Autor: Abderrahim Ait Alla

While the digital world has already made its way into many other production areas, the bakery industry has so far benefited little from these technological developments. This is because many baking processes are manually controlled and rely on expert knowledge. In addition, the process data from the machines is still not automatically captured via sensors. This paper describes a procedure for digitizing baking processes by developing an IoT framework consisting of an IoT device including measurement methods, an edge gateway, and a simulation-based solution for process optimization.

New production technologies have inherent technical risks, but also established production technologies can not always be transferred to new products or components without problems. Accordingly, these technical risks must be taken into account when planning production systems. This paper presents an approach for the optimized allocation of development resources with regard to the technical risks of production technologies and processes. First, the production scenario is modeled and simulated using GRAMOSA. Then the simulation results are used for the optimized assignment of the development budget by means of mathematical optimization.

Optical systems are a popular choice for quality inspection because they are not only contactless and precise but also comparably fast. Particularly in cases where a 100% quality inspection is required low measurement and evaluation time is of essential importance. With high production rates of several parts per second, manual inspection is not feasible anymore and the evaluation needs to be automatically carried out by algorithms. In this article we propose a fast method for anomaly detection in image data based on principal component analysis and filtering. The method shows competitive performance on a data set of challenging surface inspection.

The comprehensive digitalization of work environment constitutes major challenges in connection with new media and an extended human-computer interaction. In this context, service and maintenance proved to be an important field of application for this form of Industry 4.0. An adequate process execution and documentation has to be secured, even under challenging conditions, e.g. malfunctions on short notice, harsh working conditions or tight timeframes. These requirements as well as the increasing digitalization promote the application of Augmented Reality solutions in this area. With a focus on maintenance of wind energy turbines, this article describes the state of the art in relation to current solution approaches and development needs in connection with a process-oriented application of Augmented Reality. Therefore, current solutions that are already on the market are considered as well as results of the joint research project “AR Maintenance System”.

Wind energy has become an established technology for the generation of electricity from renewable energies. However, suitable sites for the installation of onshore wind turbines are limited or often already developed. Consequently, offshore wind energy will play a key role in the future of renewable energy production. Especially in implementing the turnaround of energy policy. The installation of offshore wind farms is comparatively complex and expensive due to the harsh weather conditions and the limited availability of resources. Therefore, new concepts for the construction of offshore wind parks have to be developed and evaluated. However, classical planning tools reach their limits due to such complex problems. This gap can be closed by means of a process simulation.

The importance of data science for production and logistics continues to grow because more data are available due to Industry 4.0-Applications used for process and system optimization. In addition, the improved methods and tools for data analysis enable an easier processing of application-specific issues. This article is the second part relating to data science in production and logistics. While the first article dealt with the definition of terms and the potential of data analysis, the article at hand is dedicated to the application of data science in production and logistics by means of various application examples.

The implementation of industry 4.0 concepts requires a new understanding of data processing and analysis. Data Science integrates approaches of mathematical modelling and performant implementation to analyse data of specific application areas. Within this first article, the basics of Data Science are presented and perspectives for a data-driven production and logistics are discussed. Within a second article in a following edition, the process steps for structured data analysis will be explained and illustrated by means of application examples.

This paper deals with forecasting of single period products taking the apparel industry as an example. Due to the resulting shortcomings of conventional forecasting methods, i.e. the statistical methods, and subjective estimates, the improvement of sales forecasting using a community-based approach is proposed. A platform for a professional community can be created by cloud computing tools.