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Fuzzy comprehensive model of manufacturing industry transfer risk based on economic big data analysis

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Języki publikacji
EN
Abstrakty
EN
Aiming at the problems of low accuracy, low efficiency and low stability of traditional methods and recent developments in advanced technology incite the industries to be in sync with modern technology. With respect to various available techniques, this paper designs a fuzzy comprehensive evaluation model of the manufacturing industry for transferring risk based on economic big-data analytics. The big-data analysis method is utilized to obtain the data source of fuzzy evaluation of the manufacturing industry to transfer risk using data as the basis of risk evaluation. Based on the risk factors, the proposed model establishes the risk index system of the manufacturing industry and uses the expert evaluation method to design the scoring method of the evaluation index system. To ensure the accuracy of the evaluation results, the manufacturing industry’s fuzzy comprehensive model is established using the entropy weight method, and the expert evaluation results are modified accordingly. The experimental results show that the highest efficiency of the proposed method is 96%, the highest accuracy of the evaluation result is 75%. The evaluation result’s stability is higher than the other existing methods, which fully verifies the effectiveness and can provide a reliable theoretical basis for enterprise risk evaluation research.
Rocznik
Strony
art. no. e139959
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
autor
  • Department of Economics, Shenyang Institute of Science and Technology, Shenyang, 110167, China
autor
  • College of International Business, Shenyang Normal University, Shenyang, 110034, China
Bibliografia
  • [1] M. Jan, M.S. Khalid, A.A. Awan, and S. Nisar, “Proposing probabilistic operational risk evaluation model for textile industry using bayesian approach”, Fibres Text. East. Eur., vol. 26, no. 1, pp. 10–20, 2018.
  • [2] M. Hatzisymeon, S. Kamenopoulos, and T. Tsoutsos, “Risk evaluation of the life-cycle of the Used Cooking Oil-to-biodiesel supply chain”, J. Clean. Prod., vol. 217, no. 20, pp. 836–843, 2019.
  • [3] X. Zhang, “Financial risk evaluation of listed enterprises in marine engineering equipment manufacturing industry”, J. Coast. Res., vol. 98, no. 1, p. 155, 2019.
  • [4] M.I. Marcondes, W.H. Mariano, and A.D. Vries, “Production, economic viability and risks associated with switching dairy cows from drylots to compost bedded pack systems”, Animal, vol. 14, no. 2, pp. 1–10, 2019.
  • [5] M. Kaur and S. Kadam, “Bio-Inspired Workflow Scheduling on HPC Platforms”, Tehniˇcki Glasnik, vol. 15, no. 1, pp. 60–68, 2021, doi: 10.31803/tg-20210204183323.
  • [6] R. Tulder, B. Jankowska, and A. Verbeke, “Introduction: International Business in a VUCA World”, in International Business in a VUCA World: The Changing Role of States and Firms, vol. 14, p. 1, eds. R. Tulder, B. Jankowska, and A. Verbeke, Emerald Publishing Limited, Bingley, 2019.
  • [7] Z.G. Wang, B.X. Zhu, and S.L. Liao, “Research on dynamic evaluation of core manufacturer risk considering weighted multiplier: based on supply chain viewing angle”, Sci. Technol. Manag. Res., vol. 422, no. 4, pp. 265–273, 2019.
  • [8] T.K. Biswas and K. Zaman, “A fuzzy-based risk evaluation methodology for construction projects under epistemic uncertainty”, Int. J. Fuzzy Syst., vol. 21, no. 4, pp. 1221–1240, 2019.
  • [9] H. Zhao, Y. Wang, X.D. Zhang, and S.B. Ma, “Financing risk evaluation of featured town ppp project based on cloud model”, J. Civil Eng. Manag., vol. 36, no. 4, pp. 81–88, 2019.
  • [10] G. Durić, G. Todorović, A. Dordević, and A. B. Tišma, “A New Fuzzy Risk Management Model for Production Supply Chain Economic and Social Sustainability”, Econ. Res.-Ekon. Istraz., vol. 32, no. 1, pp. 1697–1715, 2019, doi: 10.1080/1331677X.2019.1638287.
