Automotive Original Equipment Manufacturers Research Articles

Dimensions of Digital Transformation for Digital Supply Chains—Evidence from an Automotive OEM Group

Overview: This article explores the implications of digital transformation on digital supply chains, particularly in a group of automotive companies. We conducted an in-depth case study in a conglomerate of companies that supply auto parts to original equipment manufacturers (OEMs) located in southern Brazil. This case was intentionally chosen because of the Randon Company’s commitment to innovative practices during the digital transformation process. We identified five dimensions of digital transformation that impact digital supply chain performance: (1) digital governance, (2) smart digital networks, (3) digital empowerment, (4) digital automation, and (5) digital workforce. Demands for greater efficiency, new technologies, market differentiation, innovation, and sustainability drive digital supply chains to strongly interact with these dimensions. We propose a multidimensional framework that highlights the dynamics and interdependence of these digital transformation dimensions and how they can potentially meet digital supply chain demands. We provide managerial implications and ideas for future research.

What are the root causes of material delivery schedule inaccuracy in supply chains?

PurposeThis study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?Design/methodology/approachA mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.FindingsThe effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.Practical implicationsThe findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.Originality/valueThe findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

How to load your auto carrier. A hybrid packing approach for the auto-carrier loading problem

The distribution of new vehicles is a critical cost factor for automotive original equipment manufacturers (OEMs). While trains are the ideal mode for overland transport, OEMs often opt for road transport via auto carriers due to flexibility reasons or unavailability of rail networks. Auto carriers are specialized trucks equipped with flexible loading platforms. Finding the optimal configuration of platforms can be a difficult task due to passenger vehicles varying in size, shape, and weight. This problem is known as the auto-carrier loading problem (ACLP). The current literature deals with the ACLP on a strategic level with the goal to maximize transport capacities. However, it neglects the resulting increase in operational complexity.In this paper, we present a hybrid packing approach to solve the ACLP on an operational level. Our approach considers the shapes of the vehicles as polygons and incorporates an approximation of continuous rotation by combining a geometric algorithm with a mixed integer model formulation. We aim not only to maximize loading capacities but also to provide instructions for truck drivers on how to arrange the vehicles on the truck. We conduct a proof of concept (POC) with actual auto carriers to verify the feasibility of our approach and to quantify potential benefits for our industry partner. The POC confirms that our approach reliably generates feasible and comprehensive loading instructions. Not only could the implementation of our approach reduce lead times and transportation damages, but in addition, could completely automate the manual load creation process in distribution centers.

Carbon footprint analysis of Jaguar Land Rover Slovakia s.r.o.

The automotive industry around the world, including Slovakia, is taking steps to decarbonize their production in accordance with the Science Based Targets initiative (SBTi). Decarbonizing the operations of automotive Original Equipment Manufacturers (OEMs)requires a holistic and systemic understanding of all environmentally relevant resource flows and the implementation of technologically and economically affordable solutions to achieve their targets for reducing the carbon footprint (CF). The CF analysis of Jaguar Land Rover Slovakia s.r.o. (JLR SK) was performed using the Greenhouse Gas Protocol (GHG Protocol). The purpose of the study was to improve theunderstanding of CF within the context of an OEM. The authors of this study are not aware, as of the date of publication, of any other comparable outputs from CF analyses by GHG Protocol in Slovakia, making this study the first of its kind. The results showed that the total CF was 1,143,205 tCO2e(tonnes of carbon dioxide equivalent); of which SCOPE 1 emissions were 18,584 tCO2e (1.62%), SCOPE 2 emissions were 0.87 tCO2e (0.0001%) and SCOPE 3 emissions were 1,130,866 tCO2e (98.38%). Normalized CF values for all three SCOPES were 309.81 tCO2e per active employee; 3.08 kgCO2e per unit of turnover (EUR); and 14.95 tCO2e per vehicle produced. Current legislative standards and the active participation of OEMs can lead the automotive industry towards sustainability, including the achievement of net-zero carbon emissions despite rising consumption or production volume. With respect tosome ambiguities in the initial data inventory, the presented analysis shows the first approach to identifying the CF of an automotive factory based on GHG Protocol in Slovakia.

