Autor: Nico Hanenkamp

In factory operations, measuring equipment is often used without automatic storage or further processing possibilities of the measured value. In this case, employees must capture and process the measured values manually. In this article, an approach for the optical detection and digitization of measured values with the help of machine learning methods is presented. This aims to reduce the workload of the employees, avoid reading errors and enable automated documentation.

Due to competitive pressure pharmaceutical companies are also driven to increase the efficiency of their processes. In this paper an approach for the predictive detection of malfunctions of filling systems for powdery pharmaceutical products using machine learning is presented. The focus is on the prediction of filling deviations with recurrent neural networks, with the objective to detect a drift in the process stability to intervene accordingly.

Autonomous transportation systems represent a significant use case for the implementation of industry 4.0. The transparency of logistics parameters, especially the replenishment time, can be increased and controlled efficiently compared to conventional material supply based on milkrun concepts. This article describes a systematic approach to ensure the process capability of the replenishment time for logistics processes based on autonomous transportation systems applying a six sigma approach. The case study has been performed in a real production environment in the automotive supplier industry.

Facing shorter product life cycles and ever changing customer requirements, production ramp-up processes are becoming one of the most decisive factors to stay in the market. In contrast to the automotive industry, where large scale information systems (IS) and simulation tools allow the build up of digital factories, small and medium sized enterprises (SME) lack resources to satisfy the corresponding organisational and IS-specific requirements. The focus of the research project ELAN is to develop an overall approach to manage production ramp-up adapted to the needs of SME.

production management is crucial to reduce time-to-market. Regarding ever-changing dynamic requirements that production systems have to meet there is a need of cooperative development and planning models, methods and tools. By that transparency in process organizations can be achieved as well as the availability of resources and competencies can be verified. The article de-scribes a resource-oriented collaborative engineering model to allow short and long term (re-)planning of production systems.

In a highly competitive environment characterized by decreasing life cycles, lower profit margins and a stronger individualization of products enterprises are forced to adopt new strategies to stand the challenges of the market. The article describes the framework of an integrated complexity management including methods, models and information technology for the identification, the control, the reduction and the prevention of activities without any productivity in direct and indirect sectors of the supply chain.