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This paper aims to determine the type of supplier involvement in terms of time and extent of supplier involvement in automobile product development based on the characteristics of parts in the Iranian automotive industry.

The paper proposes the clustering and analytic hierarchy process (AHP) methods. Combining the K-means clustering method and metaheuristic algorithms, the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are applied to achieve better clustering results.

The results show that lack of internal knowledge, high technology change and complexity of parts increase the need to outsource the design process. In addition to these reasons, high development costs and high interface complexity justify suppliers’ early involvement.

Most research only presents a conceptual framework for understanding the various levels of supplier involvement in new product development (NPD). However, in the automotive industry, numerous parts have differing degrees of importance and priority, and experts may have varying opinions based on different criteria. Therefore, the existing conceptual model for analyzing the types of involvement of each supplier is not practical. We have formulated a problem-solving approach that utilizes the clustering and AHP methods to analyze data obtained from qualitative research and determine the type of supplier involvement.

This paper aims to identify the success factors (SFs) for total quality management (TQM) implementation in automotive spare parts companies to improve their business performance. It also intends to rank these factors in a hierarchical structure in descending order of their criticality.

In this study, a significant number of automotive spare parts companies were extensively surveyed to ascertain the contributions made by various factors toward the successful deployment of TQM practices. The collective and individual evaluation and ranking of the SFs were determined using the analytical hierarchy process (AHP) approach to develop the framework based on the prioritisation of the identified SFs.

The findings of the study show that five success factors, namely, internal environment, top management involvement, process management, supplier management and external environment, were ranked as critical factors with a total weight of 49.2%. Nine success factors, namely, employee training, teamwork, customer satisfaction, continuous improvement, communications, using new technologies, zero-defect processes, employee empowerment and benchmarking, were ranked as important with a total weight of 39.1%. The last five success factors, namely, strategic planning, quality policy, employee satisfaction, self-assessment and cost of quality, were ranked as minor factors with a total weight of 11.7%.

The current study adds to the existing body of knowledge for scholars and practitioners of TQM by specifically focusing on identifying and categorising the critical SFs for TQM implementation. The 19 categorised critical SFs have been used to construct a framework for TQM implementation in the Palestinian automotive spare parts companies. Such a framework would offer a comprehensive overview of the SFs, their categories, significance and priorities within a TQM environment in the automotive spare parts companies.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

To gain and differentiate competitive advantage, the sustainable service quality is a determining factor that railway companies can use. The purpose of this study is to identify both the importance and performance of rail transportation service quality factors in a case study as well as determine the most influential quality features.

A comprehensive approach namely importance–performance analysis (IPA) technique and decision-making trail and evaluation laboratory (DEMATEL), and interpretive structural modeling (ISM) and Matriced’ Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) techniques was utilized.

The relative position of each attribute is specified on the IPA matrix proposing four strategies of concentrate here, keep up the good work, low priority and possible overkill. This study reveals that attributes of “the company cares about having a good society” are the most influential factor, and “having good business relations with shareholders” is the most permeable factor. Actually, consumers pay attention to how companies act toward society and maintain communication with shareholders. Through ISM technique and by summing the row and column of the consistency matrix, the attributes were partitioned into four levels. Also, MICMAC analysis identified the four clusters of linkage, independent, autonomous and dependent status of the attributes in terms of the driving power and dependence power.

Due to the nature of single case study methodology, caution should be taken into consideration regarding the generazability of the obtained results.

The hybrid DEMATEL-ISM technique is used to analyze service quality factors in Iran’s transportation industry, which can be utilized in other industries as well as other countries.

Organizations are continually improving their practices to improve operational performance. They already employ Lean Manufacturing techniques (LM) to reduce unnecessary waste. Industry 4.0 techniques enhance operational performance in association with LM. Despite the proven benefits of LM principles and the advancements offered by Industry 4.0 technologies, many organizations struggle to integrate these approaches effectively. This research paper explores how LM principles can be combined with Industry 4.0 technologies to provide valuable guidance for businesses looking to adopt lean automation strategies.

A systematic literature review on LM and Industry 4.0 was done to investigate the possible technical integration of both methods. Ninety-two articles are extracted systematically from the Scopus and Web of Science databases. This study states a systematic literature review, including quantitative analysis of bibliographic networks and cluster analysis, to identify emergent ideas and their further implementation.

