Multinational Automotive Company Research Articles

The maximum length car sequencing problem

This paper introduces the maximum length car sequencing problem to support the assembly operations of a multinational automotive company. We propose an integer linear programming (ILP) formulation to schedule the maximum number of cars without violating the so-called option constraints. In addition, we present valid combinatorial lower and upper bounds, which can be calculated in less than 0.01 s, as well as binary and iterative search algorithms to solve the problem when good primal bounds are not readily available. To quickly obtain high-quality solutions, we devise an effective iterated local search algorithm, and we use the heuristic solutions as warm start to further enhance the performance of the exact methods. Computational results demonstrate that relatively low gaps were achieved for benchmark instances within a time limit of ten minutes. We also conducted an instance space analysis to identify the features that make the problem more difficult to solve. Moreover, the instances reflecting the company’s needs could be solved to optimality in less than a second. Finally, simulations with real-world demands, divided into shifts, were conducted over a period of four months. In this case, we use the proposed ILP model in all shifts except the last one of each month, for which we employ an alternative ILP model to sequence the unscheduled cars, adjusting the pace of the assembly line in an optimal fashion. The results pointed out that the latter was necessary in only one of the months.

A decision-making framework with machine learning for transport outsourcing based on cost prediction: an application in a multinational automotive company

A decision-making framework with machine learning for transport outsourcing based on cost prediction: an application in a multinational automotive company

An Investigation into the Reason for the Decrease of Tesla's Stock Price Despite Obtaining a Great Sale in 2022

Tesla, a multinational automotive and clean energy company, is one of the world's most valuable companies with its CEO Elon Musk. However, in 2022, its stock price decreased by 65% although its sales and revenue went on very well. As a result, this paper tends to find out what led to this dramatic decline through literature reviews and the authors additional analysis. This paper would solve this puzzle from the following four aspects: Musks acquisition of Twitter, a less promising NEVs market, threatening changes in Teslas biggest market (China), and the increase in the interest rate in the U.S.

Initial stage of an industrial investigation of the knowledge management practices in a large-scale multinational automotive company

Initial stage of an industrial investigation of the knowledge management practices in a large-scale multinational automotive company

Initial stage of an industrial investigation of the knowledge management practices in a large-scale multinational automotive company

Initial stage of an industrial investigation of the knowledge management practices in a large-scale multinational automotive company

Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty

The response surface methodology (RSM), which uses a quadratic empirical function as an approximation to the original function and allows the identification of relationships between independent variables xi and dependent variables ys associated with multiple responses, stands out. The main contribution of the present study is to propose an innovative procedure for the optimization of experimental problems with multiple responses, which considers the insertion of uncertainties in the coefficients of the obtained empirical functions in order to adequately represent real situations. This new procedure, which combines RSM with the finite element (FE) method and the Monte Carlo simulation optimization (OvMCS), was applied to a real stamping process of a Brazilian multinational automotive company. For RSM with multiple responses, were compared the results obtained using the agglutination methods: compromise programming, desirability function (DF), and the modified desirability function (MDF). The functions were optimized by applying the generalized reduced gradient (GRG) algorithm, which is a classic procedure widely adopted in this type of experimental problem, without the uncertainty in the coefficients of independent factors. The advantages offered by this innovative procedure are presented and discussed, as well as the statistical validation of its results. It can be highlighted, for example, that the proposed procedure reduces, and sometimes eliminates, the need for additional confirmation experiments, as well as a better adjustment of factor values and response variable values when comparing to the results of RSM with classic multiple responses. The new proposed procedure added relevant and useful information to the managers responsible for the studied stamping process. Moreover, the proposed procedure facilitates the improvement of the process, with lower associated costs.

