Case Study In Automotive Industry Research Articles

Improving Lead Time through Lean Manufacturing: A Case Study

Lean manufacturing is a well-established methodology aimed at optimizing production by eliminating waste, enabling industries to thrive in a globally competitive environment. This paper presents a case study of a well-known automotive manufacturing industry on the axle process. This article aims the Value Stream Mapping (VSM) methodology was used in the axle process to reduce lead time by producing quality products with a reduction in non-value-added activities. The current state of the axle process was created using industry data from the past six months. Significant improvements were realized following the successful implementation of VSM, including a reduction in the lead time from 89.50 hours to 50.55 hours. The new state map was also created after implementing the improvements. The results illustrated that the VSM increased the effectiveness by 56.48% in the axle process.

Variability in complex product/system design: case study in automotive industry

AbstractThe complexity of the products/systems requires an in-depth understanding of variability and its impact on all phases, from design to maintenance. This study explores Variability Management (VM) emphasizing its challenges. Conducting semi-structured interviews with experts at Renault Group, the research examines variability aspects, semantics, methods, challenges, and possible solutions. The findings offer practical insights into industrial-scale variability management, addressing the use case of the automotive industry.

Designing a green-resilient closed-loop supply chain to preserve business continuity under interrelated disruptions (a case study of the automotive battery industry)

Improving profitability, implementing the recycling cycle to reduce resource consumption, and managing interrelated disturbances to maintain business continuity are important issues for supply chain management. Accordingly, this study proposes a multi-objective optimization model to design a green-resilient closed-loop supply chain considering pricing decisions under multiple interrelated disturbances. Herein, the fuzzy c-means clustering method is employed to lessen probable scenarios and cluster main suppliers and target markets to reduce mathematical complexity. The multi-objective model is solved by adopting the improved augmented ε-constraint method to achieve the minimum total cost, total environmental impact, and network non-resiliency. The mixed uncertainty is coped with by developing a novel robust optimization method. Finally, an automotive battery manufacturing industry case study is applied to the model. The results reveal that the mutual effects of disturbances intensify the deterioration of objective functions. Also, the dynamic pricing of products reduces the financial vulnerability of the supply chain.

Proposal of a maturity model for additive manufacturing: theoretical development and case study in automotive industry

ABSTRACT Currently, organizations in the manufacturing sector are exposed to high levels of competition and constant changes in consumer requirements. To survive in an increasingly competitive environment, it is essential to adopt new technologies to ensure success in a sector that is currently going through the fourth industrial revolution, associated with Industry 4.0 (I4.0). Additive manufacturing (AM) is one of I4.0’s technologies, which can produce products layer by layer, playing a key role in the innovation of business models. In this manner, organizations must integrate AM starting by understanding their maturity level, which allows them to reflect on weaknesses and strengths as well as opportunities for improvement. However, literature is currently lacking maturity models for AM. This paper proposes a maturity model for AM, which aims to help organizations in the manufacturing sector in determining their maturity level regarding the implementation of the AM. The model developed was validated theoretically. Also, a case study was conducted on an automaker, where it was possible to conclude that, despite the analyzed company has a high level of maturity regarding the technological deployment of AM, an AM’s strategy definition is presently missing.

‘Space-time dents’ in global value chains – The Hungarian case

AbstractThe paper takes a special perspective to summarise what researchers have revealed on global value chains in Hungary. The ‘space-time’ structure is how the ‘force field’ of the amount of value added is shared and how the process it creates characterises specific global value chain networks. There is a growing literature that reveals the ‘dents’ of the GVC force field: the uneven distribution of value-added content, and mainly the controversial possibilities to upgrade in the network. Hungary is a typical example of a semi-peripheral or integrated periphery country. The paper discusses the lessons of different global value chain relations of the country in different geographical environments in terms of the two dimensions of ‘space’ and ‘time’; that is the potential and structure of value added and its dynamics, as well as compares them through an automotive industry case study.

Industry 4.0 solutions and company performance: empirical evidence from multiple case study of automotive industry

Purpose: Recent geopolitical, economic, and social changes worldwide directly impact the operations of manufacturing enterprises. This paper aims to highlight the potential outcomes achieved through the implementation of modern technological solutions within the scope of Industry 4.0 in organizations operating in the manufacturing sector. The presented results are part of wider research (Michna et al., 2021). Design/methodology/approach: The research methods consist of a comprehensive literature review of the subject under study and the results of empirical research conducted in 2023 based on multiple case study in production companies in Poland. Findings: The study presents the effects achieved by companies after implementing Industry 4.0 solutions, such as: production cost reduction, increase in profits, productivity and production efficiency and improvements in working conditions. Research limitations/implications: The study was conducted in two selected manufacturing plants operating in the Polish market, with the limitation being the sample selection and the subjective assessment of the study participants. Practical implications: The research results provide an overview of potential and achieved effects and changes in the functioning of the enterprise following the implementation of Industry 4.0 technologies. An additional categorization into effects achieved in socio-economic, environmental areas, and overall corporate governance allows for situating the organization's performance in light of recent legislative changes related to the Fit for 55 packages (Fit for 55, 2021). Originality/value: The study can assist practitioners and specialists in Industry 4.0 in analyzing the potential effects of implementing new technological solutions and aid in planning future activities within the organization. Keywords: Industry 4.0, Results, Performance, Production. Category of the paper: Empirical research results.

