Abstract
Humans have often been perceived as a leading cause of error in Zero-defect manufacturing (ZDM) processes. There is thus a reduction of human interventions in the deployment of industry 4.0 (I4.0) technologies used for ZDM such as Machine Learning (ML). However, as manufacturing (e.g., I4.0 context) is often placed within a socio-technological context involving the co-integration of humans and technology, the manufacturing processes are now more vulnerable to cyber risk and threats. System vulnerabilities also derive from limitations associated with ML. This paper highlights three challenges associated with ML: explainability, data privacy, and security for ZDM. We argue that due to the high level of data complexity and lack of flexibility in ML models, humans play a critical role in ZDM decision-making. The paper explores the concept of security culture as an enabler for transformative resilience and zero-defect manufacturing and contributes to rethinking the human-centered approach in ZDM. The paper stresses a need to enhance contextual and empirical understanding of transformative resilience and security culture in ML/ZDM environments to better address adverse events such as cyber threat situations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pasquale, S., Miranda, S., Neumann, W.P., Setayesh, A.: Human reliability in manual assembly systems: a systematic literature review. IFAC PapersOnLine 51(11), 675–680 (2018)
Global risks report 2022. World Economic Forum (2022). https://www.weforum.org/reports/global-risks-report-2022. Accessed 30 Jan 2023
Papageorgiou, T., et al.: A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing. Front. Manuf. Technol. 2, 972712 (2022)
Mark, M.S., Tømte, C.E., Næss, T., Røsdal, T.: Leaving the windows open–økt mangel på IKT-sikkerhetskompetanse i Norge. Norsk sosiologisk tidsskrift 3(3), 173–190 (2019)
Pollini, A., et al.: Leveraging human factors in cybersecurity: an integrated methodological approach. Cogn. Technol. Work 24(2), 371–390 (2022)
Skierka, I.: When shutdown is no option: Identifying the notion of the digital government continuity paradox in Estonia’s eID crisis. Gov. Inf. Quarterly 40(1), 101781 (2023)
Kott, A., Linkov, I.: To improve cyber resilience, measure it. arXiv preprint arXiv:2102.09455 (2021)
Groenendaal, J., Helsloot, I.: Cyber resilience during the COVID-19 pandemic crisis: a case study. J. Contingencies Crisis Manag. 29(4), 439–444 (2021)
Giovannini, E., Benczur, P., Campolongo, F., Cariboni, J., Manca, A.R.: Time for transformative resilience: the COVID-19 emergency. No. JRC120489. Joint Research Centre (Seville site) (2020)
Fisher, J.M., Ragsdale, J.M., Fisher, E.C.S.: The importance of definitional and temporal issues in the study of resilience. Appl. Psychol. 68(4), 583–620 (2019)
Malatji, M., Von Solms, S., Marnewick, A.: Socio-technical systems cybersecurity framework. Inf. Comput. Secur. 27(2), 233–272 (2019)
Wen, K.M., Kowalski, S.: An empirical study of security culture in open-source software communities. In: 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 863–870 (2019)
Adel, A.: Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas. J. Cloud Comput. Adv. Syst. Appl. 11(1), 40 (2022)
Lu, H., et al.: Outlook on human-centric manufacturing towards Industry 5.0. J. Manuf. Syst. 62, 612–627 (2022)
Neumann, W.P., Winkelhaus, S., Grosse, E.H., Glock, C.H.: Industry 4.0 and the human factor–a systems framework and analysis methodology for successful development. Int. J. Prod. Econ. 233, 107992 (2021)
Xu, X., Lu, Y., Vogel-Heuser, B., Wang, L.: Industry 4.0 and Industry 5.0—inception, conception and perception. J. Manuf. Syst. 61, 530–535 (2021)
Psarommatis, F., May, G., Dreyfus, P.A., Kiritsis, D.: Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research. Int. J. Prod. Res. 58(1), 1–17 (2020)
Crosby, D.C.: Quality is easy. Quality 45(1), 58 (2006)
Oliveira, M., Arica, E., Pinzone, M., Fantini, P., Taisch, M.: Human-centered manufacturing challenges affecting European industry 4.0 enabling technologies. In: Stephanidis, C. (ed.) HCII 2019. LNCS, vol. 11786, pp. 507–517. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30033-3_39
Wan, P.K., Leirmo, T.L.: Human-centric zero-defect manufacturing: state-of-the-art review, perspectives, and challenges. Comput. Ind. 144, 103792 (2023)
Holling, C.S.: Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 4(1), 1–23 (1973)
Luthar, D., Cicchetti, D., Becker, B.: the construct of resilience: a critical evaluation and guidelines for future work. Child Dev. 71(3), 543–562 (2000)
Hoegl, M., Hartmann, S.: Bouncing back, if not beyond: challenges for research on resilience. Asian Bus. Manag. 20(4), 456–464 (2021)
