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The automotive industry introduces a sizable number of new automobile products annually. The naming of new products may have significant impact on their market performance. Diverging from prior research on the impact of product names, companies' innovative consciousness endows them with a certain level of proactive positioning, thereby implementing independent naming strategies after considering product factors and consumer preferences passively. This paper analyzes the drivers of the market performance of sequential products on the naming of new products in Chinese automotive industry. We collected market share data and names of various car series from January 2007 to September 2022. Then, we calculated semantic distances between names and performed empirical tests. Our findings show that firms demonstrate a risk preference pattern contrary to traditional prospect theory when using the previous car series' market share as a reference point. In the interplay between market orientation and risk preferences, the relationship between the market share of the last car series and the similarity in names between old and new car series follows an inverted U-shaped pattern. This study extends the discussion on risk preferences within the behavioral theory of the firm (BTOF) and proposes a decision mechanism for product naming that considers market and firm factors during the response phase of market orientation. [ABSTRACT FROM AUTHOR]

In response to the challenges posed by economic globalisation and increasing customer demands, enterprises are compelled to adapt and refine their production models and operational objectives, which motivates this paper to address the integrated production scheduling and vehicle routing problem in the distributed manufacturing environment (IPSVRP-DME). The objective is to simultaneously minimise both total energy consumption and total earliness/tardiness. Initially, a mixed integer programming model is proposed to address small-scale problems using Gurobi. Considering the NP-hardness of the problem, a novel Self-adapted Gaussian Mutation-based Arithmetic Optimiser Algorithm (SGMAOA) is developed to handle medium-scale and large-scale instances. To address the complexity of decision-making, an innovative two-level encoding method that encompasses four decision dimensions is introduced. Additionally, an incremental repair strategy is devised to rectify infeasible solutions caused by unreasonable delivery batching, while a time relaxation strategy is proposed to further enhance service levels without compromising energy consumption. Comparative experiments are conducted to demonstrate the effectiveness of SGMAOA, which is benchmarked against five prominent metaheuristics. In addition, a specific example is applied for the discussion of managerial applications and to illustrate the practicality of the proposed model and solution method. [ABSTRACT FROM AUTHOR]

The shift to electric mobility is driving disruptive transformations in the automotive sector worldwide. It poses significant but different opportunities and challenges to incumbents and latecomers at both the firm and country levels. China's green industrial policy has facilitated its rapid catching up and even leapfrogging in some domains of electromobility. This article asks whether the exploitation of this window of opportunity can be replicated in other latecomer countries. Accordingly, it provides a comparative analysis of the automotive green window of opportunity in Brazil, India, and South Africa, three emerging economies with long established but structurally different automotive industries. To do so, it examines domestic preconditions, policy and enterprise responses, and preliminary industrial development outcomes. Although all the three countries face constraints in replicating China's relative success, the article shows how green opportunities and threats are unequally divided between the three countries. [ABSTRACT FROM AUTHOR]

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]