the supply chain in economic activity and its vulnerable areas. This research focuses on the forestry industry, where obtaining and maintaining the primary raw material source is notably more challenging than other supply chains. The primary objective of this study is to analyze vulnerability factors specific to the forestry supply chain (FSC) by modeling their interactions and potential influences on each other. Initially, relevant factors were defined through a comprehensive literature review, encompassing vulnerabilities both in the general supply chain and pertinent to the forestry industry, such as natural disruptions. Then, the relationships between these factors were modeled and simulated using an input-sensitive fuzzy cognitive map (FCM). A cognitive map was constructed based on expert opinions, facilitated by triangular fuzzy numbers to express expert judgments accurately. FCM simulations using a new reasoning mechanism were conducted to analyze the effects of FSC vulnerability factors on one another across three sustainability-themed scenarios: economically related vulnerabilities, socially related vulnerabilities, and environmentally related vulnerabilities. Supply chain structure, government support, and source availability were the main vulnerability factors influencing the overall resilience of the FSC. Environmental stressors such as natural disturbances and climate change, and economic shocks, were found to significantly impact FSC dynamics, highlighting the need for adaptive strategies and robust contingency planning. This research is significant for stakeholders in the forestry industry as it elucidates the vulnerability factors within the FSC and demonstrates how different vulnerabilities can influence one another.

Keywords

Forestry supply chain, sustainable forestry, supply chain vulnerability, scenario analysis, triangular fuzzy numbers, fuzzy cognitive mapping

References

• Acuna, M., Sessions, J., Zamora, R., Boston, K., Brown, M., Ghaffariyan, M.R. 2019. Methods to Manage and Optimize Forest Biomass Supply Chains: a Review. Current Forestry Reports, 5(3):124–141. https:// doi.org/10.1007/s40725-019-00093-4.
• Asan, U., Kadaifçi, Ç. 2020. A new product positioning approach based on fuzzy cognitive mapping. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(2):1047–1061.
• Barnett, J. 2018. Addressing policy challenges to woody biopower production: Social acceptance, biomass certification and limited policy support. Open Access Dissertation, Michigan Technological University, Department of Social Sciences, Michigan.
• Bueno, S., Salmeron, J.L. 2009. Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications, 36(3):5221–5229. https://doi.org/10.1016/j.eswa.2008.06.072.
• Cambero, C., Sowlati, T. 2016. Incorporating social benefits in multi-objective optimization of forest-based bioenergy and biofuel supply chains. Applied Energy, 178:721–735. https://doi.org/10.1016/j.apen ergy. 2016.06.079.
• Chen, J., Wang, L., Li, L., Magalhães, J., Song, W., Lu, W., Xiong, L., Chang, W.Y., Sun, Y. 2020. Effect of forest certification on international trade in forest products. Forests, 11(12):1270. https://doi.org/10. 3390/ f11121270.
• Curtis, P.G., Slay, C.M., Harris, N.L., Tyukavina, A., Hansen, M.C. 2018. Classifying drivers of global forest loss. Science, 361(6407). https://doi.org/ 10.1126/science.aau3445.
• Dashtpeyma, M., Ghodsi, R. 2021. Forest Biomass and Bioenergy Supply Chain Resilience: A Systematic Literature Review on the Barriers and Enablers. Sustainability, 3(12):6964. https://doi.org/10.3390/ SU13126964.
• Dechprom, S., Jermsittiparsert, K. 2019. Sustainability Related Supply Chain Risks: A Case of Multiple Organizational Strategic Networks. International Journal of Innovation, Creativity and Change, 5(2):769–785.
• Deshpande, S., Hudnurkar, M., Rathod, U. 2023. An exploratory study into manufacturing supply chain vulnerability and its drivers. Benchmarking, 30(1):23–49. https://doi.org/10.1108/BIJ-04-2021-0233/FULL/PDF.
• Dubey, R., Bryde, D.J., Dwivedi, Y.K., Graham, G., Foropon, C., Papadopoulos, T. 2023. Dynamic digital capabilities and supply chain resilience: The role of government effectiveness. International Journal of Production Economics, 258:108790. https://doi.org/10.1016/J.IJPE.2023.108790.
