one-of-a-kind production

In the production and operation of complex, one- of-a-kind products, disruptions inevitably occur. In practice, there are often deficits in terms of transparency and information flow when it comes to disruption management. Digital assistance systems facilitate disruption documentation: they increase the quality of information by locating it in the CAD model and in the overall product plan, thus accelerating targeted fault elimination. A generic data model makes it possible to use digital assistance systems for different products, trades and processes and in different product life phases. (Only in German)

One-of-a-kind or small batch manufacturer can only derive process times from previous projects to a limited extent for the planning of customer order and component-specific projects and transfer these to new projects. This is due to the fact that the process times depend on influencing parameters that are directly related to the respective component. As part of a research project, these influencing parameters to the duration for logistic processes are examined. The identification of the influencing parameters using a Delphi Study is explained in this article.

The one-of-a-kind production often is characterized by a high share in manual work. Information gathering requires an essential percentage of the workers time. The article shows as Augmented Reality can help to reduce the efforts of information gathering and to increase productivity.

Traditional productivity analysis has emerged in mass production and cannot be adopted one-to-one on One-of-a-kind production (OKP). Due to the non-repetitive character of the processes in OKP, productivity improvements do not reproduce like in mass production. In addition, preparatory activities such as orientation, material handling and positioning usually consume a lot more time than the actual value-adding activities in OKP. Therefore, OKP requires analysis methods that deliver: 1) a generic working cycle to enable repetitive productivity improvements; 2) activities of personnel in production processes, which include the preparatory activities. We introduce a state-oriented approach for productivity measurement in OKP. With a case study we show how to capture, visualize and evaluate state data of an OKP.