Search

The inspection, analysis, and modification of robot programs in series production processes is a challenging recurring task in the automotive industry. Especially the holistic analysis of detected defects and the subsequent derivation and application of corrective measures to the underlying robot program is a complex task requiring extensive knowledge in multiple fields. Existing scientific literature has demonstrated that the utilisation of Augmented Reality (AR) assisted robot programming systems (ARRPS) improves programming efficiency as well as intuitiveness when compared to traditional programming methods. However, the accuracy especially of mobile AR devices limits the applicability of AR in industrial applications. We propose to leverage the iterative nature of the optimisation process common in series production and utilise the visual capabilities of the human worker to define corrective measures based on the inspected real process result. This mitigates the impact of limited tracking accuracy as the optimisation is based on the visual perception of the user and thus decoupled from the accuracy of the employed AR device. We implemented our system based on the requirements of a sealing workstation of the Mercedes-Benz Group in Germany. A conducted experiment validates the functionality and indicates that the presented strategy of iterative AR assisted robot programming enables optimisations with a similar accuracy as the Teach-In programming as well as a significantly reduced programming time. This suggests that utilising an ARRPS can be highly beneficial in the inspection, analysis, and modification of sealing applications common in automotive series production processes. As the presented iterative AR assisted robot programming is neither limited to sealing nor to the automotive sector it can be adapted for various robot-automated applications like path-welding, gluing, or painting and can extend its utility to related sectors such as aviation or the marine industry. [ABSTRACT FROM AUTHOR]

In recent decades, supply chains have become a critical source of competitive advantage. Yet, in the Germany automotive industry, supply chains have turned out to be untransparent and prone to sustainability breaches, with the recent Volkswagen manipulation scandal exemplifying the financial and reputational consequences. Many firms today, therefore, focus on what is called sustainable supply chain management (SSCM). While drivers, barriers, and performance implications of SSCM have been widely explored, little is known about its implementation process. Building on the foundations of organisational change, this study inductively analysed 54 sustainability reports from three German automotive triads between 2014 and 2019. Our results led to an SSCM implementation framework that sequentially employed the nine most prominent implementation stages and related change measures. The framework expands our knowledge on the SSCM implementation process; furthermore, it serves as an example for industry experts aiming to turn their supply chains into sponsors for sustainability. [ABSTRACT FROM AUTHOR]

• Fuel costs were of highest decision-relevance for German car drivers. • The impact of pollutant emission reductions on fuel preferences was irrelevant. • Currently low market success for alternative fuels without incentives in Germany. • Policy interventions should target costs and availability to stimulate market demand. In the search for sustainable transport solutions, fuel production from renewable resources has received significant attention. Some proposed synthetic fuels have favorable combustion properties compared to existing fuels, e.g., significant reductions in pollutant formation. However, penetration of such fuels requires a favorable social acceptance, as demonstrated by the consumer boycott of the ethanol-blend fuel with 10% ethanol and 90% gasoline (E10) in Germany. Therefore, the consumer perspective and their preferences regarding alternative fuels should be considered in the fuel design. We use conjoint methodology to analyze the preferences of German car drivers for alternative fuels. This aims at understanding which criteria determine consumer preferences and usage decisions. Among the five considered fuel attributes (fuel availability, driving range, pollutant emissions, fuel costs, and usage requirements to enable the use of alternative fuels), fuel costs had the highest decision impact for alternative fuel preferences, followed by fuel availability and usage requirements. Pollutant emissions had the lowest impact on alternative fuel choices. A market simulation of conventional diesel and alternative fuels (dimethyl ether (DME) and a blend of diesel with oxymethylene dimethyl ethers (OME)) revealed that currently a large majority of car drivers would prefer conventional fossil fuel options, indicating a currently low consumer demand for alternative fuels. Thus, the findings demonstrate the importance of integrating social acceptance as an objective function in the design of novel fuels and production processes. [ABSTRACT FROM AUTHOR]

Today's automakers are under an enormous price and cost pressure arising from both increasing competition and complexity of operations. One way to deal with this challenge is to outsource value creation to the supply network (SN), which then assumes the responsibility for producing variant-specific modules and delivering them just-in-sequence (JIS). This results in tightly coupled buyer–supplier relationships that operate with short reaction times and are characterized by high time dependency and minimal buffers. It increases the risk exposure of buyers to disturbances in their SNs and failures propagate quickly, causing production disruptions. Based on data collected at 14 car-manufacturing plants located in Germany, this research provides an overview and exploratory evidence of current JIS market practice and proposes ways of actively managing the risks inherent in JIS SNs. [ABSTRACT FROM PUBLISHER]