An innovative computational algorithm for modelling technical readiness coefficient: A case study in automotive industry

Modelling technical availability is particularly important in systems characterised by randomness in the occurrence of tasks. The aim of the article was to develop an innovative solution using the Laplace transform and the R programming language in the form of an algorithm designed to determine a reliability measure, the technical readiness coefficient. First, the literature on the most important theorems and definitions concerning the theory of reliability was reviewed to develop assumptions and a mathematical model of the actual process of using means of transport. The subject of the research, along with the availability of actual numerical data used for calculations and analyses were presented. An algorithm based on the Laplace transform was proposed to evaluate the reliability of the system. Furthermore, the sensitivity analysis of the model was performed and the results were discussed to identify final conclusions and directions for future research. The results show that the exploitation process of the vehicle fleet studied was correctly organised and that the proposed algorithm eliminates the need to perform complex calculations using traditional methods and enables the visualisation of the achieved results. Additionally, the algorithm is universal and can be used to determine both the functional and numerical reliability characteristics of any technical object. Moreover, this solution is free of charge, and its implementation requires only the installation of the RStudio editor (version 1.4.1106).

The Effect of Customer Trust on Repurchase Intention Through User-Generated Content as an Intervening Variable in The Automotive Industry Case Study @Bandarmotor_

Social media is used for brand awareness. Instagram ranks third as a media that is often used in Indonesia. Bandarmotor is a shop that provides spare parts and two-wheeled vehicle service services located in South Banyuwangi. If there is no increase in followers and decrease in turnover, research is carried out to find out whether it is true that customer trust will increase motivation to create user-generated content and the desire to repurchase Bandarmotor. The study population of Instagram followers of @bandarmotor_ account amounted to 1533 people. The study sample using the purposive sampling method was 73 people. Questionnaires using Google forms. The analysis method uses Partial Least Squares-Structural Equation Modeling (PLS-SEM) with SmartPLS software. The results showed that Customer trust had a significant positive effect on user-generated content. Customer trust has a significant positive effect on repurchase intention. User-generated has a significant positive effect on repurchase intention. User-generated content has a significant positive effect on customer trust and repurchase intention.

Supplier selection by integrated IFDEMATEL-IFTOPSIS Method: A case study of automotive supply industry

Selecting the best supplier emerges as a crucial subject for all sectors to achieve long term collaborations in supply chains. This study object to select the most suitable supplier for a company engage in activities in the automotive supply industry. For this purpose, a five-stage Intuitionistic Fuzzy Multi-Criteria Decision Making (IFMCDM) model is conducted. Firstly, decision criteria are defined by literature research and expert group opinions. Secondly, the importance weights of these criteria are obtained by IF Decision Making Trial and Evaluation Laboratory (IFDEMATEL). Followingly, the most suitable supplier is assessed by IF Technique for Order Preference by Similarity to Ideal Solution (IFTOPSIS). In the fourth stage, Sensitivity Analysis is utilized to analyze the effect of differentiation in criterion. Lastly, a comparative analysis is carried out. The results of the study has pointed that “Price” is the most important criterion in supplier selection and “Supplier 4” is the best alternative for this case. Main contribution of this study is to integrate IFDEMATEL-IFTOPSIS method for the first time in automotive supplier selection literature and propose a specific decision framework. In addition, proposed model is found robust and valid.

