Humans in Industry 4.0

A process model for a practice-oriented analysis

JournalIndustrie 4.0 Management
Issue Volume 37, 2021, Edition 3, Pages 45-48
Open Accesshttps://doi.org/10.30844/I40M_21-3_S45-48
Bibliography Share Cite Download

Abstract

The development of Industry 4.0 changes the role of humans in operations systems. In sociotechnical systems, there is ongoing interaction between humans and technology, impacting human life and work. However, human factors are broadly ignored in research on Industry 4.0 technologies and implementation. In this work, a process model is described that supports the evaluation of the impact of a technology implementation on human factors and performance indicators. This can avoid negative consequences for employees as well as phantom profits and can contribute to a successful digital transformation.

Keywords


Bibliography

[1] Lu, Y., Industry 4.0: A survey on technologies, applications and open research issues. In: Journal of Industrial Information Integration 6 (2017), S. 1-10.
[2] Kagermann, H.; Wahlster, W.; Helbig, J.: Recommendations for implementing the strategic initiative Industrie 4.0, Securing the future of German manufacturing industry, Final report of the Industrie 4.0 Working Group. Berlin 2013.
[3] Neumann, W. P. u. a.: Industry 4.0 and the human factor – A systems framework and analysis methodology for successful development. In: International Journal of Production Economics 233 (2021), 107992.
[4] Romero, D. u. a.: Towards an Operator 4.0 Typology: A Human-Centric Perspective on the Fourth Industrial Revolution Technologies. In: Proceedings of the International Conference on Computers and Industrial Engineering. Tianjin, China 2016.
[5] Rohleder, B.: Digitalisierung der Logistik. Berlin 2019.
[6] Brynjolfsson, E.; McAfee, A.: The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York 2014.
[7] Rose, L. M.; Orrenius, U. E.; Neumann, W. P.: Work environment and the bottom line: Survey of tools relating work environment to business results. In: Human Factors and Ergonomics in Manufacturing & Service Industries 23 (2013) 5, S. 368-381.
[8] Breque, M.; De Nul, L.; Petridis, A.: Industry 5.0 – Towards a sustainable, human-centric and resilient European industry, European Union. Bruxelles 2021.
[9] Neumann, W. P.; Dul, J.: Human Factors: Spanning the Gap between OM & HRM. In: International Journal of Operations & Production Management 30 (2010) 9, S. 923-950.
[10] Shipton, H. J. u. a.: When promoting positive feelings pays: Aggregate job satisfaction, work design features, and innovation in manufacturing organizations. In: European Journal of Work and Organizational Psychology 15 (2006) 4, S. 404-430.
[11] Russell-Walling, E.: Scientific Management, in 50 Schlüsselideen Management. Heidelberg 2011.
[12] Waschull, S. u. a.: Work design in future industrial production: Transforming towards cyber-physical systems. In: Computers & Industrial Engineering 139 (2020), 105679.
[13] Lüdtke, A.: Wege aus der Ironie in Richtung ernsthafter Automatisierung, In: Botthof, A.; Hartmann, E. (Hrsg.): Zukunft der Arbeit in Industrie 4.0. Heidelberg 2015, S. 125-146.
[14] Peissner, M.; Hipp, C.: Potenziale der Mensch-Technik-Interaktion für die effiziente und vernetzte Produktion von morgen. Stuttgart 2013.
[15] Wieland, R.: Digitalisierung und der Mensch. In: Forschungsmagazin der bergischen Universität Wuppertal BUW.OUTPUT 21 (2019), S. 6-11.
[16] Dregger, J. u. a.: Challenges for the future of industrial labor in manufacturing and logistics using the example of order picking systems. In: Procedia CIRP 67 (2018), S. 140-143.
[17] Reiman, A. u.a.: Human factors and ergonomics in manufacturing in the industry 4.0 context – A scoping review. In: Technology in Society, 65 (2021), 101572.
[18] Ulich, E.: Arbeitspsychologie. 7. Auflage, Stuttgart 2011.
[19] Sgarbossa, F. u. a.: Human factors in production and logistics systems of the future. In: Annual Reviews in Control 49 (2020), S. 295-305.
[20] Kadir, B. A.; Broberg, O.; Conceição, C. S. d.: Current research and future perspectives on human factors and ergonomics in Industry 4.0. In: Computers & Industrial Engineering 137 (2019), S. 1-12.

