Internet of Things Calls for a New Way of Working

Ways to Digitally Transform Qualification, Organization, and Leadership

JournalIndustrie 4.0 Management
Issue Volume 34, 2018, Edition 3, Pages 8-12
Open Accesshttps://doi.org/10.30844/I40M_18-3_S8-12
Bibliography Share Cite Download

Abstract

When aiming for an Industry 4.0 vision, companies are well-advised to not only focus on technology and data. With any digital transformation, the careful consideration of all elements of the company’s “socio-technical triangle” (man, technology, and organization) is a central success factor. Based on a qualitative survey, we identified qualification, organization, and leadership as central dimensions of the work system. Integrative measures include identification of competence requirements, training in data-thinking as well as agile working methods and structures. Finally, leadership plays a central role in orchestrating the digital transformation.

Keywords


Bibliography

[1] Spath, D. (Hrsg): Produktionsarbeit der Zukunft – Industrie 4.0, Studie. Stuttgart 2013.
[2] Andelfinger, V. P.; Hänisch, T.: Internet der Dinge. Technik, Trends, Geschäftsmodelle. Wiesbaden 2015.
[3] Bullinger, H.-J.; ten Hompel, M.: Internet der Dinge. Berlin Heidelberg 2007.
[4] Windelband, L.; Spöttl, G.: Diffusion von Technologien in die Facharbeit und deren Konsequenzen für die Qualifizierung am Beispiel des „Internet der Dinge“. In: Faßhauer, U.; Fürstenau, B.; Wuttke, E. (Hrsg): Berufs- und wirtschaftspädagogische Analysen. Aktuelle Forschungen zur beruflichen Bildung. Opladen u. a. 2012.
[5] Ullrich, A.; Vladova, G.; Thim, C.; Gronau, N.: Akzeptanz und Wandlungsfähigkeit im Zeichen der Industrie 4.0. In: HMD Praxis der Wirtschaftsinformatik 52 (2015) 5, S. 769-89.
[6] Franken, S.: Führen in der Arbeitswelt der Zukunft: Instrumente, Techniken und Best-Practice-Beispiele. Wiesbaden 2016.
[7] Dougados, M.; Felgendreher, B.: Digitale Transformation der Supply Chain – Stand heute und in 5 Jahren. Eine branchenübergreifende Studie mit 337 Führungskräften aus 20 Ländern offenbart die Erwartungen an die Digitale Transformation. Hamburg 2016.
[8] Deuse, J.; Weisner, K.; Hengstebeck, A.; Busch, F.: Gestaltung von Produktionssystemen im Kontext von Industrie 4.0. In: Botthof, A.; Hartmann, E. A. (Hrsg): Zukunft der Arbeit in Industrie 4.0. Berlin Heidelberg 2015.
[9] Becker, K.-D.: Arbeit in der Industrie 4.0 – Erwartungen des Instituts für angewandte Arbeitswissenschaft e.V. In: Botthof, A.; Hartmann, E. A. (Hrsg): Zukunft der Arbeit in Industrie 4.0. Berlin Heidelberg 2015.
[10] Kersten, W.; Seiter, M.; See, B. von; Hackius, N.; Maurer, T.: Chancen der digitalen Transformation. Trends und Strategien in Logistik und Supply Chain Management. Hamburg 2017.
[11] Hoberg, K.; Alicke, K.; Flöthmann, C.; Lundin; J.: The DNA of Supply Chain Executives. Supply Chain Management Review 18 (2014) 6, S. 36-43.
[12] Dregger, J.; Niehaus, J.; Itter- mann, P.; Hirsch-Kreinsen, H.; ten Hompel, M.: The digitization of manufacturing and its societal challenges: a framework for the future of industrial labor: 2016 IEEE International Symposium on Ethics in Engineering, Science and Technology (ETHICS). Vancouver 2016.
