Production Planning

EU regulations demand more intensive and transparent sustainable practices from companies. Industry needs to adapt many processes and products to take charge of this responsibility. Artificial Intelligence (AI) in particular offers innovative potential. Firstly, however, this technology needs to be evaluated focusing on weak AI—market-ready systems that perform specific tasks using algorithms and data-supported models efficiently.

The transformation in production offers the chance to redesign existing value chains. Cooperation between various ecological, social and governmental stakeholders is seen as particularly key to sustainable development. However, little research has been conducted into how companies can best manage the resulting interdependencies. Agriculture is used as an example to examine how businesses can activate resources along the value chain.

The double transformation describes the necessary change in the economy in the dimensions of ecology and digitalization. Motion capture systems offer new possibilities for recording and analyzing work processes in industrial assembly. They visualize motion sequences with high frequency, precision and resolution. The question therefore arises as to how the technology can be used in the context of digital transformation to further develop the analysis of work processes and the design of workplaces. Our article discusses this on the basis of solution principles and describes implementation principles for the development of upcoming digital assistance systems.

Any networked information system that is used in the context of manufacturing and logistics in a factory can be referred to as factory software. This article describes six trends that will significantly influence the way software is used in factories in the near future. The trends are described in ascending order in terms of significance of impact.

Simulation is frequently used for prediction of the outcome of adjustments in production systems. Real decision processes must be represented in the simulation. To achieve this, complex real decision processes have to be transferred into the simulation. This leads to a high effort for the creation of simulation models. This is resolved by the concept of iterative optimization-based simulation. Instead of transferring complex decision processes into the simulation, the predicted parameters are exported and existing decision processes determine a solution.

The large number of systems offered on the market makes a well-founded selection process necessary from the requirement survey to the final system selection. A comprehensive model that systematically supports and simplifies this process is the subject of this two-part article. The methodology goes beyond a questionnaire-based query and verifies system capabilities using structured case studies. The first part of the article [1] describes the process steps from the survey and collection of requirements of the customer to the system to their structuring in customer specifications. The present second part outlines the process steps of the system rough selection up to its fine selection. The individual selection steps are methodically supported by practical references as well as by the use of concrete tools. Using the described methodology selected systems can be objectively compared - a prerequisite for effective and efficient system selection for industrial companies.