Your downloads


You might also be interested in

Serious Games as a Training Tool

Serious Games as a Training Tool

Game mechanics design to promote resilience
Annika Lange ORCID Icon, Thomas Knothe ORCID Icon
Unforeseen events are increasingly challenging manufacturing companies. Being resilient during crises is becoming a key competence. Serious games (SG) can help make resilience-building processes more transparent. This article derives specific requirements for SG from different phases of resilience and shows how these can be implemented in game mechanics in order to effectively support the training of resilience.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 98-104
MAKI—A Digital Assistant for Practice-Based Learning

MAKI—A Digital Assistant for Practice-Based Learning

Why every factory is a learning factory
Olaf Resch ORCID Icon
With the help of digital assistants, academic teaching is possible in any factory. In order to achieve the best learning effects, however, the interests of all stakeholders must be taken into account. The factory wishes to deploy its employees quickly and productively, the learners desire a positive learning experience, and the educators want to illustrate abstract concepts in a meaningful and practical way. The only way to combine all of these perspectives is via a well-thought-out educational concept and highly functioning technology.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 70-77
Learning Factories for the Future of Manufacturing in Brazil

Learning Factories for the Future of Manufacturing in Brazil

Advancing manufacturing through technology and skills development
Manufacturing firms in developing countries face challenges in closing productivity gaps while adopting Industry 4.0 technologies. Learning factories are one helpful approach to countering these challenges. One such example is the learning factory Fábrica do Futuroin São Paulo, Brazil, which has engaged students, supported competence development, and collaborated with industry in applied research, functioning as a hub for advanced manufacturing initiatives.
Serious Gaming and the Energy Transition

Serious Gaming and the Energy Transition

Collaborative knowledge generation and interactive understanding of complex interrelationships
Janine Gondolf ORCID Icon, Gert Mehlmann, Jörn Hartung, Bernd Schweinshaut, Anne Bauer
Conveying the complexity and multifaceted nature of the energy transition to a broad audience is a challenge. This article demonstrates how interactive serious games on a multitouch table can help make connections tangible and comprehensible. The games and the table were used in various conversational contexts. These are presented here in three case vignettes based on participant observation of the different applications, as well as situated and shared reflection. The vignettes demonstrate how interaction can trigger epistemic processes, enable shifts in perspective, and foster collective thinking, all of which are necessary for shaping the future of society as a whole.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 62-69
Industrial Transformation via a Machining Learning Factory

Industrial Transformation via a Machining Learning Factory

A learning module to foster competencies for a sustainability-driven transformation
Oskay Ozen ORCID Icon, Victoria Breidling ORCID Icon, Stefan Seyfried ORCID Icon, Matthias Weigold
Sustainability-enhancing transformation processes are necessary in all sectors if we are to remain within planetary boundaries. This also applies to the industrial sector as a significant emitter of greenhouse gases. Employees need new competencies to master this complex task of industrial transformation. These range from CO2 equivalents accounting to the development and evaluation of transformation scenarios, including technical measures. The learning module developed here addresses these competency requirements and uses the example of the ETA factory to show how a competency-oriented learning module for industrial transformation can be structured. It essentially comprises four phases: data collection and CO2 equivalents accounting, cause analysis, development of measures and evaluation of measures.
Industry 4.0 Science | Volume 42 | Edition 2 | Pages 38-47 | DOI 10.30844/I4SE.26.2.38
Experiencing Digital Twins in Production and Logistics

Experiencing Digital Twins in Production and Logistics

The fischertechnik® Learning Factory 4.0 as a development platform for possible expansion stages
Deike Gliem ORCID Icon, Sigrid Wenzel ORCID Icon, Jan Schickram, Tareq Albeesh
The fischertechnik® Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments.
Industry 4.0 Science | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844/I4SE.26.2.30