  • [11] Q. Kang, “Financial risk assessment model based on big data”, Int. J. Model. Simul. Sci. Comput., vol. 10, no. 4, p. 1950021, 2019.
  • [12] S. Moradi and F.M. Rafiei, “A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks”, Financ. Innov., vol. 5, p. 15, 2019.
  • [13] G. Ning, B. Fang, D. Qin, Y. Liang, and L. Zheng, “Design and application of comprehensive evaluation index system of smart grid based on coordinated planning of major network and power distribution network”, Arch. Electr. Eng., vol. 70, no. 1, pp. 103–113, 2021.
  • [14] S.R. Danyela, M.C.G.D. Silva, A.D. Ferreira, and R.E. Cabanillas-Lopez, “Solar energy industry workers under climate change: A risk evaluation of the level of heat stress experienced by a worker based on measured data”, Saf. Sci., vol. 118, no. 118, pp. 33–47, 2019.
  • [15] T.K. Biswas and and K. Zaman, “A fuzzy-based risk evaluation methodology for construction projects under epistemic uncertainty”, Int. J. Fuzzy Syst., vol. 21, no. 4, pp. 1221–1240, 2019.
  • [16] B.H. Alharbi, M.J. Pasha, and M.A.S. Al-Shamsi, “Metal contamination decrease with new legislation: A decade of metal risk evaluation in urban dust”, J. Environ. Manag., vol. 236, no. 15, pp. 214–223, 2019.
  • [17] K.J. Sileyew, “Systematic industrial OSH advancement factors identification for manufacturing industries: A case of Ethiopia”, Saf. Sci., vol. 132, no. 1, pp. 1–15, 2020.
  • [18] H.D.L. Fuente-Mella, J.L.R. Fuentes, and V. Leiva, “Econometric modeling of productivity and technical efficiency in Chilean manufacturing industry”, Comput. Ind. Eng., vol. 139, no. 1, pp. 105793(1–11), 2020.
  • [19] W. Cai et al., “Promoting sustainability of manufacturing industry through the lean energy-saving and emission-reduction strategy”, Sci. Total Environ., vol. 665, no. 15, p. 23, 2019.
  • [20] C. Wang, “IoT anomaly detection method in intelligent manufacturing industry based on trusted evaluation”, Int. J. Adv. Manuf. Technol., vol. 107, no. 3, pp. 993–1005, 2020.
  • [21] A. Sgobba and C. Meskell, “On-site renewable electricity production and self consumption for manufacturing industry in Ireland: Sensitivity to techno-economic conditions”, J. Clean. Prod., vol. 207, no. 1180, pp. 894–907, 2019.
  • [22] A.G. Frank, L.S. Dalenogare, and N.F. Ayala, “Industry 4.0 technologies: Implementation patterns in manufacturing companies”, Int. J. Prod. Econ., vol. 210, no. 4, pp. 15–26, 2019.
  • [23] D.L. Diener, D. Kushnir, and A.M. Tillman, “Scrap happens: A case of industrial end-users, maintenance and component remanufacturing outcome”, J. Clean. Prod., vol. 213, no. 10, pp. 863–871, 2019.
  • [24] Y. Wang and M. Ruan, “Risk estimation simulation of excess trading transactions based on big data”, Comput. Simul., vol. 35, no. 3, pp. 369–372(+388), 2018.
  • [25] A. Lewandowska-Ciszek, “Complexity of a modern enterprise and its flexibility in the sector of industrial automation”, Bull. Pol. Acad. Sci-Chem., vol. 68, no. 3, pp. 575–584, 2020, doi: 10.24425/bpasts.2020.133126.
  • [26] Q. Li, Z. Cao, M. Tanveer, H.M. Pandey, and C. Wang, “An effective reliability evaluation method for power communication network based on community structure”, IEEE Trans. Ind. Appl., vol. 56, no. 4, pp. 4489–4500, 2020.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5d8d9860-2c7f-4ab7-a9b6-2d4bf6bc7b38
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