Risk Factors Assessment of Smart Supply Chain in Intelligent Manufacturing Services Using DEMATEL Method With Linguistic q-ROF Information
DOI:
https://doi.org/10.31181/jopi21202417Keywords:
Intelligent manufacturing service, Smart supply chain, Lq-ROFWA, DEMATELAbstract
With the rapid development of technological informatization, competition among enterprises is gradually transitioning from being "production-centered" to being "customer-centric," making service-oriented enterprises increasingly important. In addition to this, as global manufacturing advances in the process of intelligent manufacturing (IM), there is growing attention on the integration of manufacturing and the service industry, which has garnered the interest of numerous experts and scholars in the field of intelligent manufacturing services (IMS). This article combines intelligent manufacturing enterprises, intelligent service nodes, and consumers. Based on the background of intelligent manufacturing services, it collected risk factors within the smart supply chain (SSC) that connect different service nodes. These factors were evaluated by experts using a proposed linguistic q-rung orthopair fuzzy weighted averaging (Lq-ROFWA) operator in combination with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method for aggregation operations. Finally, we obtain the conclusions that the most influential factor affecting other risk factors is the inadequate identification of core customer needs; and the most important risk factor for smart supply chains oriented to intelligent manufacturing services is the leakage of customer information. After analyzing the relevant data, we will provide some theoretical and managerial implications for IM enterprises.
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Li, X., & Zhou, D. (2020). Product Design Requirement Information Visualization Approach for Intelligent Manufacturing Services. China Mechanical Engineering, 31(7), 871-881. https://doi.org/10.3969/j.issn.1004-132X.2020.07.013
Zhang, W., Wang, X. K., Shi, Y. J., Gu, X. J., Wang, J., & Tian, J. H. (2023). Construction technology of intelligent manufacturing service systems driven by industrial big data. SCIENTIA SINICA Technologica, 53(7), 1084-1096. https://doi.org/10.1360/SST-2022-0372
Shieh, J. I., Wu, H. H., & Huang, K. K. (2010). A DEMATEL method in identifying key success factors of hospital service quality. Knowledge-Based Systems, 23(3), 277-282. https://doi.org/10.1016/j.knosys.2010.01.013
Kashyap, A., Kumar, C., Kumar, V., & Shukla, O. J. (2022). A DEMATEL model for identifying the impediments to the implementation of circularity in the aluminum industry. Decision Analytics Journal, 5, 100134. https://doi.org/10.1016/j.dajour.2022.100134
Yilmaz, I., Erdebilli, B., Naji, M. A., & Mousrij, A. (2023). A Fuzzy DEMATEL framework for maintenance performance improvement: A case of Moroccan Chemical Industry. Journal of Engineering Research, 11(1), 100019. https://doi.org/10.1016/j.jer.2023.100019
Sun, L., Peng, J., Dinçer, H., & Yüksel, S. (2022). Coalition-oriented strategic selection of renewable energy system alternatives using q-ROF DEMATEL with golden cut. Energy, 256(1), 124606. https://doi.org/10.1016/j.energy.2022.124606
Giret, A., Garcia, E., & Botti, V. (2016). An engineering framework for Service-Oriented Intelligent Manufacturing Systems. Computers in Industry, 81, 116-127. https://doi.org/10.1016/j.compind.2016.02.002
Zhang, L., Feng, L., Wang, J., & Lin, K. Y. (2022). Integration of Design, Manufacturing, and Service Based on Digital Twin to Realize Intelligent Manufacturing. Machines, 10(4), 275. https://doi.org/10.3390/machines10040275
Wang, S., Zhang, Y., Qian, C., & Zhang, D. (2021). A framework for credit-driven smart manufacturing service configuration based on complex networks. International Journal of Computer Integrated Manufacturing, 35(10-11), 1107-1132. https://doi.org/10.1080/0951192X.2021.1879400
Zhang, G., Chen, C. H., Zheng, P., & Zhong, R. Y. (2020). An integrated framework for active discovery and optimal allocation of smart manufacturing services. Journal of Cleaner Production, 273(10), 123144. https://doi.org/10.1016/j.jclepro.2020.123144
Tao, F., & Qi, Q. (2017). New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1), 81-91. https://doi.org/10.1109/TSMC.2017.2723764