[13] Kersten, W.; Schröder, M.; Indorf, M.: Industrie 4.0: Auswirkungen auf das Supply Chain Risikomanagement. In: Lödding, H.; Kersten, W.; Koller, H. (Hrsg): Industrie 4.0 – Wie intelligente Vernetzung und kognitive Systeme unsere Arbeit verändern. Berlin 2014.
[14] Hirsch-Kreinsen, H.; ten Hompel, M.: Digitalisierung industrieller Arbeit. Entwicklungsperspektiven und Gestaltungsansätze. In: Vogel-Heuser, B., Bauernhansl, T.; ten Hompel, M. (Hrsg): Handbuch Industrie 4.0 Bd. 3: Logistik. Berlin Heidelberg 2017, S. 357-376.
[15] Ulich, E.: Arbeitssysteme als soziotechnische Systeme – eine Erinnerung. In: iafob/Ulich, E. (Hrsg): Unternehmensgestaltung im Spannungsfeld von Stabilität und Wandel. Neue Erfahrungen und Erkenntnisse. Band 2. Zürich 2016, S. 81-96.
[16] Ulich, E.: Arbeitspsychologie. Stuttgart 2011.
[17] Faller, M.; Otto, C.: Industrie 4.0 gelingt nur mit aktivem Personalmanagement. MaschinenMarkt 45 (2014), S. 22-23.
[18] von Ameln, F.; Wimmer, R.: Neue Arbeitswelt, Führung und organisationaler Wandel. Gruppe. Interaktion. Organisation. In: Zeitschrift für Angewandte Organisationspsychologie (GIO) 47 (2016) 1, S. 11-21.
[19] Dombrowski, U.; Riechel, C.; Evers, M.: Industrie 4.0 – Die Rolle des Menschen in der vierten industriellen Revolu- tion. In: Kersten, W.; Koller, H.; Lödding, H. (Hrsg): Industrie 4.0. Wie intelligente Vernetzung und kognitive Systeme unsere Arbeit verändern, Berlin 2014, S. 129-153.
[20] Ludwig, T.; Kotthaus, C.; Stein, M.; Durt, H.; Kurz, C.; Wenz, J.; Doublet, T.; Becker, M.; Pipek, V.; Wulf, V.: Arbeiten im Mittelstand 4.0 – KMU im Spannungsfeld des digitalen Wandels. HMD Praxis der Wirtschaftsinformatik 53 (2016) 1, S. 71-86.
[21] Gehrckens, H. M.: Agilität im Kontext der digitalen Transformation – Kernanfor- derung an die Organisation von morgen. In: Heinemann, G.; Gehrckens, H. M.; Wolters, U. J.; dgroup GmbH (Hrsg): Digitale Transformation oder digitale Disruption im Handel. Vom Point-of-Sale zum Point-of-Decision im Digital Commerce. Wiesbaden 2016, S. 79-108.
[22] Huber, T.: Führungspersönlichkeit 4.0. So gelingt der Weg zum Digital Leader. Arbeit und Arbeitsrecht 1 (2016), S. 34-35.
[23] McAfee, A.; Brynjolfsson, E.: Big data: the management revolution. Harvard Business Review 90 (2012) 10, S. 60-68.
[24] Staufen AG (Hrsg): Deutscher Industrie 4.0 Index 2015. Industrie 4.0 und Lean. Eine Studie der Staufen AG, Köngen 2015. URL: http://www.staufen.ag/fileadmin/hq/survey/studie_deutscher_industrie_4_0_ index_2015_150907.pdf, Abrufdatum: 30.06.2017.
[25] Weiß, Y. M.-Y.; Wagner, D. J. Die Zukunft der Arbeitswelt: Arbeiten 4.0. In: Jochmann, W.; Böckenholt, I.; Diestel, S. (Hrsg): HR-Exzellenz. Wiesbaden 2017, S. 203-217.
[26] von See, B.; Kersten, W.: Digitale Transformation des Arbeitsumfelds. Identifikation und Analyse von Handlungsfeldern in Unternehmen am Beispiel der Logistik. In: Gronau, N. (Hrsg): Industrial Internet of Things in der Arbeits- und Betriebsorganisation. Berlin 2017.

