Re-design of drivers’ car seat using three dimensional reverse engineering

Automobile seat design in current practice requires satisfying the ergonomics guidelines as well as considers the comfort expectation of the population. The main aim is to re-examine the existing car seat designs and to propose a novel seat design for better comfort. The number of cars reviewed for drivers’ seat features and user comfort are based on the analysis using a statistical tool. The statistical tool analysis is defined using data from the survey conducted. The proposed design is obtained using the 3-D Reverse Engineering procedure on the selected car seat models. The result is assessed to show that the modified car seat design is superior in terms of form, shape, seat features, usability and comfort. Through this work, the basic seat needs while driving, for example pain preclusion aspects and comfort weightage are defined. The survey done can expunge the expenditure for test experimentations in the future and the proposed methodology can be useful in establishing new design standards for the seat

EFFECTS OF DRIVER PERSONAL VARIABLES ON PREFERRED VEHICLE INTERIOR COMPONENTS SETTING

This study identified and characterized the relationship between driver personal variables and preferred vehicle interior components setting. A two-phase modeling approach was employed to characterize the temporal, logical process involved in the driver selection of a preferred vehicle interior components setting. The modified Bayesian multivariate adaptive regression splines (BMARS) modeling method was employed to identify nonlinear and interactive relationships. Forty-two male and forty-four female drivers with a wide range of ages, stature, and BMI participated in the data collection. A highly adjustable vehicle mock-up was used to empirically obtain each participant’s preferred vehicle interior components setting. The study results indicated substantial non-anthropometric variability in the driver-selected seat horizontal positions and identified various interpretable nonlinearities and interactions. The study findings improve the understanding of the relationship between driver personal variables and preferred vehicle interior configuration and further inform the vehicle interior package design for driver accommodation

Ergonomic design parameters for Malaysian car driverseating position

A key element in an ergonomically designed driver workspace of a car is the correct identification of seating position and posture accommodation. Current practice by the automotive Original Equipment Manufacturer (OEM) is to utilize the Society of Automotive Engineering (SAE) standard practice and guidelines in the design process. However, it was found that utilizing such guidelines which were developed based on the American population, do not fit well with the anthropometry and stature of the Malaysian population. This research seeks to address this issue by reviewing the existing standard practices of Design Package and Ergonomic for seating position and accommodation used by a Malaysian automotive manufacturer, Perusahaan Otomobil Nasional (PROTON), and to subsequently propose a new design parameters which better fit the Malaysian population. In the first stage, 210 respondents participated in the anthropometry measurement study to determine the range of sizes for the Malaysian population. In addition, 62 respondents were involved for the driver seating position and accommodation study in the vehicle driver workspace buck mock-up survey and measurements. The results have shown that the Malaysian population are generally shorter if compared with the SAE J833 standard specification, especially for the lower body segments. From the accommodation study, it was found that the Malaysian driver preferred to seat forward, which is probably due to the shorter limb dimensions in the thigh length, buttock length, knee length and foot length. In second stage, questionnaire survey and measurement were used to develop a new design parameters and standards for driver seating positioning and accommodation model based on the Malaysian population. Statistical regression analysis was used to assist in this design parameters development. The statistical model developed was validated by comparing the calculated value of Seating Reference Point of X axis (SgRPx) with actual measurement values measured during respondents sitting in the mock-up. The result shows the difference between the calculated and measured values was within 10 %, indicating that the equation is acceptable. The findings of research are expected to enhance and improve the design guidelines / standard reference for the local automotive industry

Public Innovation and Digital Transformation

Public innovation and digitalization are reshaping organizations and society in various ways and within multiple fields, as innovations are essential in transforming our world and addressing global sustainability and development challenges. This book addresses the fascinating relationship of these two contemporary topics and explores the role of digital transformation in promoting public innovation. This edited collection includes examples of innovations that emerge suddenly, practices for processing innovations, and the requirements for transformation from innovation to the ""new normal"". Acknowledging that public innovation refers to the development and realization of new and creative ideas that challenge conventional wisdom and disrupt the established practices within a specific context, expert contributions from international scholars explore and illustrate the various activities that are happening in the world of multiple digitalization opportunities. The content covers public administration, technical and business management, human, social, and future sciences, paying attention to the interaction between public and private sectors to utilize digitalization in order to facilitate public innovation. This timely book will be of interest to researchers, academics and students in the fields of technology and innovation management, as well as knowledge management, public service management and administration

Public Innovation and Digital Transformation

Public innovation and digitalization are reshaping organizations and society in various ways and within multiple fields, as innovations are essential in transforming our world and addressing global sustainability and development challenges. This book addresses the fascinating relationship of these two contemporary topics and explores the role of digital transformation in promoting public innovation. This edited collection includes examples of innovations that emerge suddenly, practices for processing innovations, and the requirements for transformation from innovation to the "new normal". Acknowledging that public innovation refers to the development and realization of new and creative ideas that challenge conventional wisdom and disrupt the established practices within a specific context, expert contributions from international scholars explore and illustrate the various activities that are happening in the world of multiple digitalization opportunities. The content covers public administration, technical and business management, human, social, and future sciences, paying attention to the interaction between public and private sectors to utilize digitalization in order to facilitate public innovation. This timely book will be of interest to researchers, academics and students in the fields of technology and innovation management, as well as knowledge management, public service management and administration.fi=vertaisarvioitu|en=peerReviewed

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Eco‐Holonic 4.0 Circular Business Model to Conceptualize Sustainable Value Chain Towards Digital Transition

The purpose of this paper is to conceptualize a circular business model based on an Eco-Holonic Architecture, through the integration of circular economy and holonic principles. A conceptual model is developed to manage the complexity of integrating circular economy principles, digital transformation, and tools and frameworks for sustainability into business models. The proposed architecture is multilevel and multiscale in order to achieve the instantiation of the sustainable value chain in any territory. The architecture promotes the incorporation of circular economy and holonic principles into new circular business models. This integrated perspective of business model can support the design and upgrade of the manufacturing companies in their respective industrial sectors. The conceptual model proposed is based on activity theory that considers the interactions between technical and social systems and allows the mitigation of the metabolic rift that exists between natural and social metabolism. This study contributes to the existing literature on circular economy, circular business models and activity theory by considering holonic paradigm concerns, which have not been explored yet. This research also offers a unique holonic architecture of circular business model by considering different levels, relationships, dynamism and contextualization (territory) aspects

ANALYSIS OF BEST PRACTICE OF ARTIFICIAL INTELLIGENCE IMPLEMENTATION IN DIGITAL MARKETING ACTIVITIES

Rapid development of artificial intelligence is transforming the world we live in. Advancement in technology and consumer\u27s needs creates the urge for rapid adaptation by companies operating in a volatile and uncertain marketing environment in order to adequately shape their marketing decisions and achieve the best results on the market. The availability of information to consumers is greater than ever before causing an increase in the needs and demands they expect when buying and consuming a product or a service which results in higher efforts of personalization and individualization while creating marketing messages. This is precisely what innovative and disruptive technologies, such as intelligent self-learning systems based on artificial intelligence, allow companies to gain a better insight into the consumer\u27s needs and create marketing content that will result in higher engagement and conversion rates. This study investigates and analyses set of examples of best practices of artificial intelligence implementation and the benefits of its usage in marketing activities and campaigns in automotive, retail and hospitality industry through predicting, testing and optimizing. Study shows the way artificial intelligence systems make an exceptional contribution to the optimization of marketing activities and overall marketing performance efficiency. The paper ends with the conclusions and recommendations how to implement some of the presented AI solutions into the Croatian business practice