Liu, Z., Qin, X., Cheng, Q., & Tang, Y. (2021). Thoughts on Smart Supply Chain Management System. Supply Chain Management, 9, 5-15. https://doi.org/10.19868/j.cnki.gylgl.2021.09.001
Liu, W., Zeng, Y., & Qiao, X. (2022). Preliminary Study on Quality Standard System of Smart Supply Chain. Supply Chain Management, 9, 5-19. https://doi.org/10.19868/j.cnki.gylgl.2022.09.001
Liu, W., Long, S., & Wei, S. (2022). Correlation mechanism between smart technology and smart supply chain innovation performance: A multi-case study from China’s companies with Physical Internet. International Journal of Production Economics, 245, 108394. https://doi.org/10.1016/j.ijpe.2021.108394
AlMulhim, A. F. (2021). Smart supply chain and firm performance: the role of digital technologies. Business Process Management Journal, 27(5), 1353-1372. https://doi.org/10.1108/BPMJ-12-2020-0573
Viriyasitavat, W., Bi, Z., & Hoonsopon, D. (2022). Blockchain technologies for interoperation of business processes in smart supply chains. Journal of Industrial Information Integration, 26, 100326. https://doi.org/10.1016/j.jii.2022.100326
Butner, K. (2010). The smarter supply chain of the future. Strategy & leadership, 38(1), 22-31. https://doi.org/10.1108/10878571011009859
Tripathi, S., & Gupta, M. (2020). Transforming towards a smarter supply chain. International Journal of Logistics Systems and Management, 36(3), 319-342. https://doi.org/10.1504/IJLSM.2020.108694
Marc, I., & Berlec, T. (2023). Inventory Risk Decision-Making Techniques Using Customer Behaviour Analysis. Journal of Mechanical Engineering, 69(7-8), 317-325. http://dx.doi.org/10.5545/sv-jme.2023.577
Chen, J., Xiao, Y., & Zhu, B. (2021). Procurement risk evaluation from a big-data perspective: A case study of a procurement service company. Systems Engineering Theory＆Practice, 41(3), 596-612. https://doi.org/10.12011/SETP2019-1219
Oke, A. E., Kineber, A. F., Akindele, O., & Ekundayo, D. (2023). Determining the stationary barriers to the implementation of radio frequency identification (RFID) technology in an emerging construction industry. Journal of Engineering, Design and Technology. https://doi.org/10.1108/JEDT-07-2022-0348
Bekishev, Y., Pisarenko, Z., & Arkadiev, V. (2023). FMEA Model in Risk Analysis for the Implementation of AGV/AMR Robotic Technologies into the Internal Supply System of Enterprises. Risks, 11(10), 172. https://doi.org/10.3390/risks11100172
Li, B., Hou, B., Yu, W., Lu, X., & Yang, C. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86-96. https://doi.org/10.1631/FITEE.1601885
Liu, Y., & Zheng, J. (2022). Intelligent management of supply chain logistics based on 5g LoT. Cluster Computing, 25, 2271–2280. https://doi.org/10.1007/s10586-021-03487-x
Zheng, K., Huo, X., Jasimuddin, S., Zhang, Z., & Battaïa, O. (2023). Logistics distribution optimization: Fuzzy clustering analysis of e-commerce customers’ demands. Computers in Industry, 151, 103960. https://doi.org/10.1016/j.compind.2023.103960
Qi, Q., Xu, Z., & Rani, P. (2023). Big data analytics challenges to implementing the intelligent Industrial Internet of Things (IIoT) systems in sustainable manufacturing operations. Technological Forecasting & Social Change, 190, 1-15. https://doi.org/10.1016/j.techfore.2023.122401
Kaur, B., Dadkhah, S., Shoeleh, F., Neto, E. C. P., Xiong, P., Iqbal, S., Lamontagne, P., Ray, S., & Ghorbani, A. (2023). Internet of Things (IoT) security dataset evolution: Challenges and future directions. Internet of Things, 22, 1-23. https://doi.org/10.1016/j.iot.2023.100780
Fodor, G., Dahlman, E., Mildh, G., Parkvall, S., Reider, N., Miklós, G., & Turányi, Z. (2012). Design aspects of network assisted device-to-device communications. IEEE Communications Magazine, 50(3), 170-177. https://doi.org/10.1109/MCOM.2012.6163598
Weibull, K., Lidestam, B., & Prytz, E. (2022). Potential of Cooperative Intelligent Transport System Services to Mitigate Risk Factors Associated With Emergency Vehicle Accidents. Transportation research record, 2677(3), pp.999-1015. https://doi.org/10.1177/03611981221119459
Wei, X., Sun, C., Lyu, M., Song, Q., & Li, Y. (2022). ConstDet: Control Semantics-Based Detection for GPS Spoofing Attacks on UAVs. Remote Sensing, 14(21), 5587. https://doi.org/10.3390/rs14215587
Xu, M., Cui, Y., Hu, M., Xu, X., Zhang, Z., Liang, S., & Qu, S. (2019). Supply chain sustainability risk and assessment. Journal of Cleaner Production, 225, 857-867. https://doi.org/10.1016/j.jclepro.2019.03.307