Your downloads


You might also be interested in

Generative AI in Organizational Knowledge Management

Generative AI in Organizational Knowledge Management

AI literacy as a prerequisite for augmentation
Uta Wilkens ORCID Icon, Valentin Langholf ORCID Icon, Niklas Obermann ORCID Icon
Generative Artificial Intelligence (GenAI) offers new opportunities for organizational knowledge management, particularly when it comes to learning processes at the interface between explicit, firm-specific, and tacit knowledge. Its use is therefore of particular interest for application areas such as industrial maintenance. Based on a mechanical engineering case study, this article demonstrates that augmenting both processes and employees with GenAI requires AI literacy combined with professional skills.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 118-126 | DOI 10.30844/I4SE.26.5.14
Foundation Models in Industrial Robotics

Foundation Models in Industrial Robotics

Requirements for AI-supported assistance in production and logistics
Bernd Kuhlenkötter ORCID Icon, Daniel Syniawa ORCID Icon
Foundation models are increasingly changing the way robots are used in industry. Instead of writing complex programs line by line, programmers will soon be able to collaborate more closely with AI systems, describe tasks, and review generated solutions. This shifts their role from purely generating code to conceptual, supervisory, and validation activities. This article highlights the new possibilities that large AI models create for robot programming and the changes they entail for work and required skills in industrial practice.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 16-23 | DOI 10.30844/I4SE.26.5.2
AI-Driven Organization as a New Work Paradigm

AI-Driven Organization as a New Work Paradigm

Implications for individual and organizational change
Katharina Hölzle ORCID Icon, Leonie Krauch, Wolfgang Beinhauer ORCID Icon, Carsten Schmidt, Josephine Hofmann ORCID Icon
Is it sufficient to train employees in the use of AI tools, or does the AI organization require an entirely new set of competencies? This paper introduces a digital enablement model comprising three competency dimensions and demonstrates, through an upskilling program implemented at the Fraunhofer Institute for Industrial Engineering IAO, how organizations can sustainably bridge the AI adoption gap.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 94-100 | DOI 10.30844/I4SE.26.5.11
Designing Effective AI Certification

Designing Effective AI Certification

Insights from established certification domains for the standardization of human-centered AI
Katharina Morsch ORCID Icon
Certification for human-centered AI is becoming increasingly important—as a source of competitive differentiation, a response to regulatory expectations, and a mechanism for fostering human-centered work environments. But how can organizations determine whether the underlying requirements are truly embedded in practice? Drawing on expert interviews from established certification domains, this paper shows that the decisive question is answered not during the audit itself, but in the period between audit cycles. Ultimately, it is not the certificate that matters, but the commitment of organizational leadership and the organization as a whole to engage seriously in the certification process and its continuous implementation.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 86-92 | DOI 10.30844/I4SE.26.4.10
Data-Driven Supplier Selection in Industry 4.0

Data-Driven Supplier Selection in Industry 4.0

Towards an industrial platform for selection and configuration
Purushothaman Ganesh ORCID Icon, Baris E. Albayrak ORCID Icon, Jörg Franke ORCID Icon, Till Sindel ORCID Icon, Jens Fürst, Sebastian Lang ORCID Icon
Industrial supply chains must repeatedly reconfigure sourcing strategies in response to disruptions, yet supplier capability information remains heterogeneous and difficult to operationalize. Existing research addresses supplier selection, simulation, and interoperability standards separately, treating discovery, evaluation, and optimization as disconnected steps requiring manual data transformation. This paper presents a framework for a data-driven industrial platform integrating three components: Asset Administration Shell-based supplier profiles structured by an Actor-Service ontology for semantic discovery, automatic discrete-event simulation model generation from layout data for performance evaluation, and KPI-driven configuration using optimization algorithms. The main contributions are an end-to-end interoperable workflow, a two-tier supplier profile concept separating semantic descriptions from simulation parameters, and a standardized KPI interface maintaining consistency ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 4 | Pages 50-61 | DOI 10.30844/I4SE.26.4.6
Open Source as Enabler for Smart Data Ecosystems

Open Source as Enabler for Smart Data Ecosystems

How collaboration shapes sovereignty and interoperability across industrial applications
Anna Maria Schleimer ORCID Icon, Julia Pampus ORCID Icon
From automotive supply chains to smart factories, data ecosystems promise seamless collaboration across company boundaries. But how can industries build the necessary infrastructure without creating new dependencies? This article explores how open-source software can serve as a foundation and impactful tool for sovereign, interoperable technologies and standards. Yet, open-source software is not a silver bullet; rather, it poses challenges for digital sovereignty in burgeoning data ecosystems.
Industry 4.0 Science | Volume 42 | 2026 | Edition 4 | Pages 6-13 | DOI 10.30844/I4SE.26.4.1