Simulation

The Smart Factory concept describes the extensively networked production of industry 4.0, which affects the entire life cycle of a factory and, in particular, factory planning and factory operation. Both classic and more up-to-date factory planning approaches come to their limits through the new requirements. This paper identifies the requirements that are important for the future structural planning of factories and presents the need for a holistic virtual validation of the factory structure. Furthermore, a methodological approach is addressed for the solution of the challenges.

Simulation is an accepted method within modern engineering processes across different industrial domains. Especially automation engineers use modelling and simulation methods within various project phases to guarantee high quality products that are produced on high quality plants. The necessity of modern simulation technology rises due to the latest trends regarding the virtualization of production plants. This article describes the need for standards within the field of modelling and simulation of mechatronic factories and machines using a digital twin.

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.

Industrial work 4.0 challenges workers due to ambiguity, self-organization, and interconnec-tedness. To qualify workers to successfully cope with these challenges, this article introduces the software-based situational learning factory that is completed like a flight simulator. By playing these so called serious games that simulate situations on the shop floor of varying complexity, employees gain experiential knowledge and improve their IT-skills.

Many companies are challenged to consider in the planning process for working times not only current laws, labour contracts and their operative goals but also the time-related wishes of their employees. One can identify different types of employees which differ in their working time preferences as well as in the work load they have to deal with in their private lifes. Therefore, the working time configuration has to take the specific situation of the employees into account. Consequently, the process of the working time configuration becomes highly complex. But there are currently no tools available that allow decision-makers of a company to evaluate an intended working time model prospectively (i.e. before its realization) while taking dynamic aspects of their operations as well as the work-life balance of the employees into account. At best, the work-life balance of the employees is a key figure for the evaluation of a working time model but not a parameter during the planning ...

The fourth industrial revolution promises a further automation of process control by cyber-physical systems. The individual products gain the ability for controlling their production and logistics themselves. By coordinating themselves they can jointly achieve business objectives. This logical decomposition reduces the complexity of cross-company process control significantly. Thus, even exceptions on short notice can be dealt with in real-time. In operation, the required artificial intelligence will usually not be implemented on the active objects. Instead, adequate software platforms for the so-called Industry 4.0 are required. This article investigates the requirements for such platforms and describes how they can be implemented.

Globalization and the growing number of supply chain participants lead to increasing cargo transport service and, thus, to higher energy demand. Although energy prices increase at the same time, the energy balance of production networks remains unconsidered during in the network design phase. Hence, the research project E²Log analyzes how logistics networks and production environment can be coordinated in order to improve energy efficiency. In the first project phase, based on the supply chain for the production of the Volkswagen Amarok, the use case partners have derived simulation scenarios and enhanced a simulation tool to evaluate measures for the efficiency increase without ignoring classic logistic objectives soon.

Handling complexity is one of the key topics of producing enterprises worldwide. It can be noted that enterprises are indeed able to coop with the complexity of single processes due to a function-oriented organisation, yet the same organisational paradigm leads to local objective systems that in many cases are contrary to the global goals of the enterprise. The same problem is reflected and supported by the enterprise’s IT systems. Thus, in this paper we present an approach to convert the inflexible function oriented operations into comprehensive processes by introducing special IT systems, so-called logistic assistance systems. To illustrate the approach we introduce the problem of integrated order sequencing and transport planning in the automotive industry and present a prototypical solution.

In view of the demographic changes, questions arise about the long-term development of the performance of work systems with an aging workforce. These questions are especially urgent if work systems with predominantly mental demands should be operated for many years. The simulation tool ESPE-AS can help to forecast the development of the performance of a work system with the given workforce for future periods. This paper describes a first approach of this simulation-based planning method and illustrates it using the routine activities for creating operations plans.

Due to dynamics and complexity within production and delivery networks, customer demands are often highly volatile. In order to achieve a well-founded production planning and control, future customer demands have to be predicted precisely. Classical statistical forecasting methods are often easy to apply but are not able to react on dynamic effects within the data. Methods of nonlinear dynamics consider qualitative in addition to quantitative information within past order data to find possible deterministic structures and, as a result, to achieve better forecasts of the future. This article deals with the development of a data base containing recommendations to choose suitable prediction methods in different situations.