THE UPGRADED ROLE OF THE VALUE STREAM DESIGN FOR REDESIGNING THE FACTORY LAYOUT

The case study shows the re-optimization of an initial new factory layout design with Value Stream Design (VSD). The VSD is a quantitative method and its’ final goal is to make a waste free optimized material flow. The primary goal of arrangement is to reduce transportation distances and frequencies, optimize human load. Initially the whole factory shop floor layout design was already made in push concept. The plans were made by production management, logistics, engineering department at the headquarter of the multinational automotive company with based on VDI2870 holistic concept linking strategy on tactics and operation. On the layout (v1.) the hundreds of machines were placed and arranged by CAD (Computer Design) engineers to fit the space. The factory building has 15,000 m2 with empty shop floor waiting for the final decisions for equipment. The factory production area was shared into six main production areas (P1-P6), which correlates with their product complexity of the product families. Each production area output can be finished product (FP) or semi-finished product (SFP) for the next production areas. To validate the whole factory layout it was necessary to involve lean experts that identified disadvantages and constraints. Without lean implementation the company’s transportation waste would be 49% more per year. The Value Stream Design importance nowadays is upgrading to a higher level, when the whole global business is changed, the labor force fluctuates, and the cost and delivery time reduction plays a vital role in the company’s profit and future. The research shows that if the decision taking is based on real data and facts the controlling and management can do its best in time. Using VSD and re-evaluating the transportation routes, frequency and costs is the first step to define a smooth, low cost, material flow (v2.). This development ensured the company to drive from push to pull production through mixed production system. Originally, the production flow was clockwise orientation. It was changed step by step to mixed production by eliminating work in process storages, implementing FIFO lanes, Milk Run, and Kanban. The total annual transportation distances were reduced from 4,905,000 m between the rump-up and serial production period. The warehouse storage size was reduced to 50% and implementation cost from €75,000 to €32,500. By eliminating work in process storages along production lines it was possible to open a new two way transportation road that also will serve the AGV’s operations in industry 4.0 projects. Due to decreased lead time the logistic labor productivity increased by 45%. Besides taking measurements for the VSD it was used Value Stream Mapping as a lean tool and an own designed VSD evaluation and a simulation software. The VSD team’s cooperative actions reduced the evaluation and validation time with 65% then it was initially planned. The implementations were evaluated from the rump-up phase to the first serial productions and the results were confirmed by controlling and management

CROSS BORDER ACQUISITION OF JLR: A BOON OR PAIN FOR TATA MOTORS

ABSTRACT The trend of mergers and acquisitions (M&A) has seen a surge during the past decades. Indian companies have also adopted the strategy of mergers and acquisitions for their expansion & growth. It facilitates the corporations in gaining competitive advantages and increase in their shareholder’s wealth. The study till date analysed both inbound and outbound deals and M&A’s positive or adverse impact on a company. Studies conducted in the past showed that the rate of unsuccessful mergers and acquisitions were more in number than the successful ones. This paper attempts to evaluate the effect of Earnings before Interest and Tax (EBIT) of Jaguar & Land Rover (JLR) on share prices of Tata Motors. Tata Motors is an Indian conglomerate automotive company of Tata Sons based in India and also the parent of JLR. Tata Motors has acquired JLR, a British multinational automotive company, in 2008 for $2.3bn. This study has taken price movement of a share of Tata Motors on NSE for the year 2008 to 2018 and correlated it with the corresponding years’ EBIT of JLR which significantly contributes to profits of Tata Motors in the form of dividends. Thirteen weeks price moving an average of Tata Motors on NSE is taken as one set of variable, and quarterly EBIT of JLR is taken on another. The study has also taken into consideration various macroeconomic factors that would have had an impact on the performance of JLR and in turn on the prices of Tata motors. This study found that the EBIT of JLR is positively correlated with the share price of Tata Motors. Keywords Acquisition, Earning Before Interest and Tax, Share Price, Correlation

