FIR e. V. an der RWTH Aachen

Since 2016, the “Digital in NRW” Competence Centre has been supporting SMEs in the manufacturing industry in designing their individual digital transformation. With an Industry 4.0 maturity assessment, we define the status quo of SMEs, derive SME-specific measures from this, develop a digitalization roadmap and accompany the SME transformation. This paper presents the results of the four-year SME support. By analyzing the results of all maturity assessments, potential analysis and design workshops, we present the most frequent and most effective measures for a successful digital transformation of SMEs. The result of the paper is an action guideline for SMEs to initiate their own digital transformation based on formalized experience.

Today's manufacturers are facing numerous challenges such as highly entangled and interconnected supply chains, shortening product lifecycles and growing product complexity. They thus feel the need to adjust and adapt faster on all levels of value creation. Self-optimization as a basic principle appears a promising approach to handle complexity and unforeseen disturbances within supply chains, machines and processes. Therefore it will improve the resilience and competitiveness of manufacturing companies. This paper gives an introduction to the concept of self-optimizing production systems. After a short historical review, the different levels of value creation from supply chain design and management to manufacturing and assembly are analyzed considering their specific demands and needs for self-optimization. Examples from each of these levels are used to illustrate the concept of self-optimization as well as to outline its potential for flexibility and productivity. This paper closes with an outlook on the current scientific work and promising new fields of action.

The optimization of lithium-ion batteries (LiBs) recycling is crucial not only from a waste management perspective but also to decrease the dependence on imports of critical raw materials. In addition, the diversification of the recycling technologies is very important for better flexibility of the market. This study aims at investigating the recovery of Co from spent LiBs using solvent extraction from a real chloride-based solution obtained after the removal of Mn, which is very rarely reported. Cyanex 272 was used as the extractant and the effect of several variables on the extraction efficiency was considered to model and optimize the separation of Co and Ni. The number of extraction stages directly affects not only the process efficiency but also its cost. Thus, in this work, a novel approach was developed to assist in the selection of the number of extraction stages using a dynamic method based on the factorial design of experiments and response surface methodology combined with the Kremseŕs Equation. This method can assist the process design, decrease the overall cost of the operation, and optimize the separation of Co and Ni in a reduced number of extraction stages. The concentration of Co and Ni in the feed solutions is ∼ 8.3 g/L and 1.9 g/L, respectively. Based on the results, 98% extraction efficiency for Co can be achieved in 1 to 2 extraction stages with low co-extraction of Ni (<5%) when using 0.6–0.8 M Cyanex 272, O:A ratio below 1 and pH ∼ 5, but several combinations of conditions could provide similar results.

Subscription business transforms traditional business models of machinery and plant engineering. Many manufacturing companies struggle to pull out the potential created by Industry 4.0 and make it economically usable. In addition to technological innovations, it is necessary to transform the business model. This leads to a shift from ownership-based and product-centric business models to outcome-based business models, which focus on the customer’s value and thus realize a unique value proposition and competitive advantage – the outcome economy. Based on a case study analysis among manufacturing companies, this paper provides further clarification including a definition and constituent characteristics of subscription business models in machinery and plant engineering.

OBJECTIVES: In this study, we explored crucial factors that explain a person's attitude toward and his or her assessment of telemedical systems. Special focus lies on the link between the perspective of physicians (telemedicine users) and technicians (telemedicine designers) to find potential barriers hindering the broad application of telemedical systems in hospitals and doctors' offices. METHODS: A survey among medical professionals (n = 34), technical professionals (n = 39), and a control group (n = 44) was conducted. The collected data were assessed in terms of domain knowledge, attitudes toward telemedicine, and potential implementation barriers. RESULTS: Participants favored the conventional method over telemedical monitoring in regards to privacy, security, and time efficiency. In contrast, telemedicine was preferred with reference to efficiency of data analysis, long-term care, and emergency adequacy. Significant differences between the professional groups were found regarding perceived cost effectiveness, patients' compliance, privacy protection, and false alarm sensitivity. Medical professionals exhibited the most reluctance toward using telemedical treatments. CONCLUSIONS: The perceived drawbacks are attributed to a general uncertainty about the reliability of telemedical systems, in combination with concerns about personal data privacy, security, and loss of control. The reported fear of not being able to correctly use and handle the systems assumes a low usability of devices. To acquaint medical professionals with the benefits and limitations of telemedical systems, telemonitoring and tele-treatment should be included in the education of medical personnel at an early stage.