The Influence of Green Supply Chain Management (GSCM) on Environmental Performance: Case Study of Automotive Industry in Malaysia

The Influence of Green Supply Chain Management (GSCM) on Environmental Performance: Case Study of Automotive Industry in Malaysia

Service-oriented decomposition and verification of hybrid system models using feature models and contracts

• Simulink services to enable modular development and reuse. • Hybrid contracts as formal interface description. • Feature modeling to capture variability in Simulink services. • Compositional verification of hybrid systems modeled in Simulink. • Semi-automatic verification with KeYmaera X. The design of fault-free hybrid control systems, which combine discrete and continuous behavior, is a challenging task. Their hybrid behavior and further factors make their design and verification challenging: These systems can consist of multiple interacting services, and commonly used design languages, like MATLAB/Simulink do not directly allow for the verification of hybrid behavior. Furthermore, services can be reused in new system designs and sometimes require changes in their structure to fit to the new context. By providing hybrid contracts , which formally define the interface behavior of hybrid system services in differential dynamic logic ( d L ), and providing a decomposition technique, we enable compositional verification of Simulink models with interacting services. This enables us to use the interactive theorem prover KeYmaera X to prove the correctness of hybrid control systems modeled in Simulink. With the inclusion of feature modeling in the design process and the creation of hybrid contracts, we are able to capture a wider range of behavior, while still enabling the use of formal interface description in the verification. We demonstrate the applicability of our approach with a temperature control system and an automotive industrial case study.

We are never ever getting back together: Constraints on business relationship reactivation after bankruptcy-acquisition

The purpose of this paper is to create a conceptual model of constraints on business relationship reactivation after bankruptcy-acquisition. Previous research has predominantly focused on contexts where positive aspects of reactivation come to the forefront; accordingly, it is argued that more research is needed on elements constraining reactivation. A case study in the Swedish automotive industry is used to develop the model. The model contributes to studies on business relationship reactivation by showing that during different relationship episodes (ending, dormant and expected future episodes) it is possible to identify elements that constrain a subsequent reactivation. In the empirical context of the study, these elements are unsettled debts, unilateral switching costs and expected future losses.

Using fuzzy logic to support maintenance decisions according to Resilience-Based Maintenance concept

Many authors have highlighted the importance of physical assets maintenance management in relation to resilience engineering, especially for systems operating under significant uncertainty. Thus, the authors presented a new approach to system maintenance based on resilience concept implementation. They introduced Maintenance Support Potentials (MSP) as a measure of an organization's maintenance support capacity. Moreover, based on the MSP definition, they developed a fuzzy-based organization's maintenance support potential level assessment method. The proposed approach takes into account two main MSP parameters – potential readiness level and process regency. It followed four main steps, including organization's MSP identification/evaluation, MSP weights assessment, Maintenance Support Capacity assessment, and final reasoning. A case study of a global manufacturer from the automotive industry is presented to illustrate the method's applicability. The authors also indicated further research directions to optimize the maintenance strategy based on Resilience-Based Maintenance concept.

Proposed maintenance policy using reliability centered maintenance (RCM) method with FMECA analysis: A case study of automotive industry

PT XYZ is one of the companies engaged in the automotive industry, especially in the motorcycle industry that produces spare parts on motorcycles. One component made by PT XYZ is Guide Comp Level K1AA. The problem in the company is one of the machines to produce the Guide Comp Level K1AA component, which is a Press machine that has a high frequency of failure. The Press machine itself consists of electrical and mechanical systems that each system consists of sequential subsystems as many as 6 subsystems and 10 subsystems. By using risk matrix, obtained two critical subsystems namely brake and trigger. Then the analysis of failure mode, effects and criticality analysis (FMECA) was carried out with output in the form of risk priority number (RPN), obtained that the selected critical subsystem is the brake. Using the reliability centered maintenance (RCM) method, then obtained the maintenance policy for the brake critical subsystem are 2 scheduled on condition task with interval time is 2.55 months and 2 scheduled restoration task with 5,394 months. The proposed maintenance policy also decreases the total maintenance cost for the critical subsystem brake up to 25% from existing maintenance costs.

EU industrial policy: Between modernization and transformation of the automotive industry

The transport sector accounts for 27 % of total CO2 emissions in the EU, with almost half of these being attributed to passenger cars and the automotive industry. In order to meet the Paris Agreement 1.5 °C target, these figures call for interventionist policies that go beyond individual consumer choice and transform industrial sectors. The article conceptually elaborates on the advancements in transformation and transition studies to analyse the role of industrial policy for accelerating social-ecological transformations. The article empirically analyses key characteristics of EU industrial policy and critically discusses its potential for transforming the automotive sector, based on a qualitative case study of the Austrian automotive (supplier) industry. Our analysis suggests that EU industrial policies at most ecologically modernise and at worst actively conserve the unsustainable structures of the automotive industry. This is because EU automotive industrial policies (1) defend economic growth and competitiveness, (2) focus narrowly on innovation (policy) and refuse to disrupt unsustainable industrial pathways as well as (3) promote ecological modernisation through efficiency instead of absolute emission reductions. The article concludes with entry points for a transformative industrial policy beyond ecological modernisation.