• Dursun, M., Goker, N., Gumus, G. 2019. Evaluation of supply chain configuration criteria using fuzzy cognitive map, in: AIP Conference Proceedings. American Institute of Physics Inc., Rhodes, p. 2116. https://doi.org/10.1063/1.5114530/754221.
• Elias, A.A., Donadelli, F., Paiva, E.L., Bacic Araujo, P.P. 2021. Analysing the complexities of sustainable wood supply chain in the Amazon: a systems thinking approach. International Journal of Logistics Management, 32(4):1481–1505. https://doi.org/10. 1108/IJLM-07-20200276/FULL/PDF.
• Elleuch, H., Dafaoui, E., Elmhamedi, A., Chabchoub, H. 2016. Resilience and Vulnerability in Supply Chain: Literature review. IFAC-PapersOnLine, 49(12):1448–1453. https://doi.org/10.1016/J.IFACOL .2016.07. 775.
• Feng, Y., Audy, J.F.,2021. Forestry 4.0: A framework for the forest supply chain toward industry 4.0. Gestao e Producao, 27(4). https://doi.org/10.1590/0104-530X5677-20.
• Frias, M., Filiberto, Y., Nápoles, G., García-Socarrás, Y., Vanhoof, K., Bello, R. 2018. Fuzzy cognitive maps reasoning with words based on triangular fuzzy numbers, in: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (Eds.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp. 197–207. https://doi.org/10.1007/978-3-030-02837-4_16/FIG URES/5.
• Garrett, R.D., Levy, S.A., Gollnow, F., Hodel, L., Rueda, X. 2021. Have food supply chain policies improved forest conservation and rural livelihoods? A systematic review. Environmental Research Letters, 16(3):033002. https://doi.org/10.1088/1748-9326/abe0ed.
• Gavrilut, I., Halalisan, A.F., Giurca, A., Sotirov, M. 2016. The interaction between FSC certification and the implementation of the EU timber regulation in Romania. Forests, 7(1):3. https://doi.org/10.3390/ f7010003.
• Gomes, L. de C. 2022. Mitigation of Supply Chain Vulnerability Through Collaborative Planning, Forecasting, and Replenishment (CPFR), in: Khojasteh, Y., Xu, H.,, Zolfaghari, S. (Eds.), International Series in Operations Research and Management Science. Springer, Cham. https:// doi.org/10.1007/978-3-031-09183-4_5.
• Greenslade, C., Murphy, R.J., Morse, S., Griffiths, G.H. 2021. Breaking Down the Barriers: Exploring the Role of Collaboration in the Forestry Sector of South East England. Sustainability, 13(18):10258. https://doi.org/10.3390/SU131810258.
• He, Z., Turner, P. 2021. A Systematic Review on Technologies and Industry 4.0 in the Forest Supply Chain: A Framework Identifying Challenges and Opportunities. Logistics, 5(4):88. https://doi.org/ 10.3390/logistics5040088.
• Hoffmann, S., Jaeger, D., Shuirong, W. 2018. Adapting chinese forest operations to socio-economic developments: What is the potential of plantations for strengthening domestic wood supply? Sustainability, 10(4):1042. https://doi.org/10.3390/su10041042.
• Irvanizam, I., Rusdiana, S., Amrusi, A., Arifah, P., Usman, T. 2018. An application of fuzzy multiple-attribute decision making model based onsimple additive weighting with triangular fuzzy numbers to distribute the decenthomes for impoverished families. Journal of Physics: Conference Series, 1116(2): 022016. https://doi.org/10.1088/1742-6596/1116/2/ 022016.
• Ivanov, D. 2017. Simulation-based single vs. dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. International Journal of Integrated Supply Management, 11(1):24–43. https://doi.org/ 10.1504/IJISM.2017.083005.
• Ivanov, D., Sokolov, B. 2019. Simultaneous structural–operational control of supply chain dynamics and resilience. Annals of Operations Research, 283(1–2). https://doi.org/10.1007/s10479-019-03231-0.
