In the context of the 4th Industrial Revolution, the rise of electric vehicles and personal mobility is evident. However, regulatory authorities seem to overlook vast opportunities, placing two-wheelers in a deregulated category and focusing mainly on regulations. This paper addresses a notable trend in e-mobility, emphasizing aspects often overlooked in traditional motorcycle regulatory research. Using the industrial life cycle theory as a basis, we identify challenges that have affected traditional combustion-engine two-wheelers and offer insights to navigate similar obstacles in the future. Consequently, the domestic two-wheeler market experienced a brief surge but lacked sustainable growth prospects. However, considering Japan's success in establishing global standards and design dominance in the two-wheeler industry despite its late entry, there appears to be potential for a rebound in the domestic market.

본 연구는 규모가 작고 역동적인 벤처기업의 성장에 영향을 줄 수 있는 요인들 중에서 기업의 전략적 행동이나 경영자의 경영 역량과 관련된 제품 전략과 CEO(최고경영자) 특성의 효과를 실증적으로 분석하였다. 벤처기업의 표본을 이용하여 FGLS 추정법에 기반한 회귀분석의 결과에 따르면, 제품 전략과 CEO 특성은 기업 성장에 유의한 영향을 주며 추가적인 설명력을 가진다. 일반적으로 제품의 다양화와 마케팅을 강조할수록, 변화혁신을 완화할수록 성장률이 높았다. 그리고 CEO가 경영지배력을 가진 창업자가 아니거나, 현재 사업관련 분야에서 실무경험이 많을수록 성장률이 높았다. 또한, 연구개발 위주 업무경력보다 일반경영 중심의 업무경력을 가진 CEO가 관리하는 기업이 성장률이 더 높았다. 제품 전략과 CEO 특성이 벤처기업의 성장에 미치는 이런 효과들은 조직수명주기 단계에 따라서 달라졌다. 창업기 및 초기 성장기에 있는 기업에서 제품의 마케팅 강조 전략과 CEO의 일반경영 중심 업무경력이 기업 성장에 미치는 긍정적 효과가 상대적으로 강화된 반면, 성숙기 및 정체기에 있는 기업에서 이런 제품 전략과 CEO 특성이 기업 성장에 미치는 효과가 부정적이거나 유의하지 않았다.

It is crucial that the manufacturing companies should consider reducing environmental impacts of the products in the early design phases. Life Cycle Assessment method is one of the most widely used and important tools for assessing the overall environmental impacts of products across the entire lifecycle. Based on the Life Cycle Assessment method, we provide a systematic guideline for eco-product design. The proposed guideline is primarily aimed at product developers or engineers, as a insight to the integration of environmental thinking into the early design phase, and therefore into products. This guideline has several steps: select the target product, describe the product structure, quantify the environmental impacts, identify meaningful underlying environmental impacts, and develop an environmental strategy. The usefulness of proposed guideline is also demonstrated through the application to an actual vacuum cleaner.

This paper introduce information technology of software process assessment. It consists of eight parts such as concepts and introductory guide, a reference model for processes and process capability, performing an assessment, guide to performing assessments, an assessment model and indicator guidance, guide to competency of assessors, guide for use in process improvement, and guide for use in determining supplier process capability.

Parametric life-cycle cost(LCC) models have been integrated with traditional design tools, and used in prior work to demonstrate the rapid solution of holistic, analytical tradeoffs between detailed design variations. During early designs stages there may be competing concepts with dramatic differences. Additionally, detailed information is scarce, and decisions must be models. for a diverse range of concepts, and the lack of detailed information make the integration make the integration of traditional LCC models impractical. This paper explores an approximate method for providing preliminary life-cycle cost. Learning algorithms trained using the known characteristics of existing products be approximated quickly during conceptual design without the overhead of defining new models. Artificial neural networks are trained to generalize on product attributes and life cycle cost date from pre-existing LCC studies. The Product attribute data to quickly obtain and LCC for a new and then an application is provided. In additions, the statistical method, called regression analysis, is suggested to predict the LCC. Tests have shown it is possible to predict the life cycle cost, and the comparison results between a learning LCC model and a regression analysis is also shown