New Autonomous Vehicle Technologies Effect on Automotive Concept Design Stages

In the last two decades, lean product development and design processes, which have rapidly penetrated the automotive industry companies with all their functions and spread globally, have come to life with measurements, benchmarks and flow maps on value creation or value addition in new product project stages. In the literature review, two customer-oriented approaches, namely lean product development and user experience design, have been widely adopted within the discipline of new product design. However, the lack of comparative efficiency measurements or studies addressing their integration in scientific publications constitutes the primary motivation of this research. Furthermore, the selection of the research scope and boundaries is based on convenience sampling, enabled by direct qualitative research with companies managerial or design positions in the Turkish automotive supplier industry. The main objective of this study is to conduct a comparative analysis aimed at integrating user experience activities with lean product development process flows, and to reveal metric values that enhance efficiency through overall system optimization. On the other hand, the most important recommendation of the study is that the basic process will benefit positively by including user experience activities, which are the most important application of customer-oriented design approaches, into the highly efficient lean product development flow. Therefore, the study conducted with the mixed research method was completed under the preference of quantitative face-to-face interview method in the in-depth questioning of the qualitative numerical data obtained by the questionnaire method. User experience activities, which are newer and less widespread than lean product development processes, have also revealed the frequency of project-based, customer-oriented design activities, along with the demographic organizational structure of the participating companies in the automotive industry, such as the age of establishment, number of employees, number of designers, etc.

The Way from Lean Product Development (LPD) to Smart Product Development (SPD)

Currently, an increasing number of drivers are facing challenges associated with the decline in physical function as they age. The widespread adoption of autonomous vehicle technology holds the promise of addressing travel difficulties for older adults. However, older adults encounter difficulties in using autonomous vehicle technology due to their declining physiological conditions and cognitive abilities. Therefore, this paper aims to understand the readiness of older adults for autonomous vehicle technology. The paper applies Human Readiness Level (HRL) to discuss the performance of older adults using three specific autonomous vehicle features—Adaptive Cruise Control (ACC), Blind Spot Warning (BSW), and Lane Departure Warning (LDW). Employing a literature review approach, this paper conducts database screening on the research topics evaluating the readiness and driving performance of older adults for autonomous vehicle technology with the application of HRL and related theories. In total, 15 articles were selected as core literature. We propose recommendations and measures on how to enhance the readiness of older adults for autonomous vehicle technology from both technological and individual perspectives to enhance the readiness of older adults for autonomous vehicle technology and ensure safer travel for them.

Lean is not minimizing cost, cycle time, or waste. Lean is maximizing value. In product development (PD)—a process where the systems engineering (SE) of products plays a significant role—sometimes getting lean requires doing more, not less. Providing a preferred combination of technical performance, affordability, and lead time requires a flexible and lean PD process. Value is affected not only by the presence of necessary (value‐adding) activities in the PD process but also by the way those activities work together (as a system) to ensure that they use and produce right information. Lean PD requires the right information in the right place at the right time.The kernel of the idea is based on systems thinking. The value of a system is more than the value of its individual components. Similarly, the value of a process is more than the value of its individual activities. How well the components or activities work together (or fail to do so) makes the difference in value. If all the activities add value, how can we ensure that they work together in such a way that the overall process maximizes its potential value? Lean PD requires the systems engineering of processes.

The primary data about the role of automotive manufacturing industry in emitting dichloromethane (DCM) to the environment in Iran are provided by a case study of SAIPA Automotive Manufacturing Company in 2012. The average emission rate and emission factor of DCM in the stack exhaust air streams of the solvent-based automotive painting plant were 6.8 kg·d−1 and 19.5 g per car, respectively. The spray booths had the highest portion (about 85%) of DCM emission in the stack exhaust air streams. The average concentration of DCM (± standard deviation) in the generated wastewater of the factory was 64 ± 12 μg·L−1, but in the effluent of the factory wastewater treatment plant, it was reduced to a nondetectable level. DCM was also observed in three groundwater wells out of five monitored water resources. To control DCM emission and prevent pollution, the replacement of solvent-based paints with water-based paints is highly recommended.

