PreModIE - Predictive Model of Industrial Employability
Accounting and Finance
Empirical Social Research
Final Report Abstract
Industry 4.0 (I4.0) has ushered in unprecedented technological advancements that have been reshaping global markets by digitizing production processes and entire value chains (Patnaik, 2020). This transformation connects people, processes, and objects through realtime data exchange. Furthermore, the I4.0 transformation brings about a V.U.C.A. (volatility, uncertainty, complexity, and ambiguity) work environment, which was further intensified by the impact of the COVID-19 pandemic. As a result, the job market is undergoing disruptive changes, leading to the emergence of new job positions, e.g., data analysts, while rendering some traditional positions obsolete. This research project focuses on the implications of I4.0 on Industrial Employability (IE), specifically examining shop-floor workers who are most affected by recent labour dynamics. Presently, there is a lack of a validated and empirically tested model for IE for shop-floor workers in I4.0 within V.U.C.A. environments. Motivated by that, the following research question is raised: How can shop-floor workers’ competencies, required for a transition towards I4.0 within a V.U.C.A. environment, be improved such that their employability increases? To address this research gap, this project proposes the development and validation of a Predictive Model of Industrial Employability (PMIE). The PMIE is a model that defines IE as a combination of Knowledge, Skills, Abilities, and Other characteristics (KSAOs), clustered into four dimensions: Occupational and Technological Expertise (OTE), Adaptability (ADAP), Social Skills (SOCS), and Self-Management (SELF). This model incorporates factors at the micro (employee), meso (employer), and macro levels (socio-political), offering a holistic and context-based approach to employability. The project’s results demonstrate that targeted training interventions based on the PMIE can improve shop-floor workers' employability in I4.0 within a V.U.C.A. environment. Our results indicate an average increase of 7% in IE, according to the PMIE, within the case study company. For optimal results, long-term training measures with multiple consecutive sessions could ensure sustainable outcomes. Therefore, our findings may be less significant in comparison to long-term training programs. The design of the latter may constitute an avenue for further research.
Publications
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Developing a Scale for Industrial Employability. 10th International Research Symposium in Service Management (IRSSM-10), Dubai, United Arab Emirates, pp. 77
Metzmacher, A. I.; Beierle, S.; Heine, I.; Paluch, S.; Letmathe, P. & Schmitt, R. H.
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The Predictive Model of Industrial Employability (PMIE) -Enabling employees to effectively perform future production work. 2021 4th International Conference of the Portuguese Society for Engineering Education (CISPEE), 1-6. IEEE.
Metzmacher, Amelie I.; Beierle, Syrina; Heine, Ina; Letmathe, Peter & Schmitt, Robert H.
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Looking 4.0-ward: Wie das Internet of Production die Beschäftigungsfähigkeit in der Zukunft der Arbeit beeinflusst. INTERNET OF PRODUCTION. TURNING DATA INTO VALUE. Statusberichte aus der Produktionstechnik 2020 (AWK 2020), Aachen, Germany, pp. 305-342
Metzmacher, A. I.; Hellebrandt, T.; Heine, I.; Bergholz, M.; Döbbeler, B.; Hatfield, S.; Kieper, C.; Plutz, M.; Varandani, R. & Schmitt, R. H.
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The monetary value of competencies: A novel method and case study in smart manufacturing. Technological Forecasting and Social Change, 189, 122331.
Böhm, Robert; Letmathe, Peter & Schinner, Matthias
