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The purpose of this paper is to define the local integration rate and how it is calculated to assess its relevance as a national performance indicator for the Moroccan automotive industry.

The research methodology first followed a systematic review approach through the analysis of published research articles and academic works. This study then followed a qualitative approach based on semi-structured interviews with various actors in the Moroccan automotive industry. Finally, the findings of this work were reinforced by a case study to analyze the supply chain of a locally produced vehicle.

The results indicate that the local integration rate as calculated today overestimates the performance of the automotive industry and does not systematically guarantee a significant creation of value added.

Due to the confidentiality of the data in terms of turnover, payroll and purchase prices as well as the large number of suppliers in the different supply chains of the car manufacturer, the case study focused on only one of the six existing ecosystems.

On the basis of research work on the Moroccan automotive industry as well as interviews with various actors, the local integration rate is unanimously considered as a viable performance indicator. This study has not only led us to the method of calculating this rate by the Ministry of Industry but also demonstrated its limitations while proposing a new method of calculation to increase the value added.

This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).

A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.

Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.

The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.

Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.

This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.

The present paper examines the barriers hindering the implementation of circular economy practices in Indian automotive industries and identifies the most critical barriers to be eradicated during the circular economy implementation.

Using a questionnaire survey-based analytic hierarchy process (AHP) approach, 21 barriers to circular economy implementation are identified through a literature review and ranked using the opinions of relevant industrial experts.

Barriers, minimal or no proper economic incentives, difficulty in ensuring product quality and costing of CE products are identified as top barriers to be eliminated first to boost the chances of successful implementation of circular economy practices in Indian automotive industries.

The study not only offers a roadmap on the order of elimination of barriers towards a circular economy but also provides insights from industrial experts on how these barriers can be eradicated and the ill effects of each barrier from an automotive industry perspective.

The authors develop a theoretical framework for barrier analysis for circular economy implementation in the Indian automotive sector using the classic AHP approach to identify the most critical barriers against successful implementation. Further, this study offers managerial implications on how these barriers can be eradicated in real.

Although situational crisis communication theory (SCCT) is centered on rationality and cognitive information processing, it ignores that people are also driven by irrationality and non-cognitive information processing. The purpose of this study aims to fill this gap by examining how gender stereotypes, based on perceived spokesperson sex influence the public’s perceptions of crisis response messages.

A 2 (industry type: automotive vs daycare industry) × 2 (spokesperson’s sex: male vs female) × 2 (crisis response appeal: rational vs emotional) between-subject online experiment was conducted to examine the effect of gender stereotype in crisis communication.

Results showed that either matching spokesperson sex with sex differed industry or matching sex differed industry with appropriate crisis response appeal can generate a more positive evaluation of the spokesperson and the organization. The results also revealed under which circumstances, the attractiveness of different sex of the spokesperson can either promote or mitigate people’s perceptions of the organization. Furthermore, when people are aware of a spokesperson’s sex, in a female-associated industry, a mismatching effect of a positive violation of a male-related stereotype overrides a matching effect of a female-related stereotype in crisis communication.

This study is among the first to identify how the gender of a spokesperson and industry type affect publics’ crisis response.

Introduction: Cost competitiveness, customer focus, and sustainability compliance are essential for new-age firms to survive and succeed in the VUCA market environment. This study examines how automobile corporations have improved cost competitiveness, productivity, and product quality.

Purpose: This study examines the importance of cost competitiveness, customer focus, and sustainability compliance for the long-term survival of organisations in VUCA markets, looking at the practical efforts made by automobile corporations to enhance cost competitiveness, productivity, and quality.

Methodology: The study utilises a comprehensive analysis of the strategies and initiatives implemented by the selected automobile companies. It involves a review of relevant literature, case studies, financial data analysis, and interviews with key industry experts, providing a holistic understanding of the actions taken by these organisations to achieve their goals.

Findings: The study reveals that cost competitiveness, customer focus, and sustainability compliance are critical factors for the long-term survival and success of organisations in the automotive industry. The analysed automobile companies have undertaken practical efforts to improve cost competitiveness, enhance productivity, and ensure high-quality products, enabling them to navigate the challenges and maintain a competitive edge.

Significance: The findings of this study contribute to a deeper understanding of the importance of cost competitiveness, customer focus, and sustainability compliance in the automotive industry. It highlights the need for organisations to constantly monitor both qualitative and quantitative profit to avoid complacency and ensure long-term efficiency. The study’s insights are relevant to businesses operating in other sectors, as they face similar challenges in the VUCA market environment.