Lean practices have been applied by a diverse number of companies in manufacturing during the last decade. Nevertheless, the satisfactory and ongoing trajectory towards an effective application of Lean principles in the New Product Development Process (NPD) is currently under study as it is rather new in industry and the companies still face different challenges to successfully implement them. As a consequence, a novel Maturity Model and assessment tool called SMART has been developed as part of a Lean Transformation Toolkit (Lean T2) within the LeanPPD project funded by the European Commission (NMP-2008-214090). This Maturity Model and assessment tool has three main goals: 1) to enable companies to understand which are the main lean practices that are required to achieve a lean product development process and the four perspectives into which these practices are grouped 2) assess which are the companies' current and desired maturity levels in order to develop an action plan for continuous improvement and 3) obtain in a simple and automatic way the results of the assessment highlighting the main opportunity areas. This tool is also meant to follow the company during the Lean Product and Process roadmap, providing the firm the maturity level in each milestone. Therefore, the objective of this paper is twofold, 1) to provide first insights of the successful validation of the SMART Assessment tool in five companies in Spain and 2) to summarize the results of an exploratory study which evidences difficulties and best practices in regards to the implementation of lean practices in product development.

Automatic sensing devices and computer systems have been widely adopted by the automotive manufacturing industry, which are capable to record machine status and process parameters nonstop. While a manufacturing process always has natural variations, it is crucial to detect significant changes to the process for quality control, as such changes may be the early signs of machine faults. This motivates our study on change-point detection methods for automotive manufacturing. We aim at developing a systematic approach for detecting process changes retrospectively in complex, nonstationary data. The proposed approach consists of nonparametric change-point detection, alarm generation based on change-point estimations, and performance evaluation against historical maintenance records. For change-point detection, three nonparametric methods are suggested—least absolute shrinkage and selection operator (LASSO), thresholded LASSO, and wild binary segmentation (WBS). Multiple decision rules are proposed to determine how to generate alarms from change-point estimations. Numerical studies are conducted to demonstrate the performance of the proposed systematic approach. The different change-point detection methods and different decision rules are evaluated and compared, with scenarios for choosing one set of change-point detection method and decision rule over another combination identified. It is shown that LASSO and thresholded-LASSO outperform WBS when the shift size is small, but WBS produces a smaller false alarm rate and handles the clustering of changes better than LASSO or thresholded LASSO. Data from an automotive manufacturing plant are used in the case study to demonstrate the proposed approach. Guidelines for implementation are also provided.

The Malaysian automotive manufacturing industry is growing exponentially, but it faces shortage of the right skilled automotive manufacturing professionals to propel the industry to an unprecedented height. This research hypothesised that there are several challenges faced by new engineers in executing daily task due to knowledge gap. New engineers are not guided and trained sufficiently to undertake daily task to be more productive. Therefore, this research aims to identify the knowledge gap and challenges faced by new engineers in the automotive company in Malaysia. The scope of this research is confined to the theories of Human Capital, which relate to the capabilities and skills of an employee to improve job performance. A qualitative case study method has been employed for the analysis and comparison of data from public and private universities and automotive manufacturing industry in Malaysia. The identified target groups were human resource manager, manufacturing managers, new engineers and lecturers. This study concludes that training in industry has failed at certain aspects to produce new employees that meet current competency requirements, lead to the deterioration of employee work performance. In conclusion, this research validates the existence of a knowledge gap and acknowledges the need to overcome challenges faced by new engineers during their transition from academia to industry.
 
 Keywords: Industrial Experience, Knowledge Gap, Challenges, New Engineers, Skills, Training.

Performance and efficiency in R&D processes is a critical point for industry. Operational excellence in design and development is required to overcome the business challenges that companies face. It is the case in semiconductor industry where products have to be developed rapidly whilst maintaining a high level of quality. Study of design practices in aerospace have illustrated that only about 10% of design activity is spent on creating value [1]. If this number appears alarming for a great number of managers, many engineers participating in design activity agree that practices could be improved to address issues in efficiency and performance. Lean product development is viewed as a global approach that offers a framework to address design performance [2]–[4]. This approach has been chosen by a large microelectronics company to transform the practice of its process and product engineer on the scale of its global footprint. This paper will present how a collaboration between academics and Industry initiated in the context of local ecosystem partnership led to the design of a training program. The approach inspired from design thinking principles was developed by an interdisciplinary team of lean experts from the company, Researchers in R&D efficiency, Specialists in Pedagogy, Specialists in serious game design from academia, Project management experts and Engineers from the company. Intense collaboration within an interdisciplinary team, rapid prototyping including engineers from the company, observers from university, quick learning loop and feedback, allowed to develop the training program. This collaboration favoured discussion in terms of expectations of company and setting up the foundation of the training program including constraints of deployment up to 2000+ targeted people worldwide. The program include 2 days of face to face using experiential learning [5] and one accompanying day later with implementation progress monitoring and return and sharing on experience as a whole. Two serious games were designed, a case study and problem-based learning session were developed to introduce the main concepts chosen as the heart of the program. The last afternoon was used to workshop on participants own problems. A community of practice with a collaborative platform and in vivo sharing sessions supports the transformation. The paper discusses the training design process, hypothesis and objectives. All stakeholders learnt from this design thinking approach, leading to innovative training co-creation and demonstrating the richness of interdisciplinary teamwork and relevance of a user-centered approach for lifelong learning training design References [1] J. Oehmen and E. Rebentisch, “Waste in Lean Product Development,” LAI Pap. Ser., no. July, pp. 1–35, 2010. [2] M. S. Khan et al., “Towards lean product and process development,” Int. J. Comput. Integr. Manuf., vol. 26, no. 12, pp. 1105–1116, Dec. 2013. [3] J. M. Morgan and J. K. Liker, The Toyota Product Developement System: Integrating People Process and Technology. New York: Productivity press, 2006. [4] D. G. Reinertsen, The principles of Product Development Flow. Second Generation Lean Product Development. Celeritas Publishing, 2009. [5] D. A. Kolb, Experiential Learning: Experience as the Source of Learning and Development, 2nd editio. pearson education, 2014.