In pursuit of Digital Manufacturing

Abstract Companies are adopting several new technologies that form the pillars of Industry 4.0 production framework, of which Digital Manufacturing (DM) stands out by combining conventional manufacturing technologies with digital techniques. These are used to assist in the design and analysis of the product and manufacturing processes. The adoption of digital manufacturing is partly about technological change, but it also entails significant organizational issues, which often are overlooked by managers. The purpose of this study is to identify the key factors that enable or prevent DM implementation, considering the production paradigm of Industry 4.0. Based on a literature review that identified a preliminary list of key factors, the appropriateness of these factors is empirically tested and refined in a two-fold approach: an in-depth pilot case in a multinational automotive company that is adopting DM technologies, and a survey of 113 users, managers, implementers and researchers working on digital manufacturing and Industry 4.0. The study identified 24 key factors to be considered when firms implement DM. These are categorized into technical, organizational, project based and external factors. The findings also indicate how each factor should be considered, and that they cannot be generalized due to cultural differences inherent to each individual company. As such, this research contributes to the current research debate by identifying the critical factors to be considered when conceiving and applying models for planning and executing DM implementation.

Managing Human Errors: Augmented Reality systems as a tool in the quality journey

Abstract The manufacturing industry is shifting, entering a new era with smart and connected devices. The fourth industrial revolution is promising increased growth and productivity by the Smart Factory and within the enabling technologies is Augmented Reality (AR). At the same time as the technology is introduced, errors in manufacturing are a problem which is affecting the productivity and the quality. This research aims to find the main causes of human errors in assembly lines and thereafter explores whether augmented reality is an appropriate tool to be used in order to address those issues. Based on a literature review that identified and characterized a preliminary set of root causes for human errors in assembly lines, these causes were empirically studied in an exercise that covered an in-depth case study in a multinational automotive company. Data in form of interviews and deviation- reports have been used to identify the causing factors and the result showed that the main causes of human errors are the amount of thinking, deciding and searching for information which affected the cognitive load of the operator and in result their performance. Several interviews with experts in augmented reality allowed to verify if AR technology would be feasible to solve or mitigate the found causes. Besides that, in repetitive manual assembly operations, AR is better used showing the process in order to train new operators. At the same time for experienced operators, AR should show information only when an error occurs and when there is a need of taking an active choice. Nevertheless, while theoretically able to managing human error when fully developed, the desired application makes the augmentation of visual objects redundant and increasingly complex for solving the identified causes of errors which questions the appropriateness of using AR systems. Furthermore, the empirical findings showed that for managing human errors, the main bottleneck of an AR system is the systems artificial intelligence capabilities.

Returnable packaging as a sustainability factor in the automotive chain: a case study.

Packaging makes a valuable contribution to economic, environmental and social sustainability through product protection, waste prevention, enabling efficient business conduct and providing consumers with the benefits of the products it contains. The case study chosen for the work was carried out in a multinational automotive company with the objective of researching the financial and environmental factors in the substitution of one-way packages by the returnable ones during the distribution of parts for automotive dealers. There is no single solution to manage packaging waste. The best combination of options depends on local conditions, especially demographics and the degree of investment made in modern processing systems. Determining the best combination of options for managing packaging waste therefore requires detailed case-by-case analysis where life-cycle assessments can provide valuable decision support.

The advanced role of corporate foresight in innovation and strategic management — Reflections on practical experiences from the automotive industry

This paper portrays the current state of practice of a corporate foresight unit within a multinational automotive company, working with this task assignment for more than three decades. It describes how the early detection of medium- to long-term developments in the broader business environment, including social and market developments, is integrated into innovation and strategy processes. The purpose of this paper is to give an account of foresight practice in an automotive company that (1) covers the full range of roles and practices of a mature foresight group, (2) describes case examples how such roles and practices are played out in practice and (3) concludes with general recommendations for organizational design and processes. The paper highlights five major fields of foresight practice which encompass strategic issues in different working fields: early detection in new business environments, trend research for the generation of product innovations, prospective evaluation of innovation ideas, exploration and development of new business, and cross-functional dissemination of future-related issues. Practice examples in each field detail features of the integration of foresight in corporate processes. The paper finishes with characterizing the organizational architecture of a mature corporate foresight group, a discussion and overall conclusions.