The purpose of this paper is to analyze the new technologies that condition location strategy and enable the reindustrialization of European countries involved in the automotive industry. Business relocation has seen an upsurge in recent years. The development of the technologies linked to new mobility (connected, autonomous and electric vehicles) in European countries is increasing interest in relocating high value activities in origin countries to preserve high quality jobs and maintain competitive advantage.

The paper analyzes the changes in location factors and relocation trends through qualitative research based on a multiple case study methodology of first level suppliers in the automotive industry.

This study reveals the capacity technology has to reconfigure the automotive value chain and to change traditional location factors and relocation trends. The results show relevant differences between the traditional components production divisions characterized by offshoring processes, in a continuous decrease of activity and deindustrialization, and the new technologies divisions linked to new mobility with strong growth, reshoring of the high value activities, reindustrialization plans and links with research and development. For these new mobility divisions, protection of innovation to prevent the core knowledge transfer and strong collaboration with local research institutions are key in this highly technological location context.

The research analyzes the impact of new mobility technologies on the relocation processes in the automotive industry through an original approach based on the evolution of traditional suppliers with divisions linked to new mobility.

In 2016, the ISO/TS 16949 quality management standard for the automotive industry evolved to IATF 16949. The update brought new requirements that need to be analyzed before being implemented in organizations. Therefore, the purpose of this article is to propose guidelines to assist organizations in the automotive sector in the implementation of the elements added in the update to the IATF 16949 standard.

To fulfill this objective, the identification and analysis of the elements added in the evolution from ISO/TS 16949 to IATF 16949 was carried out, and four case studies were conducted in Brazilian automotive companies.

The main elements added to IATF 16949 with the update of the standard are the use of process failure mode effects analysis (PFMEA) for risk analysis; the development of a communication channel for employees to report cases of misconduct and non-conformities; procedures for controlling repaired/reworked products and temporary changes; and the inclusion of autonomous maintenance for the full implementation of total productive maintenance (TPM).

The main practical implication/contribution of the research is the proposed guidelines, which can support managers and automotive companies that want to implement, or will go through, the IATF certification process. The article's originality lies in the combination of a theoretical framework and case study analyses to develop the guidelines.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

The manufacturing sector is highly competitive and operationally complex. Therefore, the strategic alignment between operational excellence methodologies and Industry 4.0 technologies is one of the issues that need to be addressed. The main aim of the study is to determine the critical factors of strategic alignment between operational excellence methodologies and Industry 4.0 technologies for manufacturing industries and make comparative analyses between automotive, food and textile industries in terms of strategic alignment between operational excellence methodologies and Industry 4.0 technologies.

First, determining the critical factors based on literature review and expert opinions, these criteria are weighted, and analytical hierarchy process is run to calculate the weights of these criteria. Afterward, the best sector is determined by the grey relational analysis method according to the criteria for the three manufacturing industries selected for the study.

As a result of AHP, “Infrastructure for Right Methodology, Techniques and Tools, is in the first place,” Organizational Strategy, is in the second place, while the third highest critical factor is “Capital Investment”. Moreover, based on grey relational analysis (GRA) results, the automotive industry is determined as the best alternative in terms of strategic alignment between operational excellence (OPEX) methodologies and I4.0 technologies.

This study is unique in that it is primarily possible to obtain the order of importance within the criteria and to make comparisons between three important manufacturing industries that are important for the economies of the world.

This study aims to determine how much company participation in a type of cluster affects its economic performance.

This study includes companies operating in seven industries (automotive, engineering, textiles, information technology (IT) services, furniture, packaging and nanotechnology) in the Czech Republic. The companies are divided into three groups: members of institutionalized cluster, operating in the same region (natural clusters) and operating in other regions. Data envelopment window analysis is used to measure their performance for 2009–2019.

Results show that the effect of clustering differs among industries. Companies in three industries (automotive, engineering, nanotechnology) reveal a positive impact of the cluster initiative on performance growth. Two industries (textile, packaging) with companies operating in a natural cluster show better performance than those in an institutionalized cluster. Moreover, the IT services and the furniture industries show no positive effect of clustering on corporate performance.

This research includes 686 companies from seven industries and monitored for 11 years. On the one hand, the sample includes a relatively high number of companies overall; but on the other hand, the sample is relatively small, especially for nonclustered companies. The reason is the lack of available financial statements for small companies.

From the perspective of practical cluster policy, the authors can recommend that monitoring the performance of member companies in clusters must be one of the criteria for evaluating the success of a cluster, such as cluster initiatives.

This study distinguishes between long-standing natural clusters in a given industry and institutionalized ones that have emerged because of a top-down initiative. An original database is created for clustered and nonclustered companies in seven industries, covering the entire Czech Republic.