ChiltonLibrary

ChiltonLibrary is a collection of automotive original equipment manufacturer (OEM) repair and maintenance articles and diagrams. ChiltonLibrary includes makes and models from the 1940s to current model years. The current version has added accessibility features, including ReadSpeaker text-to-speech versions of articles. ChiltonLibrary also includes technical service bulletins/recall information, automotive repair videos, and ASE test prep quizzes. ChiltonLibrary is an authoritative resource that benefits from an intuitive interface and a mobile-responsive design.

Load Factor Optimization for the Auto Carrier Loading Problem

The distribution of passenger vehicles is a complex task and a high cost factor for automotive original equipment manufacturers (OEMs). On the way from the production plant to the customer, vehicles travel long distances on different carriers, such as ships, trains, and trucks. To save costs, OEMs and logistics service providers aim to maximize their loading capacities. Modern auto carriers are extremely flexible. Individual platforms can be rotated, extended, or combined to accommodate vehicles of different shapes and weights and to nest them in a way that makes the best use of the available space. In practice, finding feasible combinations is done with the help of simple heuristics or based on personal experience. In research, most papers that deal with auto carrier loading focus on route or cost optimization. Only a rough approximation of the loading subproblem is considered. In this paper, we present two different methodologies to approximate realistic load factors considering the flexibility of modern auto carriers and their height, length, and weight constraints. Based on our industry partner’s process, the vehicle distribution follows a first in, first out principle. For the first approach, we formulate the problem as a mixed integer, quadratically constrained assignment problem. The second approach considers the problem as a two-dimensional nesting problem with irregular shapes. We perform computational experiments using real-world data from a large German automaker to validate and compare both models with each other and with an approximate model adapted from the literature. The simulation results for the first approach show that, on average, for 9.37% of all auto carriers, it is possible to load an additional vehicle compared with the current industry solution. This translates to 1.36% less total costs. The performance of the nesting approach is slightly worse, but as it turns out, it is well-suited to check load combinations for feasibility.

Electric vehicle charging network design with capacity and service considerations

Electric Vehicles (EVs) have a critical role in the future of sustainable transportation systems. Recently, improving the infrastructure for EVs has been a major focus of organizations around the world, such as local and federal governments, energy companies, and automotive original equipment manufacturers. In this article, we present a service network design framework to improve the infrastructure for EVs with quality of service constraints taken into consideration. We present a novel charging station location model with capacity allocation. The model maximizes the EV traffic flow captured and minimizes the average service time for drivers. The model formulation allows for assessing capacity and quality of service trade-offs, and operational insights are presented to reflect drivers’ expectations. We demonstrate an application for the solution approach on a real road network and evaluate the performance based on both computational efficiency and solution quality. Our results highlight how charging times at stations affect the quality of service in the whole service system by quantifying the improvements in flow coverage while reducing waiting times. The value of faster chargers in increasing coverage and reducing the average waiting times is also highlighted in relation to a service provider’s budget constraints.

Sustainability accounting for greenhouse gas emissions measurement using the GREET LCA model: practical review of automotive ESG reporting

AbstractThe production and personal use of passenger vehicles contribute significantly to greenhouse gas (GHG) emissions, making personal transport a major contributor. In response to increasing pressure from regulators and consumers to lower emissions, original equipment manufacturers (OEMs) have introduced alternative powertrains, such as battery electric vehicles (BEVs). To assess the economic and environmental feasibility of BEVs, OEMs conduct various life cycle assessment (LCA) approaches as part of their internal management practices. However, the public presentation of their results is often ambiguous and of unverifiable quality. This study conducts a review of sustainability impact reporting using the GREET model, which is based on the total vehicle life cycle. The paper provides a summary of the input data requirements for validating emissions from both the fuel and vehicle cycles as part of the proposed model. The availability of the model components was tested against actual public corporate environmental, social and governance reporting disclosures of the largest global automotive OEMs, indicating a deficit in the public presentation of essential sustainability information. The implications of the research suggest that LCA models could benefit sustainability accounting by incorporating proper tagging during accounting practices and taking advantage of digital accounting and reporting practices such as the extensible business reporting language (XBRL). By further developing the model, sustainability reporting can benefit from its structure and workflow, thus ensuring improved information validity for stakeholders. Graphical abstract

Rubber Comprehensive Constitutive Equation and the Prediction of Tire Temperature and Rolling Resistance

ABSTRACT Cooler running tires with a reduced rolling resistance is a consideration for both tire makers and automotive original equipment manufacturers. To investigate the mechanics of energy loss and temperature rise, a nonlinear viscoelastic model suitable for numerical analysis of rolling tires is developed and demonstrated. The rubber compounds are represented as nonlinear viscoelastic materials with temperature and frequency, strain, and strain history–dependent response. Several applications to different tire designs are provided to demonstrate the impact of this new material constitutive law in improving the quality of the numerical predictions over other methods. One application is a rolling tire model that was used to delineate the tire temperature distribution and the resultant rolling resistance for different three-dimensional tread patterns.