The research findings highlight the positive impact of integrating lean production with Industry 4.0 techniques, benefiting organizations in achieving their goals. A lean automation integration framework is proposed based on the literature review and the findings.

This study provides industry administrators and practitioners valuable guidance for enhancing organizational productivity. These implications can provide businesses with competitive advantages, enhance customer satisfaction, and enable them to adapt to the dynamic demands of the contemporary business environment.

This literature review study has substantially contributed to the technological integration of lean and Industry 4.0. The work has also identified potential emerging areas that warrant further research.

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.

Autonomous and connected mobility technologies have led to a reconfiguration of the automotive industry value chain. This may involve an impact on the geography of the European automotive industry, especially for peripheral countries. The aim of the paper is to analyse the repositioning of a peripheral country (Portugal) in the core-periphery model of the automotive industry derived from this new technological context.

An eclectic theoretical framework, based on the global value chain (GVC) approach, the spatial division of labour and location theory, supports this research. Moreover, an original empirical study was developed. This study comprised a comparative analysis of two samples of firms based on the key variables related to country position. One sample comprised Portuguese traditional automotive firms and the other Portuguese firms linked to autonomous and connected mobility technologies.

The results highlight the upgrading of Portugal in the European core-periphery model of the automotive industry. This is due to the presence of domestic firms, especially multinationals, linked to autonomous and connected mobility technologies in the country. The decision power derived from their position on the first levels of supply and the added value of activities and technological innovation of these new actors change the role of the country in the European automotive industry. The main implication is that managers of domestic firms and policy makers in peripheral countries can upgrade a country’s position in the European core-periphery model by shifting its competitiveness toward knowledge-based activities linked to the new mobility technologies.

This research is supported by a novel eclectic theoretical framework based on the global value chain (GVC) approach, the spatial division of labour and location theory. Moreover, country position is analysed through empirical evidence. An original comparative empirical study based on the key variables defined under this theoretical framework was developed.

This study aims to establish a robust evaluation framework for suppliers within the automotive supply chain, specifically in the stamping sector. The primary objectives are to elucidate the performance criteria of suppliers, identify indicators and scales for measuring these criteria and find the importance of the criteria.

The evaluation framework comprises a criteria hierarchy and indicators developed based on the evaluation criteria of major automotive manufacturers. Specific indicators and measurement scales are recommended for assessing suppliers. Importance weights for the criteria are assigned based on the input of nine experts using the Analytic Hierarchy Process (AHP). Finally, four sheet metal stamping tooling (SMST) suppliers are evaluated by four specialists using the proposed evaluation framework.

The study introduces a novel classification of criteria, encompassing financial and commercial perspectives, delivery capability, supplier facility and cultural approaches and business process necessities. The findings underscore the significance of financial and commercial stability in the selection of SMST suppliers, emphasizing their role in mitigating risks associated with disruptions, bankruptcies and unforeseen events. Additionally, several SMST evaluation factors identified in this study contribute to the development of resilience capabilities, highlighting the crucial importance of their inclusion and assessment in the proposed evaluation framework.

This research presents a comprehensive model for evaluating SMST suppliers, which tackles the multidisciplinary challenges within the automotive supply chain. Given the inadequacy or nonexistence of current SMTS selection models, this study bridges the gap by exploring potential and necessary criteria, alongside 116 specific indicators and measurement scales.

Over the next decade, humanity is going to face big environmental problems, and considering these serious issues, businesses are adopting environmentally responsible practices. To put forward specific measures to achieve a more prosperous environmental future, this study aims to develop an environment-based perspective framework by integrating the Internet of Things (IoT) technology into a sustainable automotive supply chain (SASC).

The study presents a conceptual environmental framework – based on 29 factors constituting four stakeholders' rectifications – that holistically assess the SASC operations as part of the ReSOLVE model utilizing IoT. Then, experts from the SASC, IoT and sustainability areas participated in two rigorous rounds of a Delphi study to validate the framework.

The results indicate that the conceptual environmental framework proposed would help companies enhance the connectivity between major IoT tools in SASC, which would help develop congruent strategies for inducing sustainable growth.

This study adds value to existing knowledge on SASC sustainability and digitalization in the context where the SASC is under enormous pressure, competitiveness and increased variability.