Govindan, K., & Chaudhuri, A. (2016). Interrelationships of risks faced by third party logistics service providers: A DEMATEL based approach. Transportation Research Part E., 90, 177-195. https://doi.org/10.1016/j.tre.2015.11.010
Rajesh, R., & Ravi, V. (2017). Analyzing drivers of risks in electronic supply chains: a grey–DEMATEL approach. The International Journal of Advanced Manufacturing Technology, 92, 1127-1145. https://doi.org/10.1007/s00170-017-0118-3
Sreedevi, R., & Saranga, H. (2017). Uncertainty and supply chain risk: The moderating role of supply chain flexibility in risk mitigation. International Journal of Production Economics, 193, 332-342. https://doi.org/10.1016/j.ijpe.2017.07.024
Rostamzadeh, R., Ghorabaee, M. K., Govindan, K., Esmaeili, A., & Nobar, H. B. K. (2018). Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS- CRITIC approach. Journal of Cleaner Production, 175(20), 651-669. https://doi.org/10.1016/j.jclepro.2017.12.071
Wan, N., Li, L., Ye, C., & Wang, A. B. (2019). Risk Assessment in Intelligent Manufacturing Process: A Case Study of An Optical Cable Automatic Arranging Robot. IEEE Access, 7, 105892–105901. https://doi.org/10.1109/ACCESS.2019.2932756
Hansen, J., Hellin, J., Rosenstock, T., Fisher, E., Cairns, J., Stirling, C., Lamanna, C., Etten, J., Rose, A., & Campbell, B. (2019). Climate risk management and rural poverty reduction. Agricultural Systems, 172, 28-46. https://doi.org/10.1016/j.agsy.2018.01.019
Abdel-Basset, M., & Mohamed, R. (2020). A novel plithogenic TOPSIS- CRITIC model for sustainable supply chain risk management. Journal of Cleaner Production, 247(20), 119586. https://doi.org/10.1016/j.jclepro.2019.119586
Munir, M., Jajja, M. S. S., Chatha, K. A., & Farooq, S. (2020). Supply chain risk management and operational performance: The enabling role of supply chain integration. International Journal of Production Economics, 227, 107667. https://doi.org/10.1016/j.ijpe.2020.107667
Liu, C., Ji, H., & Wei, J. (2021). Smart Supply Chain Risk Assessment in Intelligent Manufacturing. Journal of Computer Information Systems, 62(3), 609-621. https://doi.org/10.1080/08874417.2021.1872045
Lin, S. S., Shen, S. L., Zhou, A., & Xu, Y. S. (2021). Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods. Automation in Construction, 122, 103490. https://doi.org/10.1016/j.autcon.2020.103490
Li, S., Huang, K., Liu, Y., Ge, F., & Liu, S. (2022). Risk Assessment in Supplier Selection for Intelligent Manufacturing Systems Based on PLS-SEM. Applied Science, 12(8), 3998. https://doi.org/10.3390/app12083998
Seker, S., Bağlan, F. B., Aydin, N., Deveci, M., & Ding, W. (2023). Risk assessment approach for analyzing risk factors to overcome pandemic using interval-valued q-rung orthopair fuzzy decision making method. Applied Soft Computing, 132, 109891. https://doi.org/10.1016/j.asoc.2022.109891
Cheng, C., Steinman, A. D., Zhang, K., Lin, Q., Xue, Q., Wang, X., & Xie, L. (2023). Risk assessment and identification of factors influencing the historical concentrations of microcystin in Lake Taihu, China. Journal of environmental sciences, 127, 1-14 https://doi.org/10.1016/j.jes.2022.03.043
Reshad, A. I., & Biswas, T., Agarwal, R., Paul, S. K., Azeem, A. (2023). Evaluating barriers and strategies to sustainable supply chain risk management in the context of an emerging economy. Business strategy and the environment, 1-20. https://doi.org/10.1002/bse.3367
Lima, F. A. D., & Seuring, S. (2023). A Delphi study examining risk and uncertainty management in circular supply chains. International journal of production economics, 258, 108810. https://doi.org/10.1016/j.ijpe.2023.108810
Minguito, G., & Banluta, J. (2023). Risk management in humanitarian supply chain based on FMEA and grey relational analysis. Socio-economic planning sciences, 87(B), 101551. https://doi.org/10.1016/j.seps.2023.101551
Lin, M., Li, X., & Chen, L. (2020). Linguistic q‐rung orthopair fuzzy sets and their interactional partitioned Heronian mean aggregation operators. International Journal of Intelligent Systems, 35(2), 217-249. https://doi.org/10.1002/int.22136
Liu, P., & Liu, W. (2019). Multiple‐attribute group decision‐making based on power Bonferroni operators of linguistic q‐rung orthopair fuzzy numbers. International Journal of Intelligent Systems, 34(4), 652-689. https://doi.org/10.1002/int.22071
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