A case study to evaluate lean product development practices in the global automotive industry

This paper describes the findings of an exploratory industrial investigation to evaluate the challenges encountered in the transformation to achieve Lean Product Development (LPD) through a case study within a multi-national automotive manufacturing company. Three specific key areas within LPD were examined in detail: (a) organisational restructuring aimed at balancing global functional engineering expertise and cross functional integration, (b) growing and maintaining technical competencies across a global product development workforce, and (c) the motivation for the separation of the in-house manufacturing divisions and subsequent integration of external specialist suppliers into the global product development process. The research examines the evolution in practices at the company since the last decades of the 20th century, through to the latest challenges in delivering global platform programmes. A comprehensive overview of related literature is also presented, which provides contextual information and justifies the selection of the key areas of focus in this study. Finally, recommendations for further research directions in this context are provided.

Improving intranet knowledge transfer through resident engineers

PurposeThis research paper aims to explore how intranet mechanisms for knowledge transfer in a multinational automotive company can be improved with the inclusion of a human interface, specifically a resident engineer. A resident engineer is an assembly line employee who spends the majority of their time in the Advanced Engineering Center, which aims to improve knowledge transfer through face‐to‐face interaction.Design/methodology/approachA multinational automotive company was selected for the study. Six years of data containing all product modifications were analyzed.FindingsThe paper concludes that electronic knowledge transfer can be significantly improved when resident engineers physically interact with the Advanced Engineering Center. Intranet knowledge transfer alone cannot overcome the considerable handicap involved in knowledge assimilation.Practical implicationsFor companies with Advanced Engineering Centers located far from assembly‐line plants, the creation of specific human mechanisms to explain the knowledge generated and to reduce approval time is indispensable.Originality/valueThis research paper features a complete case study from a multinational automotive company with more than 800 analyzed modifications.

New product development projects prioritization with Analytic Hierarchy Process in an automotive company

This work presents results from a research developed in a multinational automotive company. The main objective of this research was to prioritize New Product Development (NPD) projects. NPD projects prioritization is a Multiple Criteria Decision Making (MCDM) problem. Analytic Hierarchy Process was applied to solve this decision-making problem. Mathematical Modeling, a research method of qualitative strategy was initially adopted. However, a mixed strategy was followed, as concepts of Case Study were included in the research. Therefore, that research did not the purpose to be exhaustive, that is, initially, one of research objectives was to model, with MCDM, a real case of NPD projects prioritization. Nevertheless, the decision-making model and results presented in this work can be considered by other companies, mainly from automotive sector.

Affective Commitment to Change and Innovation Implementation Behavior: The Role of Charismatic Leadership and Employees’ Trust in Top Management

This questionnaire-based study investigated the relationship between two aspects of leadership (charismatic leadership and trust in top management) and followers’ innovation implementation behavior. Findings from 194 employees working in R&D teams of a multinational automotive company indicated that charismatic leadership and trust in top management were both positively related to innovation implementation behavior, controlling for followers’ individual differences, management level, and department affiliation. The findings demonstrate that both relationships were mediated by followers’ affective commitment to change. Results implicate the need to more closely bond the concepts of affective commitment to change and innovation implementation behavior and consider their connection in future investigations.

Corporate foresight: integrating the future business environment into innovation and strategy

This paper focuses on the practice of corporate foresight within a multinational automotive company. It portrays how the early detection of medium- to long-term developments in the broader business environment, including social and market developments, is integrated into innovation and strategy processes. The objective of the paper is to portray the genesis of corporate foresight, major working areas and benefits for the company. The initial section highlights the driving forces that promoted the establishment of corporate foresight, namely the convergence of strategic policy intelligence with strategic early recognition and futures research. The core section highlights five major fields of foresight practice which encompass strategic issues on different levels: long-term market developments, future customer needs, prospective evaluation of innovations, business processes and organisational change, and scanning and monitoring. The paper concludes with characterising the organisational architecture of a mature corporate foresight group.