• Jüttner, U., Peck, H., Christopher, M. 2003. Supply chain risk management: outlining an agenda for future research. International Journal of Logistics: Research and Applications, 6(4):197–210. https:// doi.org/ 10.1080/13675560310001627016.
• Korhonen, J., Honkasalo, A., Seppälä, J. 2018. Circular Economy: The Concept and its Limitations. Ecological Economics, 143. https://doi.org/10.1016/ j.ecolecon.2017.06.041.
• Kosko, B. 1986. Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1):65–75. https://doi.org/10.1016/S0020-7373(86)80040-2.
• Kumar, R., Kumar, A., Saikia, P. 2022. Deforestation and Forests Degradation Impacts on the Environment. In: Singh, V.P., Yadav, S., Yadav, K.K., Yadava, R.N. (eds) Environmental Degradation: Challenges and Strategies for Mitigation. Water Science and Technology Library, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-030-95542-7_2.
• Lanfredi, M., Coluzzi, R., Imbrenda, V., Nosova, B., Giacalone, M., Turco, R., Prokopovà, M., Salvati, L. 2023. In-between Environmental Sustainability and Economic Viability: An Analysis of the State, Regulations, and Future of Italian Forestry Sector. Land, 12(5):1001. https://doi.org/10.3390/LAND 12051001.
• Lee, S.J., Lee, Y.J., Ryu, J.Y., Kwon, C.G., Seo, K.W., Kim, S.Y. 2022. Prediction of Wildfire Fuel Load for Pinus densiflora Stands in South Korea Based on the Forest-Growth Model. Forests, 13(9). https://doi.org/ 10.3390/f13091372.
• Liu, J., Zhou, Y., Lu, B., Zhao, J. 2015. The reduction mechanism of supply chain vulnerability based on supply chain disruption risk. Systems Engineering - Theory & Practice, 35(3):556–566. https://doi.org/ 10.12011/1000-6788(2015)3-556.
• Liu, Q., Ning, Z. 2023. Impact of Global Supply Chain Crisis on Chinese Forest Product Enterprises: Trade Trends and Literature Review. Forests, 14(6):1247. https://doi.org/10.3390/F14061247.
• Luo, L., O’Hehir, J., Regan, C.M., Meng, L., Connor, J.D., Chow, C.W.K. 2021. An integrated strategic and tactical optimization model for forest supply chain planning. Forest Policy and Economics, 131:102571. https://doi.org/10.1016/J.FORPOL.2021.102571.
• Mensah, P., Merkuryev, Y., Longo, F. 2015. Using ICT in Developing a Resilient Supply Chain Strategy. Procedia Computer Science, 43:101–108. https:// doi.org/10.1016/J.PROCS.2014.12.014.
• Özesmi, U., Özesmi, S.L. 2004. Ecological models based on people’s knowledge: A multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1–2):43–64. https://doi.org/10.1016/j.ecolmodel.2003.10.027.
• Palander, T., Tokola, T., Borz, S.A., Rauch, P. 2024. Forest Supply Chains During Digitalization: Current Implementations and Prospects in Near Future. Current Forestry Reports, 10(3):223–238. https:// doi.org/10.1007/S40725-024-00218-4/FIGURES/4.
• Pandirwar, A.P., Khadatkar, A., Mehta, C.R., Majumdar, G., Idapuganti, R., Mageshwaran, V., Shirale, A.O. 2023. Technological Advancement in Harvesting of Cotton Stalks to Establish Sustainable Raw Material Supply Chain for Industrial Applications: A Review. Bioenergy Research, 16(2):741–760. https://doi.org/10.1007/s12155-022-10520-3.
• Pandit, P., Earthperson, A., Tezbasaran, A., Diaconeasa, M.A. 2021. A quantitative approach to assess the likelihood of supply chain shortages, in: ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE). https://doi.org/ 10.1115/IMECE2021-73696.