Effect of lignin acetylation on the mechanical properties of lignin-poly-lactic acid biocomposites for advanced applications

Bioplastics that possess characteristics like durability and low cost are desired for versatile applications in industries such as automotive manufacturing, marine transport manufacturing, aerospace applications, and the building industry. The automotive industry is an example of an industry that is now shifting towards a more focused approach addressing the issue concerning sustainability and the development of sustainable material. To achieve a lightweight and sustainable construction, one of the methods used by the automotive original equipment manufacturers is by substituting conventional fossil-based, non-renewable composites, and metallic materials with a bio-based alternative. One of the drawbacks with biobased polymers can be the poor interfacial adhesion, leading to poor mechanical properties when compares to conventional material. The aim of this research is to investigate if a low-cost by-product could be used as a component in a composite matrix material in the automotive industry to reduce the final weight and increase the non-petrochemical material usage of composite material without compromising the thermal and mechanicals properties demanded. In this research, lignin was chemically altered by esterification the functional groups to increase the compatibility with polylactic acid. The esterification was performed with the use of acetic acid anhydride and pyridine. To evaluate and determine the esterification, Fourier transform Infrared Spectroscopy was used. By blending the modified lignin with polylactic acid the intention was to improve the thermomechanical properties and the interfacial linkage between the components. The effects of lignin acetylation on the tensile properties, impact strength, and thermal stability and moisture repellence behaviour were investigated. According to the experimental results the modification of lignin, increased the impact strength for all the blends containing acetylated lignin compares to pristine lignin. The largest increase observed was for blends containing 20 wt% acetylated lignin and polylactic acid, which resulted in a 74% improvement compared with the blend composed of pristine lignin and polylactic acid. Similarly, the thermal stability was improved significantly with acetylation of the lignin. Moreover, the moisture repellence behaviour was also increased. The reason for the improved properties can be explained by the better interfacial compatibility between lignin and polylactic acid matrix. An increased thermal stability and a moisture repellent behaviour of the blends containing chemically modified lignin could be observed when compared with neat polylactic acid which makes the acetylation treatment of lignin a possible approach for the future of biocomposite production.

Udo Krüger Charts TechnoTeam's Journey

Udo Krüger Charts TechnoTeam's Journey

Dynamic and Ordinary Capabilities in Industry Transformation: The Case of the Electric Vehicle Industry

In 2018, in the Dialogue, Debate, and Discussion section of MOR 14.3, an interesting series of articles was published in the ‘Forum on Tesla and the Global Automotive Industry’, where researchers discussed the future dynamics of the global automotive sector. In their work, Perkins and Murmann (2018) contended that, based on Tesla's success, a well-funded company could develop a new electric vehicle (EV) from scratch and move it into production within three to five years if it would invest one to two billion USD in design, development, and manufacturing. Expressing a contrasting view, MacDuffie (2018) questioned this possibility, arguing that EV product architecture is unlikely to become substantially more modular and any new entrant would therefore have to develop the ordinary capabilities that current automotive original equipment manufacturers possess, and there is no guarantee that a firm can develop such capabilities. Teece (2018) joined the debate by proposing a capability-based framework within which to analyze four paradigm shifts that have marked progress in the global automotive industry: EVs, autonomous vehicles, connected cars, and personal mobility services. He argues that these paradigm shifts have created opportunities for new entrants while posing challenges to incumbent firms. To navigate through the uncertainty associated with these paradigm shifts, incumbent firms need to enhance and refine their dynamic capabilities and leverage their integration skills. Jiang and Lu (2018) based their contribution to this debate on the development of the Chinese EV market. In MOR 15.1, published in 2019, Teece (2019) further elaborated his framework to facilitate analysis of the prospects for Chinese firms seeking a stronger foothold in the global automobile market. All these articles have been well received by MOR readers and were ranked among the top 20 articles in full-text view times between June 2021 and June 2022.

