Titlebook: Lean Management, Kaizen, Kata and Keiretsu; Best-Practice Exampl Marc Helmold,Ayşe Küçük Yılmaz,Tracy Dathe Book 2022 The Editor(s) (if app

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,Kaizen (改善), Kaikaku (改革) and Innovation Management,a production system, usually less than 20 or 30% in a given period of time (Radharmanan et al., 1996). Opposite of Kaizen is Kaikaku, which represents drastic change or radical improvement that is usually a result of big investments in technology and/or equipment (Imai, 1986). This way of making imp

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,Keiretsu Networks (系列 ネットワーク),st of a multi-layered system of suppliers focused on the core company (Gabrowiecki, 2006). Keiretsu networks have received much attention in the European automotive and transportation sector through the success of Japanese companies like Toyota, Mitsubishi or Hitachi and other conglomerates in achie

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,Kyouiku (教育) – The Learning Enterprise,nt processes in order to adapt the knowledge base and scope for action to the new needs. This is based on an open and individualized organization that allows and supports innovative problem-solving. Mechanisms that support such learning processes are the following:

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,5S Concept: Muda (無駄), Muri (無理) and Mura (斑),k (Liker, 2004). Enterprise must target value-added processes and eliminate or reduce waste, whereby waste can be visible (obvious) or invisible (hidden), as shown in Fig. 8.1 (Lehmann, 2021). The main idea of lean management is about highlighting the things that add value by reducing or eliminating

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Industry 4.0 and Artificial Intelligence (AI) in Lean Management,g. 10.1). Industry 4.0 is a name given to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things, cloud computing and cognitive computing. Industry 4.0 is commonly referred to as the fourth industrial revolution (Gi

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Analytical and Operations Research Methods and Techniques in Lean Management,ed for business transformation, i.e. investing in advanced information technologies and systems and, above all, in finding staff who have the necessary competencies to discover the hidden potential in data and to invest in the existing intellectual capital in gaining analytics and operations researc

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