Wind is an environmental stimulus that stresses plants of all growth forms at all life-stages by influencing the development, architecture, and morphology of roots and shoots. However, comparative studies are scarce and no study directly investigated whether shoot and root morphological traits of trees, grasses and forbs differ in their response to short wind pulses of different wind intensity. In this study, we found that across species, wind stress by short wind pulses of increasing intensity consistently changed root morphology, but did not affect shoot morphological traits, except plant height in four species. Wind effects in roots were generally weak in tree species but consistent across growth forms. Furthermore, plant height of species was correlated with changes in specific root length and average diameter.Our results indicate that short-pulse wind treatments affect root morphology more than shoot morphology across growth forms. They further suggest that wind stress possibly promotes root anchorage in young plants and that these effects might depend on plant height.

AbstractAcross the tree of life, organismal functional strategies form a continuum from slow-to fast-growing organisms, in response to common drivers such as resource availability and disturbance. However, the synchronization of these strategies at the entire community level is untested. We combine trait data for >2800 above-and belowground taxa from 14 trophic guilds spanning a disturbance and resource availability gradient in German grasslands. Most guilds consistently respond to these drivers through both direct and trophically-mediated effects, resulting in a ‘slow-fast’ axis at the level of the entire community. Fast trait communities were also associated with faster rates of whole ecosystem functioning. These findings demonstrate that ‘slow’ and ‘fast’ strategies can be manifested at the level of whole ecosystems, opening new avenues of ecosystem-level functional classification.

AbstractThe megatrend sustainability will significantly change the future value creation of manufacturing companies. Their current linear value creation models need to change to conform to the requirements of sustainability. To face the challenges the circular economy has emerged as a promising economic model. With the shift from a linear to a circular economy, companies have to adjust their current business models. However, they still lack knowledge and adequate methods to develop circular business models. Hence, the aim of the paper is a circular business model canvas (CBMC) for designing circular businesses. A systematic literature review was conducted to identify significant criteria for circular business models. The criteria were analyzed utilizing a qualitative content analysis to derive the business model elements required for the circular economy. Existing elements from the Business Model Canvas were enriched and five new elements, e.g., reverse logistics, were found. Then, the business model elements were logically arranged resulting in the CBMC. It guides companies to systematically develop business models for the circular economy. The CBMC was evaluated by applying it within a mediumsized company in the electronics sector.

AbstractIn global competition, companies are continuously searching for a competitive advantage. A growing number of companies have identified the design of their products as a major success factor. The promises for companies associated with outstanding design are extremely attractive, ranging from better customer experience to higher sales growth. But even though the advantages of design seem to be compelling, companies are still having difficulties in leveraging the benefits of design.In this paper, we want to identify the underlying challenges that explain this gap. We are especially interested in the role and challenges of design in interdisciplinary product development. Therefore, we perform a literature review that clarifies terminology, analyses research approaches and identifies challenges. We determine seven key challenges for design in interdisciplinary product development. Furthermore, we observe a limited scope of research approaches in the reviewed literature. Based on these findings, we propose three further research directions to strengthen the role of design in interdisciplinary product development in the future.

AbstractNew trends and technologies in product creation increase complexity, but at the same time create new potentials such as efficiency rise in task processing by Artificial Intelligence. Established models in the early phase of product creation such as the W-model or the Aachener Innovation Management model, do not fully exploit these new potentials in the field of strategic product planning and innovation management (SPPIM). For this reason, existing models are analysed in SPPIM in order to derive a requirements profile consisting of potentials and goals for a new model. A new model in SPPIM lays the foundation to support companies in enabling a more efficient task fulfilment by taking advantage of new technologies and trends. To guide the development of advanced SPPIM models, the derived potentials and goals are applied to the guideline VDI 2220:1980.

Sustainability and digitalization are two central fields of action in the manufacturing industry that are of crucial importance for future business success. While the digital transformation has been a key topic on the strategic agenda of many companies for several years, corporate sustainability transformation has become significantly more relevant in the recent past. However, since the digital transformation is still in progress, there is a need for a holistic transformation that includes both topics. Conducting this so-called dual transformation is a necessary and time-critical task for companies to keep their businesses viable. Yet, despite their relevance and intersections, digital and sustainability transformation are often considered separately in companies today.For this reason, the goal of this paper is to identify current challenges and barriers as well as to transparently disclose the potentials of the dual transformation. Therefore, an interview study with digitalization and sustainability experts from the manufacturing industry is conducted using a semi-structured questionnaire. Consolidating the results, an overarching framework is developed structuring the essential fields of action for the dual transformation. The results of the interview study complement preliminary work in the research field and simultaneously allow companies to better address interdependencies and synergies of the dual transformation.

An approach to predict standard times for upcoming individual customer orders based on historical production orders is developed. The approach follows the CRISP-DM model with regard to the steps data preparation, modeling and evaluation. A precision steel tube straightening machine is provided as a validation example. Furthermore, the paper focuses on how the standard time prediction approach can be successfully integrated into the IT infrastructure of a production focused company. Especially data availability and accessibility in enterprise resource planning systems are discussed. The presented approach offers users the value of more precise scheduling in production than established approaches.

AbstractManufacturing companies face the challenge of combining increasing productivity and quality standards with customer″​=oriented mass production. To achieve the required quality standards, quality controls are carried out after selected production steps. These are often visual inspections by trained personnel based on checklists. To automate visual inspection industrial, cameras and powerful machine vision algorithms are needed. Large amounts of visual training data are usually required in order to train these algorithms. However, collecting training data is time″​=consuming, especially in customer″​=oriented mass production. Synthetic training data generated by CAD tools and rendering software can alleviate the lack of available training data. Within the paper at hand, a novel approach is presented examining the use of synthetic training data in machine vision applications. The results show that synthetically generated training data used to train machine vision quality gates is fundamentally suitable. This offers great potential to relieve process and productions developers in the development of quality gates in the future.

We consider formation problems for chains of disoriented, mobile robots with limited visibility operating in asynchronous rounds (Async). More precisely, we study the Chain-Formation and the Gathering problem. Chain-Formation considers a chain of robots between two stationary outer robots: Each inner robot has two identifiable neighbors, and the goal is to arrange the robots on the line segment connecting the outer robots. The Gathering problem considers a closed chain (without outer robots) and demands all robots to gather on a single, not predefined point. The robots move in the Euclidean plane and are luminous, i.e., equipped with a light visible to the neighboring robots. At each point in time, the light can have one out of a constant number of colors.We introduce a family of algorithms inspired by the Hopper algorithm [1]. For the Chain-Formation problem, we modify the Hopper algorithm so that we can guarantee a (1+ε)-approximation to the optimal chain length (instead of a 2-approximation). Our main result is an asymptotically optimal algorithm for Gathering of a closed chain of disoriented, luminous robots with limited visibility in the Euclidean plane. All algorithms have a worst-case optimal runtime of O(n).