Open Access Articles

Carbon footprints are a widely discussed topic impacting the individuals as well as companies. A company can be transparent in their actions, by publishing a carbon footprint. These footprints can be calculated for a single product or the whole company. However, there is a variety of different carbon footprint standards. The internationally most recognized ones are the publicly available specification 2050, Greenhouse Gas protocol (2011) and ISO 14067. This paper compares the standards and gives a recommendation for the application of product carbon footprints.

Smart Factory is the vision of a production environment in which manufacturing plants and logistics systems organize themselves as far as possible without human intervention. The article describes a project, at the start of which none of the participants created a relation to “Smart Factory” or “Industry 4.0”. Rather, the objective was to drastically reduce the current delivery time of 6-8 weeks. The result is a completely digitized business process from order creation, product development, design, manufacturing as well as processing for “batch size 1” with a reduction in lead time to less than 10 %.

A successful digital transformation for attaining Industry 4.0, is a crucial success criterion for many companies today. The ongoing global COVID-19 pandemic has shown the need for digitalization in companies and has further accelerated this development. However, these times, companies are confronted with an uncertain order and profit situation. Thus, they need to allocate their investments purposefully. Evaluating the digital maturity by using a profound indicator system is therefore a sound basis for decision making. This paper develops such a sociotechnical KPI model along the dimensions “Strategy and Organizational Leadership”, “Digital Skills/Human Capital” as well as “Smart Process/Operations”. In the future, this model can be used for determining the digital maturity and thus, it can be applied for allocating digitalization investments.

The construction industry has taken first steps towards digitalized processes with the use of Building Information Modeling (BIM) systems and the modelling of processes. However, there are few successful examples of IT-supported processes in the largely manual construction phase. This article provides insights from a practical study, which examined the implementation of a workflow management system as a potential next step.

This paper describes an analysis and design method for knowledge management integrating man and machine in the age of the 4th Industrial revolution (Industry 4.0). Digitized work p rocesses require employees in an Industry 4.0 environment to have the competence to adequately deal with fluid situations on the basis of their own knowledge and the ability to place this knowledge in situation-specific contexts. To this end, the development of a comprehensive understanding of processes is elementary.

The development of Industry 4.0 changes the role of humans in operations systems. In sociotechnical systems, there is ongoing interaction between humans and technology, impacting human life and work. However, human factors are broadly ignored in research on Industry 4.0 technologies and implementation. In this work, a process model is described that supports the evaluation of the impact of a technology implementation on human factors and performance indicators. This can avoid negative consequences for employees as well as phantom profits and can contribute to a successful digital transformation.

This article describes a predictive maintenance approach in which a flexible sensor toolkit records and a prediction model monitors the component wear within technical systems. The condition of the components is not determined continuously, but based on time-discrete measurements. The prediction model predicts the presumable remaining useful life of the components based on the recorded data. A machine learning tool is trained with historical wear curves and used to generate the prediction. The training data is collected through statistical tests in which the influencing variables and characteristic curves of different types of wear are identified.

Reinforcement learning (RL) can be more and more used for real-world decision problems in production. The article gives an introduction into the functionalities of RL as well as its preferred areas of application. It further describes project examples from everyday production. The presented knowledge of current research is intended to make this sub-area of artificial intelligence accessible to a broader audience and to increase the added value in production.

In developing countries approx. 10 % of all medicines are falsified. The securPharm system in Germany prevents this high level of counterfeiting. It allows to identify and stop counterfeit drugs along the supply chain. The identification works via a data storage and polling system on supranational level. Based on system difficulties, pharmacies do not accept the system fully. Besides online pharmacies are an issue because they are not as much secured as the stationary distributors. Subsequently a comparison of the European and the US system shows their equality in most of the elements.

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.