In collaborative product development, diverse stakeholders are involved in distributed engineering activities. This situation makes it difficult to ensure, manage, and improve the quality across company boundaries. Therefore, this work determines the characteristics of collaborative engineering which have an influence on the quality of distributed product development. Several interoperability frameworks were analyzed in order to get insights into key areas for collaboration design. Furthermore, a systematic literature review provided the best practices for improvement efforts. The derived quality attributes were condensed and adapted to collaborative product development in the four key areas of organization and processes, data/artifacts, information technology systems and infrastructure, and social factors. This enables product developers to examine their collaborative engineering environment and to identify room for improvement and to enhance quality. A case example of an engineering change order shows a collaborative data flow process, in which the quality attributes may indicate improvement measures.

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Kurzfassung In der Produktentwicklung sind Reifegradmodelle ein geeignetes Instrument zur Bewertung und Verbesserung der Entwicklungsumgebung. Die folgende Literaturanalyse untersucht Unterschiede und Gemeinsamkeiten von bestehenden Reifegradmodellen für die Produktentwicklung und analysiert diese hinsichtlich ihrer Themen- und Prozessgebiete, sogenannter Handlungsfelder, in denen jeweilige Qualitätsmerkmale für eine leistungsfähige Produktentwicklung zu verorten sind.

The purpose of this paper is to define and describe an expert system for decision-support in further training management in small and medium-sized enterprises (SME). This will be achieved by evaluating the interrelation of the methodological-didactic characteristics of further training activities on the one hand and, on the other hand, linking them with individual learning preferences in order to recommend high-quality and individual measures when selecting continuing vocational training. The approach takes into account both the teaching-learning-arrangement and the learning style of a learner for algorithm-based decision support. For this purpose inherent characteristics of training activities were determined and interrelations identified by qualitative content analysis of educational literature and expert interviews among training managers of SMEs.

Additive manufacturing (AM) enables industries to accomplish mass customization by creating complex products in small batches. For this purpose, fused deposition modeling (FDM) is widely used in 3D printing where the material is applied layer-by-layer from a digital model to form a three-dimensional object. There still exist problems in FDM processes regarding the failure rate of printed parts. Failures vary from deformed geometry, clogged nozzles, and dimensional inaccuracies to small parts not being printed that may be attributed to various process steps (e.g., poor quality CAD models, converting issues, overheating, poor quality filament, etc.). The majority of these defects are preventable and are caused by imprudent try-and-error print processes and troubleshooting quality control. The aim of this chapter is to propose a quality gate reference process with defined requirement criteria to prevent the occurrence of defects. The framework shall be applied in quality control and in-situ process monitoring to enhance overall manufacturing quality.