Manganese plays a central role in lithium-ion batteries (LIBs) but its recycling is rarely addressed when compared to other valuable metals present in LIBs, such as Co and Ni. Thus, the main goal of this work was to study and achieve the separation of Mn from Co and Ni by solvent extraction from a leachate obtained from LIBs using hydrochloric acid in an upscaled reactor, which is an innovative aspect of this work. The results confirmed the high selectivity of D2EHPA towards Mn, which could be completely extracted in two stages (0.5 M D2EHPA at pH 2.5). The main co-extracted metals were Al, Cu and Co, but with lower concentrations than Mn. The behavior of minor impurities such as Zn and Mg was also monitored. Scrubbing using manganese chloride was crucial to remove impurities from the loaded organic and prevent their presence in the stripping product, and high O:A ratios negatively affected the scrubbing efficiency. Keeping the concentration of HCl up to 0.5 M in the stripping stage helped to limit the stripping of impurities. Manganese oxide was precipitated as a product with 99.5% purity (with traces of Zn, Cu and Co), which could be reused in the battery value chain.

One of the major challenges facing today's manufacturing industry is to differentiate from competition in a highly globalized world. As a consequence to the increasing competitive pressure, many companies transform their product centered business models towards service based business models to differentiate from competition. However, the transformation is often underestimated regarding its complexity and its management challenges to behavioral change. As a consequence lots of transformation initiatives fail. Besides difficulties in structuring the magnitude of changes in processes and structures, many transformation managers do not perceive the risk of employee resistance against changes, which is one of the key factors causing the failure of transformation. The objective of this paper is to enhance the existing body of research on manufacturer's organizational transformation towards Product-Service Systems. More detailed, the objective is to develop new knowledge to support the management during the decision-making process in the way how and by means of which instruments the change of behavior can be supported when transforming from a manufacturer to a solution. We developed a reference framework which structures and defines the relevant dimensions of behavioral change. The identification and validation of the success factors build the second component of our research. We conducted an empirical investigation in the German manufacturing industry and got 79 data sets. Structural equation modelling was applied for the analyses and the validation of the hypotheses. By this analysis we linked management practice with employee behavior and transformational success variables. On the basis of the gained insights decisions can be made concerning the successful transformation from manufacturer to a solution-oriented service provider.

Real-time data analytics methods are key elements to overcome the currently rigid planning and improve manufacturing processes by analysing historical data, detecting patterns and deriving measures to counteract the issues. The key element to improve, assist and optimize the process flow builds a virtual representation of a product on the shop-floor - called the digital twin or digital shadow. Using the collected data requires a high data quality, therefore measures to verify the correctness of the data are needed. Based on the described issues the paper presents a real-time reference architecture for the order processing. This reference architecture consists of different layers and integrates real-time data from different sources as well as measures to improve the data quality. Based on this reference architecture, deviations between plan data and feedback data can be measured in real-time and countermeasures to reschedule operations can be applied.

Smartification and digital refinement of products to enable the design of smart ones is a pivotal challenge in the manufacturing industry. Companies fail to design smart products due to missing knowledge of digital technologies and their integral part in product development processes. This paper presents a methodology that enables the derivation of digital functions for smart products through selected cases in manufacturing usage. We develop a morphology that consists of digital functions for smartification. In this context, we explained and derived characteristics by a set of examples regarding smart products in the manufacturing industry. Our methodology reduces the time spent initiating a development project with the focus on smartification.