Nowadays, scientific developments in the vehicle and infrastructure automation sectors are developing steadily to benefit end users in ways like reducing traffic congestion, enhancing safety, facilitating stress-free travel, reducing fuel costs, and enabling smart parking. Autonomous vehicles sometimes known as self-driving cars, can entirely replace conventional automobiles to accomplish safe and reliable intelligent transportation systems (ITS). Autonomous vehicles have improved sensing capabilities and can drive on roads that would be difficult for humans. Software, technology, and people, together with their interactions, are the building blocks of Autonomous vehicles. Even though Autonomous vehicles have been studied extensively, many issues have yet to be fixed. One of the biggest obstacles to developing Autonomous vehicles is improving their ability to communicate with other vehicles and pedestrians. Second, it's unclear whether society will benefit or suffer from people's tendency to place an excessive amount of faith in Autonomous vehicles and assume that they will never malfunction. Capabilities analysis is essential to making sure autonomous vehicles are safe and widely accepted. The proposed review outlines the key benefits, drawbacks, and difficulties of autonomous vehicle technology and provides a detailed analysis of the different autonomous vehicle mechanisms. Further, the developed review intends to formulate future prospects by examining the advantages and disadvantages of autonomous driving technology to encourage the development of innovative autonomous vehicle technologies to prevent human error and increase road safety and security.

Industry 4.0 (I4) technologies are gaining increased importance in the manufacturing industry, as they can provide several benefits such as an increase in efficiency, lower costs, higher revenues, etc. This article empirically examines the critical success factors (CSF) for implementing I4 technologies in Indian automotive manufacturing industry. In this regard, CSF and performance outcomes of I4 technologies are identified from published literature. The relationships between CSF and performance outcomes are then examined by regression analysis. The results indicate that “Data governance” is the most critical factor, as it affects all the four performance outcomes (operational, product, economic, and responsiveness). Similarly, “Legal aspects” affects three out of the four performance outcomes (operational, product, and economic performance), while “Collaboration and teamwork” affects only operational performance and responsiveness. The study provides an understanding of factors which are critical in achieving performance outcomes about I4 technologies in the automotive manufacturing industry. The findings can also help automotive manufacturing firms toward an informed decision making in terms of various strategies required to adopt I4 technologies successfully.

The prevalence and development of autonomous vehicle technology can reshape the status quo in vehicle manufacture, transportation, and logistics industries, leading to safer daily travel activities, higher power savings, and lower urban pollution. Traditional autonomous vehicle technology focuses on advancing vehicle-related technology, aiming to equip vehicles with high computational capability at a cost. One key initiative to improve traffic information collection and processing is the development and enhancement of smart road systems. This is often done by deploying a large number of roadside units (RSUs) thanks to the internet of things and cloud computing technology. However, few studies have explored RSU location planning problems under uncertainty. Existing RSU location models in dynamic edge computing scheduling mostly ignore uncertainties associated with task transmission and model parameters. We develop a two-stage robust satisficing RSU location model featuring uncertain demands for computing tasks and random delays in task processing and transmission. The optimization model minimizes the riskiness of violating various capacity budgets and task latency thresholds. We reformulate the robust RSU location model as an equivalent mixed-integer convex optimization model that can be solved by a cutting plane algorithm. We propose two acceleration methods to enhance the problem-solving process. The numerical experiments illustrate that our robust model is computationally tractable and outperforms the deterministic model and queuing model in various metrics. The resulting RSU location pattern differs significantly from that of the deterministic and queueing-based models. Specifically, the robust model can reduce computing task transmission quantity, average task completion time, and the magnitude and probability of capacity budget violation both in our grid network and random-generated networks.

