Search

Buyer-supplier negotiations play a central role in supply chain networks regulating the conditions for the exchange of goods. This paper investigates the relationships between influencing factors and their impact on bargaining power. In a multiple case study, we triangulate secondary data from 12 German automotive suppliers. Following a structural equation modelling approach, bargaining power and the effects of those influencing variables are analysed. Four relationships can be found. (1) The availability of internal resources and the existence of strong buyer-supplier relationships have a positive effect on bargaining power. (2) A positive influence of buyer-supplier relationships on gaining access to internal resources can be identified. (3) External market conditions have a small negative effect on bargaining power. (4) No significant effect can be found looking at the impact of external market conditions on internal resources or buyer-supplier relationships. The findings support theory by showing that internal resources and strong buyer-supplier relationships positively influence bargaining power. Based on these results, managers are advised to prioritise strategic resource allocation, implement win-win strategies, and employ benchmarking techniques to enhance competitiveness and strengthen bargaining positions. [ABSTRACT FROM AUTHOR]

Production systems have to be adapted continuously to changing circumstances. This means that the responsible production planner has to frequently make decisions about reconfiguring complex systems under uncertainty whose outcome greatly affects the company's business success. Discrete-event simulation is one powerful tool to support the necessary analysis and scenario evaluation, but still remains time-consuming and tricky to set up and to maintain. When implemented as a digital twin of the production system, the simulation model can maintain a high degree of accuracy over a long time period. An approach to realise this potential is presented and illustrated in this paper with a use case from the automotive industry. This paper contributes several new methods and findings to the development of digital twins of production systems: Firstly, it demonstrates how exceptional events in the validation of the digital twin can be handled. Secondly, it shows how structural changes in the system can be discovered using data on machine activity and process mining. Thirdly, the paper introduces a possibility on how to assess the accuracy of the digital twin. Furthermore, it demonstrates how to assess the robustness of the digital twin to estimation errors in machine processing times. [ABSTRACT FROM AUTHOR]

Managing acquired subsidiaries can be daunting. Parent and affiliate executives strive to co-create value, but fixed mindsets around subsidiary autonomy can result in diverging interests and outcomes. Through a longitudinal study of Audi’s post-acquisition integration of supercar manufacturer Lamborghini, this article provides guidance on how to manage the level of acquired subsidiary autonomy as a strategic dial that can be dynamically adjusted over time for mutual benefit. This dynamic approach to autonomy rests on three specific managerial levers—appraisal respect, organizational identity, and resource orchestration. These can enable the renewal of competitive capabilities and sustain post-acquisition success. [ABSTRACT FROM AUTHOR]

For automobile manufacturers, reducing vehicle interior noise is essential for increasing customer satisfaction and vehicle quality. Windshield wipers are one of the major components that generate such noises, and faulty wipers could negatively affect passengers' psychological and physiological perceptions while driving. Thus, identifying faulty wipers during the manufacturing process would improve the driving experience and vehicle and road safety as well as reduce driver distraction. However, the existing windshield wiper noise-detection process is entirely manual, relies upon human subjectivity, and is time-consuming. Accordingly, this paper develops a novel automated windshield wiper fault-detection system. First, a novel binarization approach is used to effectively binarize the transformed spectrograms of sound signals from windshield wiper operation to segment nAoisy regions. Then, a new matrix-factorisation approach called orthogonal binary singular value decomposition is proposed to decompose binarized mel spectrograms into uncorrelated binary eigenimages to extract meaningful features and identify faulty wipers. Then, the $ k $ k -nearest neighbour classifier is utilised to classify the extracted features into normal or faulty windshield wipers. Finally, to demonstrate the effectiveness of the proposed system, it was validated on real-life windshield wiper reversal and squeal noise datasets, where it outperformed existing methods and achieved accuracies of 95% and 94%, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides new opportunities to improve manufacturing lines' performance and in-plant logistic processes. The digital milk-run system represents the new frontier to optimize material handling strategies but is still not fully exploited to address material distribution depending on the time slots required by the manufacturing lines. Therefore, to fill this gap, this paper investigates the actual integration of the milk-run system with an IoT system. An analytical model for planning a dynamic routing strategy for tugger trains to deliver the materials to different workstations of a production line has been developed. The proposed model provides a materials distribution consistent with the time slot required by the manufacturing line, ensuring the minimisation of the total distance of the routes. An algorithm developed in Python is proposed to solve the NP-hard problem (nondeterministic polynomial time problem). The model has been applied to a real case of a worldwide automotive company to validate and prove its efficacy and efficiency. Indeed, compared to the current in-plant logistic strategy, (i) the inventory stock of each workstation was ensured, (ii) the average utilization rate of the tugger trains' fleet was improved, and (iii) the daily path was minimized. [ABSTRACT FROM AUTHOR]

Introduction and summary The automotive industry has embarked on a major transition from manufacturing gas-powered vehicles to producing electric vehicles (EVs). (1) In this article, we parse out the implications [...]

Previous research on automated guided vehicles (AGVs) and Industry 4.0 has mainly focused on technical aspects, while the humans who work with AGVs as well as the influences on the organisation have received limited attention. This paper aims to explore human- and organisation-related challenges in the introduction of AGVs in production facilities. Two cases are examined and analysed using the Human, Technology, and Organisation (HTO) model, which highlights the interactions between the three subsystems: human, technology, and organisation. The paper shows that several human- and organisation-related challenges arise when introducing AGVs, such as supporting the AGVs with new work procedures for managing AGV errors, determining what different operators need to know about AGVs, and developing acceptance among employees. The identified challenges complement the technical focus in previous research. Actions are proposed to facilitate the introduction of AGVs to avoid or manage the identified challenges. The findings of this study can help managers involved in the introduction of AGVs to consider human- and organisation-related aspects to improve AGV performance and employee well-being. [ABSTRACT FROM AUTHOR]