CASCADE: An Asset-driven Approach to Build Security Assurance Cases for Automotive Systems

Security Assurance Cases (SAC) are structured arguments and evidence bodies used to reason about the security of a certain system. SACs are gaining focus in the automotive industry, as the needs for security assurance are growing in this domain. However, the state-of-the-arts lack a mature approach able to suit the needs of the automotive industry. In this article, we present CASCADE, an asset-driven approach for creating SAC, which is inspired by the upcoming security standard ISO/SAE-21434 as well as the internal needs of automotive Original Equipment Manufacturers (OEMs). CASCADE also differentiates itself from the state-of-the-art by incorporating a way to reason about the quality of the constructed security assurance case. We created the approach by conducting an iterative design science research study. We illustrate the results using the example case of the road vehicle’s headlamp provided in the ISO standard. We also illustrate how our approach aligns well with the structure and content of the ISO/SAE-21434 standard, hence demonstrating the practical applicability of CASCADE in an industrial context.

Development and application of magnesium alloy parts for automotive OEMs: A review

China is currently vigorously implementing the “energy conservation and emission reduction” and “dual carbon” strategies. As the most resource-advantaged light metal material in China, Magnesium (Mg) alloy is progressively expanding its application in automobile, rail transportation, aerospace, medical, and electronic products. Chongqing University, Shanghai Jiaotong University, and Australian National University have conducted extensive research on the preparation, properties, and processes of Mg alloys. In the past 20 years, the proportion of Mg alloy in the automotive industry has gradually expanded, whereas currently the design and development of Mg alloy parts for automobiles has rarely been reported. Thus, the application models and typical parts cases of Mg alloy are summarized mainly from the four systems of the whole vehicle (body system, chassis system, powertrain system, interior, and exterior system). Subsequently, two actual original equipment manufacturers (OEM) cases are used to introduce the development logic of reliable die-cast Mg alloy, including forward design, formability analysis, process design analysis, structural redesign, manufacturing, and testing, aiming to share the methods, processes, and focus of attention of automotive OEMs for developing Mg alloy parts to enhance the confidence and motivation of applying Mg alloy in automotive field. Eventually, the multiple challenges faced by Mg alloy materials are sorted out and how to face these challenges are discussed. National policies and regulations, environmental protection and energy saving, and consumer demand will continue to promote the application of Mg.

Highlights from the Detroit SID Vehicle Display Symposium and Exposition

Highlights from the Detroit SID Vehicle Display Symposium and Exposition

Part Change Management: A Case Study on Automotive Engineering and Production; Domestic and International Perspectives

AbstractDue to interest in aspects such as process, strategies, and tools of engineering changes expressed in a literature review, a case study was done on a major automotive original equipment manufacturer (OEM) to assess the perceived quality of its part engineering change management process and supporting system through its employees’ eyes. A combination of 12 interviews lasting 12 h and 46 written surveys was used to capture the views of participants from all major functions found at the research and development (R&D) headquarters of the OEM: Purchasing, Production, Development, and one group consisting of all other functions (“Other”). Statistical analysis was performed to identify statistically significant differences between employee perceptions of an engineering change management system among different departments, amount of use, and years of use. It was found that statistically significant differences exist in terms of understanding the usability of the system between different departments and also between different years of experience.

Multi-criteria and real-time control of continuous battery cell production steps using deep learning

Electric vehicles driven by batteries are a key part of a sustainable mobility sector. Unfortunately, battery cell production is still associated with various negative environmental impacts, the use of critical raw materials and high manufacturing costs. The rising battery demand forces automotive original equipment manufacturers to drastically increase their capabilities over the next decades while fulfilling economical and ecological requirements. Continuous production technologies bear the potential to meet future battery cell demands by enabling higher throughputs compared to established batch processes. The control and optimization of continuous battery cell production steps with respect to product quality, manufacturing costs and environmental impacts is challenging due to high parameter spaces as well as temporal dependencies of production processes. Therefore, this study develops a controller that performs real-time optimization by proposing set parameters leading to desired quality, minimal costs and impacts of manufacturing activity. The controller is implemented using a deep learning model incorporating sequential information of the production process. A continuous mixing process with data acquired from a battery cell pilot line is used to validate the outlined controller. As result, the implementation for this use case achieves a relative error of 7.63% across all controllable parameters.

