Magnus Kandler, M.Sc.

The economically successful implementation of Industry 4.0 methods in industrialcompanies requires a structured introduction process. The main objective of such astructured implementation process is the case-specific analysis and evaluation ofavailable Industry 4.0 methods to select the most suitable ones for an individualcompany. The presented methodology aims to establish a financial, strategic, andbenefit-oriented evaluation approach for Industry 4.0 methods to assess their potential.The core of our methodology is a general Industry 4.0 toolbox providing a structuredclassification of different methods. In the first phase, a limited set of Industry 4.0methods is derived from the toolbox by classification into production typologies. In thesecond phase, all Industry 4.0 methods of the derived set are strategically assessed aswell as evaluated from a monetary perspective. The evaluation of the methods isbased on company-specific characteristics, its strategic focus, and its (market)environment. Based on this evaluation, we develop specific, value-addingimplementation scenarios. In the third phase, the identified implementation scenarioswith their specific order of prioritized methods are simulated in a System Dynamicsmodel. The three phases ultimately result in a recommendation for a company-specificIndustry 4.0 implementation roadmap

Mittels Industrie 4.0 und Digitalisierung werden dezentrale Entscheidungen und kurze Regelkreise möglich. Allerdings überwiegen in den Unternehmen starre, hierarchische Organisationsstrukturen, welche der Dezentralität komplementär entgegenstehen. Um zukünftig das Potenzial nutzen zu können, sind agile Strukturen und neue Führungssysteme in der Produktion erforderlich. Es bedarf eines Ansatzes, der ein digitales, agiles Management der Produktion ermöglicht und das Lean Leadership ergänzt.

Industry 4.0 is increasingly finding its way into production. New technologies and instruments are increasingly changing production processes and, in particular, production work. New instruments to support employees make it possible to increase labour productivity, which is a key factor contributing to economic growth. While at the beginning the benefits were unclear and the investment strategy was missing, today the lack of acceptance and unsuitable organizational structures in the companies are the biggest obstacles. This is reminiscent of the first failure of Lean Management. Only after several years with the anchoring of Lean principles as a philosophy and a sustainable improvement culture, Lean Management was successful. What is needed is an approach that involves employees in incremental process improvements using Industry 4.0 in order to support the acceptance. To develop this approach, the respective success factors of Lean Management and Industry 4.0 are first studied and then combined to form an integrated process optimization approach. The result is a human-centred optimization approach that promotes an increased acceptance of Industry 4.0 and supports employees in coping with future flexible work content.

A central challenge in the implementation of digitalisation and Industry 4.0 in companies is the human-centred development and design of the new technologies. These technologies have a major impact on the way people work and thus also on the motivation and satisfaction of employees. A thorough understanding of underlying drivers of employees' technology acceptance and possible resistance to change is crucial for a successful implementation of such technologies. Experimental economic research methods comprise a way to record the effects on human behaviour and working methods allowing for causal evidence. Controlled laboratory conditions offer the option to vary only individual variables and measure their influence on human behaviour, motivation, satisfaction, or working methods. In contrast to standard computer labs, learning factories offer the possibility to carry out experiments in a real production environment and thus observing behaviour in real work tasks in a realistic environment. This leads to increased external validity while at the same time the strict experimental protocol still allows making causal claims. Learning factories so far do not fulfil laboratory requirements. We first outline the prerequisites for the execution of empirical experiments. We then introduce our research concept, based on the example of the Learning Factory Global Production of wbk. The goal of this paper is to create an environment that allows collecting empirical and meaningful research data in a learning factory. Finally, we exemplify our concept with an experimental design on how digitalisation can foster decentralised decision-making. This research will inform a more human-centred design of digitalisation technologies and its effects on employee behaviour.

Die zunehmende Digitalisierung der Arbeitswelt stellt Führungskräfte vor neue Herausforderungen. Sie sind gefordert, den Wandel aktiv zu gestalten und dabei die Mitarbeitenden unter sich ständig verändernden Rahmenbedingungen situativ zu führen und zu motivieren. In diesem Fachbeitrag wird die Entwicklung eines Leitbildes vorgestellt, das Führungskräfte unterstützen soll, die notwendigen Rahmenbedingungen für gute Führung und soziale Interaktion in einer digitalisierten Arbeitswelt zu schaffen.