• Papageorgiou, E.I., Salmeron, J.L. 2013. A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems, 21(1):66–79. https://doi.org/10.1109/TFUZZ.2012.2201727.
• Parsopoulos, K.E., Papageorgiou, E.I., Groumpos, P.P., Vrahatis, M.N. 2003. A first study of fuzzy cognitive maps learning using particle swarm optimization. 2003 Congress on Evolutionary Computation, CEC 2003 – Proceedings, 2:1440–1447. https://doi.org/ 10.1109/CEC.2003.1299840.
• Pettit, T.J., Croxton, K.L., Fiksel, J. 2019. The Evolution of Resilience in Supply Chain Management: A Retrospective on Ensuring Supply Chain Resilience. Journal of Business Logistics, 40(1):56–65. https://doi.org/10.1111/JBL.12202.
• Podvesovskii, A.G., Isaev, R.A. 2018. Visualization metaphors for fuzzy cognitive maps. Scientific Visualization, 10(4):13–29. https://doi.org/10.26583/ sv.10.4.02.
• Radhakrishnan, S., Harris, B., Kamarthi, S. 2018. Supply chain resiliency: A review, in: Khojasteh, Y. (Ed.), Supply Chain Risk Management: Advanced Tools, Models, and Developments. Springer, Singapore, pp. 215–235. https://doi.org/10.1007/978-981-10-4106-8_13/TABLES/3.
• Ramage, M.H., Burridge, H., Busse-Wicher, M., Fereday, G., Reynolds, T., Shah, D.U., Wu, G., Yu, L., Fleming, P., Densley-Tingley, D., Allwood, J., Dupree, P., Linden, P.F., Scherman, O. 2017. The wood from the trees: The use of timber in construction. Renewable and Sustainable Energy Reviews, 68:333–359. https://doi.org/10.1016/ j.rser.2016.09.107.
• Raulier, F., Dhital, N., Racine, P., Tittler, R., Fall, A. 2014. Increasing resilience of timber supply: How a variable buffer stock of timber can efficiently reduce exposure to shortfalls caused by wildfires. Forest Policy and Economics, 46:47–55. https://doi.org/ 10.1016/J.FORPOL.2014.06.007.
• Roos, A. 2023. Forest damage and forest supply chains: a literature review and reflections. International Journal of Forest Engineering, 34(3):330–339. https://doi.org/10.1080/14942119.2023.2240607.
• Sabahi, S., Stanfield, P.M. 2019. Understanding the impact of supply chain resilience antecedents through FCM, in: IISE Annual Conference and Expo 2019. Florida.
• Scholz, J., De Meyer, A., Marques, A.S., Pinho, T.M., Boaventura-Cunha, J., Van Orshoven, J., Rosset, C., Künzi, J., Kaarle, J., Nummila, K. 2018. Digital Technologies for Forest Supply Chain Optimization: Existing Solutions and Future Trends. Environmental Management, 62(6):1108–1133. https://doi.org/ 10.1007/s00267-018-1095-5.
• Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., Vacchiano, G., Wild, J., Ascoli, D., Petr, M., Honkaniemi, J., Lexer, M.J., Trotsiuk, V., Mairota, P., Svoboda, M., Fabrika, M., Nagel, T.A., Reyer, C.P.O. 2017. Forest disturbances under climate change. Nature Climate Change, 7(6):395–402. https://doi.org/10.1038/nclimate3303.
• Sharma, S.K., Routroy, S., Singh, R.K., Nag, U. 2022. Analysis of supply chain vulnerability factors in manufacturing enterprises: a fuzzy DEMATEL approach. International Journal of Logistics Research and Applications, 27(5):814–841. https://doi.org/10.1080/13675567.2022.2083590.
• Sharma, S.K., Sharma, R., Jindal, A. 2024. An integrated structural model for supply chain vulnerability influencing factors in manufacturing enterprises. Journal of Modelling in Management, https:// doi.org/10.1108/JM2-10-2023-0227/FULL /PDF.