This paper describes the statistical machine translation systems developed at RWTH Aachen University for the GermanEnglish, EnglishTurkish and ChineseEnglish translation tasks of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018).We use ensembles of neural machine translation systems based on the Transformer architecture.Our main focus is on the GermanEnglish task where we scored first with respect to all automatic metrics provided by the organizers.We identify data selection, fine-tuning, batch size and model dimension as important hyperparameters.In total we improve by 6.8% BLEU over our last year's submission and by 4.8% BLEU over the winning system of the 2017 GermanEnglish task.In EnglishTurkish task, we show 3.6% BLEU improvement over the last year's winning system.We further report results on the ChineseEnglish task where we improve 2.2% BLEU on average over our baseline systems but stay behind the 2018 winning systems.

Especially in times of increasing competition and price pressure, companies are striving to constantly improve their own performance. Due to the increasing networking of companies, enterprise performance is more and more influenced by the supply chain performance. It can be assumed that the optimal performance can only be achieved through a holistic view (including the supply chain). For the optimization of networks as well as for the optimization of companies, key performance indicators (KPIs) are often used. Since each company determines the KPIs individually based on its own priorities, there is no comparability and thus the optimization of inter-company value chains is significantly more difficult. The aim of this paper is to identify KPIs that are relevant for a large number of manufacturing companies. The analysis is focused on operational performance dimensions such as efficiency/cost, time, quality and flexibility. For this purpose, a systematic literature analysis of 180 papers was conducted in order to identify frequently considered KPIs. Based on a subsequent quantitative and qualitative analysis eleven particularly relevant KPIs were derived. The identified KPIs form the basis for a holistic analysis of the operational performance of the company. With the basic setting of KPIs, it is possible to create comparability between companies and thus provide the basis for cross-company optimization.

The steady increasing of supply chain complexity due to a rising global cross-linking of production and sales regions leads to an increasing sensitivity to disturbances while in the meantime the requirements of the availability, the time of delivery and the security of supplies within the supply chain increases. To meet this challenges the security of the supply chain infrastructure and the feasibility of supply chain processes need to be ensured, despite of the high specialization within the supply chain partners, the low stock and time buffers, and the information shortcoming between supply chain partners. In this research, a System Dynamics simulation model, based on the manufacturing supply chain model of Sterman, has been developed for representing the actual complexity and dynamic in manufacturing supply chains. Therefore, the modeled manufacturing supply chain shows the processes of a four level supply chain focusing the processes and interactions of the mid-positioned two supply chain participants. The main contribution of the work described in this paper, is the description and implementation of necessary additional modules and parameters to Sterman's basic model for the diagnosis of disturbance impacts as well as for the realization of supply chain adjustments. Finally, the model has been simulated and examined for realistic values.

Due to lack of experience of companies with digital business models, agricultural machinery manufacturers and agricultural service companies are facing a positioning problem in their ecosystem. Smart services are getting more important for these companies and they have issues to define a matching business model for their newly developed smart services. The lack of a framework for smart service business models makes it even harder for companies to successfully develop new services. This paper contributes to a better understanding of business models for smart services and establishes a common morphological framework to define different types of business models for smart services. Six types of business models of industrial smart services were identified during the research based, which was based on a literature review and interviews with leading experts in the field of smart services. The validation of the developed types and its practical application was carried out as part of the German research project Smart-Farming-World and its four developed use cases. This paper gives a detailed description of the application of the framework on the use case nPotato.

Big Data is one of the most discussed trend themes worldwide in both research community and industrial practice. Thus, researchers as well as company representatives focus on the study of Big Data technologies and the potentials deriving from gaining and structuring data. In this paper, we firstly conduct a literature review. Then, we describe the approach to implement reaction management in manufacturing environment before we finally report on a research project aiming at applying Big Data technologies in producing companies. The project goal is to develop a real-time capable platform in consideration of industrial requirements. With the help of a Big Data platform, producing companies will be enabled to use several applications like e.g. monitoring, prognosis or reaction. By receiving appropriate measures defined in the platform, the ability of producing companies to detect and to react proactively to failures deriving in manufacturing will sustainably increase.