After the First World War, during the transitional period the Republic of Turkey from the Ottoman Empire, this qualitative research that sheds light on changes and variations were the focus of the industry, for the conservation and use of located original value in the historical development, it has been compiled with archival-tracer information. The Ottoman Empire, after the industrial revolution, these historical studies that discussed the effects of the touch industry has ceded to the Republic of Turkey, in the automotive assembly industry realized the product development and product design activities, with examples of significant diffraction in all, were transferred with qualitative data. The main objectives of the study are how and why the automotive industry, which was established before the world wars and restructured afterwards, came to life in the global context and the regional developments it showed. Therefore, in the coming days, the study was carried out in a narrow field in order to understand the global integrity where geographical regions converge, and the structure in which full integration has been completed in companies and organizations in the automotive industry. In this context, the geographical continent transition taking place among Turkey’s automotive industry, within the limits of qualitative studies, creating a historical perspective, the region, released today stems from the importance of global integration centers. In this context, Turkey is located between geographic continent transitions to create operating limits of the automotive industry, the global integration of the indicator is due primarily to the fact that the reveal. The study evaluated two different development structures in the automotive industry: mass manufacturing and vehicle design, product development. Therefore, examining the development of the automotive assembly industry in the light of original product design studies, which constitute the basic requirement of mass manufacturing, strengthened the cause and effect flow. Under the new technology-oriented, original product design, the enhancement or sustainability of global competitiveness, regional mass manufacturing industry, specific applications for the automotive sector were included in the findings and main results of the research. In addition, the use of common platforms and parts between global companies in the automotive assembly industry or a common design approach in cost-oriented mass manufacturing has been determined within the scope of the research boundaries. The global competition that shapes the basic development of the automotive industry, the brand and model memory of regional markets, short product life cycles, product diversity and increasing consumer demands, predictable implementation of similar basic strategies have shaped the background of the study. Research limits built-in, well situated Turkey’s automotive assembly industry in the 1960s to set up and under liberal policies in the mid-1980s to be included with mass manufacturing capacity with a special agreement with the European customs union, today, global integration, to be completed by Asian companies cannot stay away from the expected in this sector in the working axis is revealed.

According to Marimuthu and Ismail (2009), “Human capital refers to processes that relate to training, education and other professional initiatives in order to increase the levels of knowledge, skills, abilities, values, and social assets”. In today’s global economy, a nation’s success is depending fundamentally on the knowledge, skills and competencies of its people. Education systems are designed and implemented to equip students with knowledge and skills that will help them succeed in their chosen profession in life. Hence, this study aims to identify strategies to close the knowledge gap of new engineers in the automotive industry in the country. The scope of this research is confined to the theories of Human Capital, which relate to the capabilities and skills of an employee in improving job performance. A qualitative case study method was employed for the analysis and comparison of data from public and private universities and the automotive manufacturing industry in Malaysia. The informants involved in this study included one human resource manager, two manufacturing supervisors, six new engineers and four lecturers. Based on the findings, this study has recommended various strategies for different groups of stakeholders to close the knowledge gap of new engineers in the automotive industry.

Significant global shifts have inundated the production of automotive components over the past few years. To meet global "Sustainable Development Goals," the industry is moving from internal combustion engines to electric vehicles. The fourth industrial revolution has changed the current phase of global manufacturing's competitiveness and can potentially increase competitiveness in the future. Because of this shift, manufacturers of automotive components now need different skill sets. Consequently, a better alignment of graduate attributes with evolving industry demands is necessary to create sustainable job opportunities for industrial and mechanical engineering graduates expected to fill critical roles in the automotive manufacturing industry. This paper presents the findings of an investigation into the engineering programs of higher education institutions within South Africa and how the engineering graduate attributes in the mechanical and industrial programs align with the automotive industry demands. The advent of the Fourth Industrial Revolution has caused significant upheaval in employment stability. Apprentices are being phased out of the workforce and replaced by smart machines. The automobile industry has seen revolutionary changes in its production methods in the last century. Conversely, higher institutions have not implemented major curriculum changes and still practice traditional teaching processes. The technology used in the average classroom has barely changed in the past three to four decades. The study's overarching goal was to create a model that colleges and universities can use to close the skills gaps through curriculum updates that better prepare students for success in the workplace. The automotive sector and academic institutions collaborated on an action research project to develop this framework. A framework emerged from the study aimed at assessing how well higher education institutions meet the needs of industries, and it shows the required measures to remain competitive.