The “Semiconductor Crisis” as a Result of the COVID-19 Pandemic and Impacts on the Automotive Industry and Its Supply Chains

In the first half of 2020, the coronavirus pandemic led to a drastic slump in the automotive industry, which was replaced by a surprisingly rapid growth in demand in the fall of 2020, and consequently led to the current shortages in microelectronic products. The prospect of an equally rapid economic recovery in the automotive industry is still threatened by supply bottlenecks for raw materials and key components, foremost for semiconductors. The so-called ‘semiconductor crises’ show exemplarily the overlapping of short-term supply chain disruptions with long-term structural features of the semiconductor industry. The combination of both is preventing that the supply situation in the automotive industry will improve quickly. First in this paper, the reasons for and respective effects of the crisis on the automotive industry are investigated by a quantitative market analysis. Second, specific strategic measures and options of automotive Original Equipment Manufacturers (OEM) and suppliers in Germany to cope with the situation and increase resilience in future supply chains are described by the means of qualitative expert interviews. By that, the paper helps in understanding the actual situation in the automotive industry, on the one hand, and contributes to the field of strategic supply chain and risk management with a focus on practical implications on the other hand. The results aim to support political stakeholders as well as small and medium sized enterprises to prepare themselves for future developments in the automotive market and changes in manufacturer–supplier relationships due to the transformation to new powertrain technologies and digitization.

Untangling the complexity generating material delivery “schedule instability”: insights from automotive OEMs

PurposeChanges frequently made to material delivery schedules (MDSs) accumulate upstream in the supply chain (SC), causing a bullwhip effect. This article seeks to elucidate how dynamic complexity generates MDS instability at OEMs in the automotive industry.Design/methodology/approachAn exploratory multiple-case study methodology involved in-depth semistructured interviews with informants at three automotive original equipment manufacturers (OEMs).FindingsDynamic complexity destabilizes MDSs primarily via internal horizontal interactions between product and process complexities and demand and SC complexities. A network of complexity interactions causes and moderates such instability through complexity absorption and generation and complexity importation and exportation.Research limitations/implicationsThe multiple-case study contributes to empirical knowledge about the dynamics of MDS instability. Deductive research to validate the identified relationships remains for Future research.Practical implicationsIn revealing antecedents of complexity’s effect on MDS instability, the findings imply the need to develop strategies, programs, and policies dedicated to improving capacity scalability, supplier flexibility, and the flexibility of material order fulfillment.Originality/valueBuilding on complexity literature, the authors operationalize complexity transfer and develop a framework for analyzing dynamic complexity in SCs, focusing on complexity interactions. The identification and categorization of interactions provide a granular view of the dynamic complexity that generates MDS instability. The identified and proposed importance of readiness of the SC to absorb complexity challenges the literature focus on external factors for explaining complexity outcomes. The results can be used to operationalize such dynamic interactions by introducing new variables and networks of relationships. Moreover, the work showcases how a complexity perspective could be used to discern the root causes of a complex phenomenon driven by non-linear relationships.

In-Service Corrosion Performance of Automotive AA7075 Sheet Alloy

Aluminum alloys offer higher specific strength than advanced high strength steels, making them preferred material choice for automotive light weighting. Among them, AA7075 sheet alloy offers significantly higher strength than 5xxx and 6xxx alloys and has been developed to provide high in-service strength for automotive structural applications. However, there is a need to understand the stress corrosion cracking (SCC) susceptibility of AA7075 under “in-service” conditions for the automotive market. Currently there are no commonly accepted corrosion testing specifications or evaluation criteria for AA7075 sheets among material suppliers and automotive original equipment manufacturers. Furthermore, there is a lack of in-service data to validate the accelerated laboratory exposure testing. In this study, in-service SCC performance of the AA7075 test coupons was evaluated by road exposure for a two-year period under harsh Canadian winters. The SCC specimens were loaded using four-point bending frames, and were investigated in various tempers and surface conditions (bare vs. e-coated). Overall, the road exposure results were consistent with the lab accelerated testing such as slow strain rate tensile testing in specific environments. Coating provided sufficient corrosion protection such that the stressed coupons survived after 2-year road exposure without reduction in strength. For bare metals, overaged T73 and in-service (T6+paint bake) tempers outperformed T6 temper. Localized corrosion of AA7075 sheets with various tempers was also studied using potentiodynamic polarization technique.Acknowledgements: Dr. Danick Gallant, Aluminum Technology Center at National Research Council Canada; Kennesaw lab support in Novelis Global Research and Technology Center.