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Die Bedeutung der Ressource Wissen für die Unternehmensentwicklung ist heutzutage unumstritten. Um wettbewerbsfähig bleiben zu können, müssen Unternehmen die Erzeugung, Teilung und systematische Nutzung von Wissen fördern. Dabei stehen sowohl die individuelle Wissensbasis (und damit jeder Mitarbeiter) als auch die kollektive Wissensbasis (und damit das ganze Unternehmen) im Vordergrund. Der Faktor Kultur gewinnt in diesem Zusammenhang zunehmend an Bedeutung: Er beeinflusst alle drei Ebenen des Wissensmanagements - Mensch, Organisation und Technologie. Neben den Besonderheiten der Organisationskultur und der Kultur unterschiedlicher Mitarbeitergruppen sind in international agierenden Unternehmen auch die spezifischen Merkmale der jeweiligen Landeskultur zu berücksichtigen. Gemeinsam mit dem Lehrstuhl für Wirtschaftsinformatik und Electronic Government der Universität Potsdam hat acatech im Mai 2008 einen Workshop mit Vertretern aus Wirtschaft, Politik und Wissenschaft zum Umgang mit Wissen im interkulturellen Vergleich veranstaltet. Vor diesem Hintergrund entstand der acatech diskutiert-Band "Umgang mit Wissen im interkulturellen Vergleich - Beiträge aus Forschung und Unternehmenspraxis". Darin enthalten sind Beiträge, die u. a. danach fragen, welche wechselseitigen Beziehungen zwischen Technik und Kultur bestehen, inwieweit Experten- und Wissensnetzwerke als interkulturelles Instrument zum Umgang mit Wissen geeignet sind, wie Unternehmen ihre Mitarbeiter auf Auslandseinsätze und die Begegnung mit fremden Kulturen vorbereiten können und welche Rolle Kommunikation als Methode des Wissenstransfers spielt.
A growing number of business processes can be characterized as knowledge-intensive. The ability to speed up the transfer of knowledge between any kind of knowledge carriers in business processes with AR techniques can lead to a huge competitive advantage, for instance in manufacturing. This includes the transfer of person-bound knowledge as well as externalized knowledge of physical and virtual objects. The contribution builds on a time-dependent knowledge transfer model and conceptualizes an adaptable, AR-based application. Having the intention to accelerate the speed of knowledge transfers between a manufacturer and an information system, empirical results of an experimentation show the validity of this approach. For the first time, it will be possible to discover how to improve the transfer among knowledge carriers of an organization with knowledge-driven information systems (KDIS). Within an experiment setting, the paper shows how to improve the quantitative effects regarding the quality and amount of time needed for an example manufacturing process realization by an adaptable KDIS.
Time to change
(2020)
Industry 4.0 leads to a radical change that is progressing incrementally. The new information and communication technologies provide many conceivable opportunities for their application in the context of sustainable corporate management. The combination of new digital technologies with the ecological and social goals of companies offers a multitude of unimagined potentials and challenges. Although companies already see the need for action, there was in the past and currently still is a lack of concrete measures that lever the potential of Industry 4.0 for sustainability management. During the course of this position paper we develop six theses (two from each sustainability perspective) against the background of the current situation in research and practice, and policy.
Industry 4.0, i.e. the connection of cyber-physical systems via the Internet in production and logistics, leads to considerable changes in the socio-technical system of the factory. The effects range from a considerable need for further training, which is exacerbated by the current shortage of skilled workers, to an opening of the previously inaccessible boundaries of the factory to third-party access, an increasing merging of office IT and manufacturing IT, and a new understanding of what machines can do with their data. This results in new requirements for the modeling, analysis and design of information processing and performance mapping business processes.
In the past, procedures were developed under the name of “process-oriented knowledge management” with which the exchange and use of knowledge in business processes could be represented, analyzed and improved. However, these approaches were limited to the office environment. A method that makes it possible to document, analyze and jointly optimize the new possibilities of knowledge processing by using artificial intelligence and machine learning in production and logistics in the same way and in a manner compatible with the approach in the office environment does not exist so far. The extension of the modeling language KMDL, which is described in this paper, will contribute to close this research gap.
This paper describes first approaches for an analysis and design method for a knowledge management integrating man and machine in the age of Industry 4.0.
Since more and more business tasks are enabled by Artificial Intelligence (AI)-based techniques, the number of knowledge-intensive tasks increase as trivial tasks can be automated and non-trivial tasks demand human-machine interactions. With this, challenges regarding the management of knowledge workers and machines rise [9]. Furthermore, knowledge workers experience time pressure, which can lead to a decrease in output quality. Artificial Intelligence-based systems (AIS) have the potential to assist human workers in knowledge-intensive work. By providing a domain-specific language, contextual and situational awareness as well as their process embedding can be specified, which enables the management of human and AIS to ease knowledge transfer in a way that process time, cost and quality are improved significantly. This contribution outlines a framework to designing these systems and accounts for their implementation.
Faced with the triad of time-cost-quality, the realization of knowledge-intensive tasks at economic conditions is not trivial. Since the number of knowledge-intensive processes is increasing more and more nowadays, the efficient design of knowledge transfers at business processes as well as the target-oriented improvement of them is essential, so that process outcomes satisfy high quality criteria and economic requirements. This particularly challenges knowledge management, aiming for the assignment of ideal manifestations of influence factors on knowledge transfers to a certain task. Faced with first attempts of knowledge transfer-based process improvements [1], this paper continues research about the quantitative examination of knowledge transfers and presents a ready-to-go experiment design that is able to examine quality of knowledge transfers empirically and is suitable to examine knowledge transfers on a quantitative level. Its use is proven by the example of four influence factors, which namely are stickiness, complexity, competence and time pressure.
From employee to expert
(2021)
In the context of the collaborative project Ageing-appropriate, process-oriented and interactive further training in SME (API-KMU), innovative solutions for the challenges of demographic change and digitalisation are being developed for SMEs. To this end, an approach to age-appropriate training will be designed with the help of AR technology. In times of the corona pandemic, a special research design is necessary for the initial survey of the current state in the companies, which will be systematically elaborated in this paper. The results of the previous methodological considerations illustrate the necessity of a mix of methods to generate a deeper insight into the work processes. Video-based retrospective interviews seem to be a suitable instrument to adequately capture the employees' interpretative perspectives on their work activities. In conclusion, the paper identifies specific challenges, such as creating acceptance among employees, open questions, e.g., how a transfer or generalization of the results can succeed, and hypotheses that will have to be tested in the further course of the research process.