Industrial food production represents one of the largest industries, accounting for a share of ten percent of the world’s gross domestic product. Simultaneously, it is responsible for 26 percent of global greenhouse gas emissions. Due to increasing CO2 taxes and population’s call for sustainability and CO2 reduction, it is facing challenges in terms of economic profitability and stakeholder demands. These challenges could partly be overcome by participating in data ecosystems in which data are refined as data products, understood, exchanged and monetized as economic goods. Despite large amounts of data, collected parenthetically along the value chain in food production, potentials of data analytics and data ecosystems are only marginally exploited. Food production mainly focuses on traditional, product-centric business models. This work shows the conceptualization of a data ecosystem for food production, enabling data-based business models. Therefore, resources, actors, roles and underlying relationships of future ecosystem are analyzed. Building on these, corresponding architectural and analytical artifacts that support data ecosystem exploitation are presented. A food production data ecosystem is exemplified by applying data analytics to compressor data, which reveals high potentials for CO2 reduction.

Volatile electricity prices caused by an increase of renewable energy sources push producing companies towards taking in an active role in balancing the electricity grid. Possible actions at the customer side to actively adapt to volatile energy prices are called demand response actions. In production logistics such actions can be the modification of production schedules motivated by possible economic benefits. So far, the focus in scheduling problems has been the optimization in the dimensions of quality, time and costs. This paper presents the results of a simulation study on the economic benefits of demand response actions for a generic production system.

In order to achieve a holistic cost management approach, the maintenance and service costs should already be assessed during the development of machines and equipment. The required information in the company, like PLM, process and test data, are commonly not available or vague, especially in early development phases. This paper introduces a feasible method for an early assessment of maintenance and service costs during product development. In doing so, appropriate cost assessment methods are selected, based on the availability and quality of the existing information in the individual development phases. The evaluations of these methods are aggregated in a software tool, so that the respective cost information is displayed with a maximum, minimum and most probable value. The developed software tool was validated in cooperation with a new electric vehicle manufacturer.

When planning, creating, and evolving systems throughout their lifecycle, it is essential to assess their impact on our world. Despite this pressing need, existing structured methods for systematically assessing social, economic, and environmental impacts are not related to targets of the United Nations' sustainable development goals. Moreover, existing Architecture Description Languages (ADLs) lack concepts and tooling for sustainability assessment. Our aim is to allow modeling systems, their sustainability properties, and sustainability questions in a structured manner for a wide domain. This paper proposes the engineering and design of a domain-specific language for sustainability assessment embedded into ADLs and showcases its use for evaluating a citizen energy community system as a case study. We discuss possibilities on how to use such models in their further processing and explore challenges in technical realization. This initial step towards standardizing the sustainability assessments of modeled systems throughout the development is both comprehensive and formal so that developers can make informed, sustainable decisions based on consequence assessments upfront.

Today’s manufacturing companies embedded in non-hierarchical production networks are facing multiple and dynamic customer-supplier-relationships. In the course of increasing complexity of products and growing needs for flexibility and product variation companies focus more on core competencies and thus more production processes are shifted to external suppliers. This complex environment leads to growing coordination-efforts and wasteful turbulences throughout the entire network. The result is a delivery reliability of usually less than 65% within the European machinery and equipment industry generating an estimated loss of efficiency of 1 billion Euros per year. Besides additional costs the missing delivery reliability entails poor customer satisfaction and increased lead times compromising the competitiveness of individual companies as well as the entire machinery and equipment industry (Gunasekaran, 2000; Reinhart, 2006).

Due to the changing energy market with regards to fight against the climate change, manufacturing companies are forced to enhance their efforts in energy management. Since the topic was mostly neglected for quite a while, there are no concepts that cover an holistic approach for industrial energy management. This paper presents a way to merge the existing partial systems for manufacturing companies by looking at the crucial information flows and solving the subsequent holistic optimization problem using the concept of walrasian auction with integrated Q-learning algorithm. The result can support business by identifying the needed (information-) resources and implementing a holistic optimization of energy usage.