Risk Factors Assessment of Smart Supply Chain in Intelligent Manufacturing Services Using DEMATEL Method With Linguistic q-ROF Information

Authors

DOI:

https://doi.org/10.31181/jopi21202417

Keywords:

Intelligent manufacturing service, Smart supply chain, Lq-ROFWA, DEMATEL

Abstract

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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Author Biographies

  • Tingjun Xu, Nanchang Hangkong University, School of Economics and Management, Nanchang, China

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  • Haolun Wang, Nanchang Hangkong University, School of Economics and Management, Nanchang, China

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  • Liangqing Feng, Nanchang Hangkong University, School of Economics and Management, Nanchang, China

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  • Yanping Zhu, Nanchang Hangkong University, School of Economics and Management, Nanchang, China

    .

References

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

Published

2024-01-28

How to Cite

Xu, T., Wang, H., Feng, L., & Zhu, Y. (2024). Risk Factors Assessment of Smart Supply Chain in Intelligent Manufacturing Services Using DEMATEL Method With Linguistic q-ROF Information. Journal of Operations Intelligence, 2(1), 129-152. https://doi.org/10.31181/jopi21202417