Autor: Hermann Lödding

Analyzing and optimizing labor productivity is an important task in productions with high personnel costs. Existing methods allow to identify potentials require a lot of effort. This article presents an automated approach to analyze labor productivity with 3D cameras. The developed method is based on the primary-secondary analysis.

Highly valuable goods are usually customized to the specific needs of the customer. It is therefore essential to integrate the customer in the important decisions during the engineering phase. This article introduces a Augmented Reality solution to design and plan variants on-site together with the customer. This solution was tested on a real example, a retrofit of an emission reducing technology on a ship.

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.

The competitiveness of manufacturing companies producing in high-wage countries is strongly depending on labour productivity. In the course of Germany’s demographic change, companies with manual production need to compare the heterogeneous impact factors on labour productivity to develop goal-oriented measures for productivity improvements. The integral productivity analysis enables the user to identify the various impacts on labour productivity and to prioritise fields for process optimisation. The approach is based on the state-oriented modelling of worker activities.

Technologies such as Digital Mock Up (DMU) or Virtual Reality (VR) help to expose and fix design errors at an early stage in the product development process. Nevertheless the discovery of these potential problems is still a common obstacle. The approach introduced within this paper improves the existing virtual product validation method by using the company’s existing knowledge of former projects. The DMU-Cockpit integrates the usually spread experience of known errors in one tool by modelling a relation between classified components and sources of error in a matrix. This allows the automatic selection of potential sources of error for a chosen part and derives the most valuable validation tasks of the knowledge base.