Data-based identification of knowledge transfer needs in global production networks

Manufacturing companies’ value chains are increasingly distributed globally, which presents companies with the challenge of coordinating complex production networks. In general, these production networks grew historically rather than having been continuously planned, leading to heterogeneous production structures with many tangible and intangible flows to be coordinated. Thereby, many authors claim that the knowledge flow is one of the most important flows and the source of competitive advantage. However, today’s managers face major challenges in transferring production knowledge, especially across globally distributed production sites. The first obstacle to a successful knowledge transfer is to identify what kind of knowledge should be transferred between whom and at what time. This process can take months of information collection and evaluation and is often too time-consuming and costly. Thus, this paper presents an approach to automatically identify at what point knowledge should be transferred. In order to achieve this, the company's raw data is being used to identify which employees work on similar production processes and how these processes perform. Therefore, production processes, which can be compared with each other, need to be formed, even though these processes may be performed at different production sites. Still, not every defined cluster of production processes necessarily requires the initiation of knowledge transfer since performing a knowledge transfer always entails considerable effort and some processes might already be aligned with each other. Consequently, in a next step it is analyzed how these comparable production processes differ from each other by taking into account their performances by means of feedback data. As a result, trigger points for knowledge transfer initiation can be determined

Strain Measurement in Aluminium Alloy during the Solidification Process Using Embedded Fibre Bragg Gratings

In recent years, the observation of the behaviour of components during the production process and over their life cycle is of increasing importance. Structural health monitoring, for example of carbon composites, is state-of-the-art research. The usage of Fibre Bragg Gratings (FBGs) in this field is of major advantage. Another possible area of application is in foundries. The internal state of melts during the solidification process is of particular interest. By using embedded FBGs, temperature and stress can be monitored during the process. In this work, FBGs were embedded in aluminium alloys in order to observe the occurring strain. Two different FBG positions were chosen in the mould in order to compare its dependence. It was shown that FBGs can withstand the solidification process, although a compression in the range of one percent was measured, which is in agreement with the literature value. Furthermore, different lengths of the gratings were applied, and it was shown that shorter gratings result in more accurate measurements. The obtained results prove that FBGs are applicable as sensors for temperatures up to 740 °C