Evaluation of Economic Plausibility of Integrating Maintenance Strategies in Autonomous Production Control: A Case Study in Automotive Industry

Abstract Recent advances in autonomous production control (APC) enhance to cope with growing challenges of manufacturing industry regarding high delivery accuracy and short delivery dates, i.e. concurrently dealing with shorter planning horizons, increasing planning complexity and an increasing rate of changes in planning. However, the majority of APC approaches are based on idealized assumptions, lack integrative planning, and control mechanisms. In particular, the state-of-the-art APC approaches mainly ignore maintenance planning strategies, related decision factors, and maintenance measures. The ultimate practical implications are a reduced decision quality, and a resulting low acceptance of APC systems. In most cases, therefore, implementation of APC systems in industrial applications are not yet affordable. Pursuing this line of research, this paper presents a conceptual model for integrating maintenance strategies in APC. The focus is on evaluation of economic plausibility of the integrated approach, which deploys a cost function for a market-oriented decision-making. Using an agent-based simulation environment, the model is evaluated in an industrial use case in the automotive sector.

A Multicomponent Distributed Framework for Smart Production System Modeling and Simulation

In order to control manufacturing systems, managers need risk and performance evaluation methods and simulation tools. However, these simulation techniques must evolve towards being multiperformance, multiactor, and multisimulation tools, and this requires interoperability between those distributed components. This paper presents an integrated platform that brings interoperability to several simulation components. This work expands the process modeling tool Papyrus to allow it to communicate with external components through both distributed simulation and cosimulation standards. The distributed modeling and simulation framework (DMSF) platform takes its environment into consideration in order to evaluate the sustainability of the system while integrating external heterogeneous components. For instance, a DMSF connection with external IoT devices has been implemented. Moreover, the orchestration of different smart manufacturing components and services is achieved through configurable business models. As a result, an automotive industry case study has successfully been tested to demonstrate the sustainability of smart supply chains and manufacturing factories, allowing better connectivity with their real environments.

International Joint Venture (IJV) in emerging economies: Case Study of Automotive Industry

International Joint Venture (IJV), a popular form of Foreign Direct Investment (FDI), is a favourite mode for automobiles multinational companies (MNCs) to expand into new markets, especially emerging economies. However, many things are considered by MNCs in selecting investment destinations, including industry growth and the quality of destination countries. Therefore, emerging economies need to show their attractiveness to attract MNCs to invest there. This research has analysed the growth of the automotive industry in two emerging economies, India and Indonesia, and its economic impacts, and provides information on the contribution of the IJV to the host country and explains the attractiveness of the two countries, particularly in the automotive field. The findings on the research are useful in giving consideration to MNCs to invest in both countries, especially in the automotive field. Furthermore, it also provides information to emerging economies about the contribution of the IJV company to the host country.

Internal logistics flow simulation: A case study in automotive industry

ABSTRACT This work focuses on internal logistics (IL), which is defined as the flows of materials inside the same business or the same plant. We centre on the IL activities of a car-assembling company. The main objective is to propose simulation models’ concepts that evaluate assembling lines under the IL point of view. We present a real case study based on SEAT S.A., a company in the Volkswagen group. We developed a Discrete-Event Simulation model through the Plant Simulation software. The company set the main Key Performance Indicators, which are related to the logistics flows’ performance (LFP) and an assembly lines’ aisles utilisation. The results expose which aisles are overused, the disturbs among the logistics flows, and the LFP, regarding the total of backorders, the trips duration, and the routes’ length in terms of distances and time. Moreover, we offer aset of best practices for further applications in that field.

A simulation-based methodology for aiding advanced driver assistance systems hazard analysis and risk assessment

The increasing complexity of the Advanced Driver Assistance Systems (ADAS) is making more difficult to perform the Hazard Analysis and Risk Assessment (HARA). These items require high-performance Electronic Control Units (ECU) with extensive software functionalities. To correctly operate they interact with the driver, environment and other vehicle functions through high-speed in-vehicle networks, as well as a wide range of sensors and actuators. As a result, they implement complex behaviors whose outcome in presence of faults is not trivial to identify and classify as requested by the concept phase included in the most recent functional safety standards. In this paper we present a simulation-based methodology to perform the HARA of a vehicle function by mixing the usual industrial approach, based on the designers' knowledge, with one that makes use of a vehicle-level simulator. The simulation-based approach provides an automatic and systematic method to assess the complex interaction of the item under analysis with other vehicle functions in possibly complex operational situations, thus making the prediction of hazards easier. We choose to demonstrate the approach by applying it to a well-known automotive industry case study: an Advanced Emergency Braking System (AEBS). In this way, it is possible to analyze the effects of the function provided by the item, keeping into account the simulations results and comparing them to similar situations analysis available in literature. Thanks to the obtained simulation-based results, safety engineers can formulate a more objective hypothesis, in particular during the hazard classification subphase.