• Sharma, S.K., Srivastava, P.R., Kumar, A., Jindal, A., Gupta, S. 2023. Supply chain vulnerability assessment for manufacturing industry. Annals of Operations Research, 326(2):653–683. https:// doi.org/10.1007/S10479-021-04155-4/FIGURES/8.
• Soyer, A., Bozdag, E., Kadaifci, C., Asan, U., Serdarasan, S. 2023. A hesitant approach to sustainable supply chain risk assessment. Journal of Cleaner Production, 418:138103. https://doi.org/ 10.1016/j.jclepro.2023.138103.
• Sun, C.C. 2010. A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12):7745–7754. https://doi.org/10.1016/j.eswa.2010.04.066.
• Thiffault, E., Berndes, G., Junginger, M., Saddler, J.N., Smith, C.T. 2016. Mobilisation of Forest Bioenergy in the Boreal and Temperate Biomes: Challenges, Opportunities and Case Studies, Mobilisation of Forest Bioenergy in the Boreal and Temperate Biomes: Challenges, Opportunities and Case Studies.
• Torresan, C., Garzón, M.B., O’grady, M., Robson, T.M., Picchi, G., Panzacchi, P., Tomelleri, E., Smith, M., Marshall, J., Wingate, L., Tognetti, R., Rustad, L.E., Kneeshaw, D. 2021. A new generation of sensors and monitoring tools to support climate-smart forestry practices. Canadian Journal of Forest Research, 51(12):1751–1765. https://doi.org/10.1139/cjfr-2020-0295.
• van Laarhoven, P.J.M., Pedrycz, W. 1983. A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1–3):229–241. https://doi.org/10.1016/ S0165-0114(83)80082-7.
• Vermeulen, W.J.V., Kok, M.T.J. 2012. Government interventions in sustainable supply chain governance: Experience in Dutch front-running cases. Ecological Economics, 83:183–196. https://doi.org/10.1016/ j.ecolecon.2012.04.006.
• Wang, M., Radics, R., Islam, S., Hwang, K.S. 2023. Towards Forest Supply Chain Risks. Operations and Supply Chain Management: An International Journal, 16(1):97–108. https://doi.org/10.31387/OS CM0520375.
• Wang, S., Tian, X. 2022. Research on Sustainable Closed-Loop Supply Chain Synergy in Forest Industry Based on High-Quality Development: A Case Study in Northeast China. Forests, 13(10). https://doi.org/10.3390/f13101587.
• Wolfsmayr, U.J., Rauch, P. 2014. The primary forest fuel supply chain: A literature review. Biomass and Bioenergy, 60:203–221. https://doi.org/10.1016/ j.biombioe.2013.10.025.
• Yang, J., Liu, H. 2018. Research of Vulnerability for Fresh Agricultural-Food Supply Chain Based on Bayesian Network. Mathematical Problems in Engineering, 2018(1):6874013. https://doi.org/ 10.1155/2018/6874013. Yesil, E., Dodurka, M.F., Urbas, L. 2014. T
• riangular fuzzy number representation of relations in Fuzzy Cognitive Maps, in: IEEE International Conference on Fuzzy Systems. Beijing, pp. 1021–1028. https://doi.org/10.1109/FUZZ-IEEE.2014.6891653.
• Zanon, L.G., Carpinetti, L.C.R. 2018. Fuzzy cognitive maps and grey systems theory in the supply chain management context: A literature review and a research proposal, in: IEEE International Conference on Fuzzy Systems. Rio de Janeiro, pp. 1–8. https://doi.org/10.1109/FUZZ-IEEE.2018.8491473.
• Zhang, X., Sun, C., Munn, I.A., Gordon, J. 2021. How to protect the U.S. forest products industry from the perspective of trade? A comparison of policies within the forest supply chain. Forest Policy and Economics, 133:102616. https://doi.org/10.1016/J.FORPOL. 2021.102616. Zhang, X., Wang, J., Vance, J., Wang, Y., Wu, J., Hartley, D. 2020. Data Analytics for Enhancement of Forest and Biomass Supply Chain Management. Current Forestry Reports, 6(2):129–142. https://doi.org/ 10.1007/s40725-020-00111-w.