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Lean Management ist eine weit verbreitete Methode zur Optimierung der gesamten Wertschöpfungskette industrieller Güter. Die hier untersuchte Methode ergänzt die bestehenden Kennzahlen um einen ökologischen Aspekt und hebt bislang verborgene Potenziale in ungeahnter Höhe. Kunden profitieren von reduzierten Energiekosten und können bereits für anstehende ökologische Richtlinien, zum Beispiel einen PCF (Product Carbon Footprint), entscheidende Vorleistungen treffen.
In recent years, machine learning algorithms have made a huge development in performance and applicability in industry and especially maintenance. Their application enables predictive maintenance and thus offers efficiency increases. However, a successful implementation of such solutions still requires high effort in data preparation to obtain the right information, interdisciplinarity in teams as well as a good communication to employees. Here, small and medium sized enterprises (SME) often lack in experience, competence and capacity. This paper presents a systematic and practice-oriented method for an implementation of machine learning solutions for predictive maintenance in SME, which has already been validated.
Bisher sind die Auswirkungen der Digitalisierung auf das Qualitätsmanagement kaum diskutiert worden. Nun wurden im Rahmen einer Expertenbefragung die wichtigsten Chancen und Risiken identifiziert. Eine zentrale Anforderung dabei ist ein konsequentes „Right First Time“, für dessen Umsetzung das präventive Qualitätsmanagement eine
entscheidende Rolle spielt.
Die Liebherr Hydraulikbagger GmbH setzt sich aktiv mit der Implementierung von Risikomanagementsystemen auseinander und treibt so das risikoorientierte Prozessmanagement weiter voran. Dabei gilt es vor allem, Prozesse auf Risiko-Anfälligkeiten und ihre Relevanz für den Unternehmenserfolg zu analysieren.
Process risks are omnipresent in the corporate world and repeatedly present organizations with the challenge of how to deal with these risks. Efforts in trying to analyze and prevent these risks are costly and require many resources, which do not always bring the desired added value. The goal of this work is to determine how a benefit-oriented resource allocation can be made for risk-oriented process management. For this purpose, the following research question is posed: "How can systematic prioritization decisions regarding risk-oriented process management be made?” To answer it, an evaluation procedure is developed which assesses processes based on their characteristics regarding potential risk disposition as well as entrepreneurial relevance. For this purpose, requirements for such a procedure are first collected and used to define selection criteria for it. After the detailed analysis of known selection and evaluation procedures, one of them is selected and used for further development. Next steps include the definition of relevant criteria for the evaluation of the processes by examining process characteristics regarding their suitability for process evaluation. The focus here lies on characteristics that provide indications of the risk disposition and business relevance of processes. The result of this approach is a scoring model with a criteria catalog consisting of 15 criteria according to which a process is evaluated. The evaluation result is presented both numerically and in a matrix. This enables the comparison of several processes and a derived prioritization of those for a more in-depth risk analysis. The application of this approach will ensure a benefit-oriented allocation of resources in the management of process risks and increased process reliability.