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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.
As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can “forget” their present specialization.
Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors.
Business processes are regularly modified either to capture requirements from the organization’s environment or due to internal optimization and restructuring. Implementing the changes into the individual work routines is aided by change management tools. These tools aim at the acceptance of the process by and empowerment of the process executor. They cover a wide range of general factors and seldom accurately address the changes in task execution and sequence. Furthermore, change is only framed as a learning activity, while most obstacles to change arise from the inability to unlearn or forget behavioural patterns one is acquainted with. Therefore, this paper aims to develop and demonstrate a notation to capture changes in business processes and identify elements that are likely to present obstacles during change. It connects existing research from changes in work routines and psychological insights from unlearning and intentional forgetting to the BPM domain. The results contribute to more transparency in business process models regarding knowledge changes. They provide better means to understand the dynamics and barriers of change processes.