Powley, E.H., Barker Caza, B., Caza, A. (eds.): Research Handbook on Organizational Resilience. Edward Elgar Publishing, Cheltenham (2020)
Westrum, R.: A typology of resilience situations. In: Hollnagel, E., Woods, E. (eds.) Resilience Engineering, 1st edn., pp. 55–65. CRC Press, Boca Raton (2006)
Lampel, J., Shamsie, J., Shapira, Z.: Experiencing the improbable: rare events and organizational learning. Organ. Sci. 20(5), 835–835 (2009)
Shen, Y., Cheng, Y., Yu, J.: From recovery resilience to transformative resilience: how digital platforms reshape public service provision during and post COVID-19. Public Manag. Rev. 20(5), 835–845 (2022)
March, J.G.: Exploration and exploitation in organizational learning. Organ. Sci. 2(1), 71–87 (1991)
Asadzadeh, A.R., Khavarian-Garmsir, A.R., Sharifi, A., Salehi, P., Kötter, T.: Transformative resilience: an overview of its structure, evolution, and trends. Sustainability 14(22), 15267 (2022)
Wuest, T., Weimer, D., Irgens, C., Thoben, K.D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23–45 (2016)
Ni, D., Xiao, Z., Lim, M.K.: A systematic review of the research trends of machine learning in supply chain management. Int. J. Mach. Learn. Cybern. 11, 1463–1482 (2020)
Mugurusi, G., Oluka, P.N.: Towards explainable artificial intelligence (XAI) in supply chain management: a typology and research agenda. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds.) APMS 2021. IAICT, vol. 633, pp. 32–38. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85910-7_4
Von Faber, E., Kohler, A.: The gap: information security in systems with artificial intelligence How algorithms and artificial intelligence can pose a threat to IT security. Datenschutz und Datensicherheit - DuD. 43(7) (2019)
Zimmermann, V., Renaud, K.: Moving from a ‘human-as-problem” to a ‘human-as-solution” cybersecurity mindset. Int. J. Hum. Comput. Stud. 131, 169–187 (2019)
Song, G.A., Fink, G.A., Jeschke, S.: Security and Privacy in Cyber-Physical Systems, 1st edn. Wiley, Chichester (2017)
McEvoy, R., Kowalski, S.: Cassandra’s calling card: socio-technical risk analysis and management in cyber security systems. In: STPIS@ ECIS, pp. 65–80
Liu, L., De Vel, O., Han, Q.-L., Zhang, J., Xiang, Y.: Detecting and preventing cyber insider threats: a survey. IEEE Commun. Surv. Tutor. 20(2), 1397–1417 (2018)
Williams, J.C.: A User Manual for the HEART Human Reliability Assessment Method. DNV Technica (1992)
Evans, M.G., He, Y., Yevseyeva, I., Janicke, H.: Published incidents and their proportions of human error. Info. Comput. Secur. 27(3), 343–357 (2019)
Wong, W.P., Tan, K.H., Govindan, K., Li, D., Kumar, A.: A conceptual framework for information-leakage-resilience. Ann. Oper. Res. 1–21 (2021)
Burdon, M., Coles-Kemp, L.: The significance of securing as a critical component of information security: an Australian narrative. Comput. Secur. 87, 101 (2019)
Bella, G., Curzon, P., Lenzini, G.: Service security and privacy as a socio-technical problem: literature review, analysis methodology and challenge domains. J. Comput. Secur. 23(5), 563–585 (2015)
McGregor, S.E., Watkins, E., Caine, K.: Would you slack that? Proc. ACM Hum.-Comput. Interact. 1(CSCW), 1–22 (2017)
Powell, D., Magnanini, M.C., Colledani, M., Myklebust, O.: Advancing zero defect manufacturing: a state-of-the-art perspective and future research directions. Comput. Ind. 136, 103596 (2022)
Meredith, J.: Theory building through conceptual methods. Int. J. Oper. Prod. Manag. 13(5), 3–11 (1993)
Mora, M., Gelman, O., Paradice, D., Cervantes, F.: The case for conceptual research in information systems. In: CONF-IRM 2008 Proceedings, p. 52 (2008)
Caiazzo, B., Di Nardo, M., Murino, T., Petrillo, A., Piccirillo, G., Santini, S.: Towards Zero Defect Manufacturing paradigm: a review of the state-of-the-art methods and open challenges. Comput. Ind. 134, 103548 (2022)
Willingham, D.T.: Inflexible knowledge: The first step to expertise [blog]. American Educator (2002). https://www.aft.org/periodical/american-educator/winter-2002/ask-cognitive-scientist. Accessed 4 Jan 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mitcheltree, C.M., Mugurusi, G., Holtskog, H. (2024). Cyber Security Culture as a Resilience-Promoting Factor for Human-Centered Machine Learning and Zero-Defect Manufacturing Environments. In: Silva, F.J.G., Ferreira, L.P., Sá, J.C., Pereira, M.T., Pinto, C.M.A. (eds) Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems. FAIM 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-38165-2_86
Download citation
DOI: https://doi.org/10.1007/978-3-031-38165-2_86
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-38164-5
Online ISBN: 978-3-031-38165-2
eBook Packages: EngineeringEngineering (R0)