• Title/Summary/Keyword: Function Deployment

Search Result 308, Processing Time 0.038 seconds

Development of Environment Function deployment for Efficient Life Cycle Assessment (효율적인 전과정 평가를 위한 환경 기능 전개 시스템 개발)

  • Yang Kwang-Mo;Kim Sun-Jin;Kang Kyong-Sik
    • Journal of the Korea Safety Management & Science
    • /
    • v.8 no.1
    • /
    • pp.113-130
    • /
    • 2006
  • As environmental damage increase by a highly developed material civilization of today, many companies take a growing immensely interest in the influence of environment for beginning a new paradigm year by year. The previous assessments dose not run the gamut of industry but is confined within a certain facility or an area. Industrial processes and operations can not be accomplished independently but are connected with each others through suppliers and customer, and these ideas are fundamental notions of Life Cycle Assessment(LCA). This paper will introduce Life Cycle Assessment(LCA) in environment which is rising, and would like to build environmental management system using approach of Quality Function Deployment(QFD) and Safety Function Deployment(SFD) belonging to the assessment method.

Improvement and Systematization of Pre-Study Work for Design Value Engineering in Construction Projects by Quality Function Deployment (품질기능전개(QFD) 기법을 적용한 건설프로젝트 설계VE 준비단계 업무 개선 및 체계화)

  • Yang Jin Kook;Kim Soo-Yong
    • Korean Journal of Construction Engineering and Management
    • /
    • v.6 no.4 s.26
    • /
    • pp.122-132
    • /
    • 2005
  • The importance of design value engineering in construction projects recently is rising, which deal about in reduction of cost and improvement of quality. Especially, From half of this year 2005, It is predicted that the value engineering is necessary to be carried out in the projects having project cost more than 10 billion won. In increasing importance of value engineering, we require a way by which we accomplish value engineering systemically and effectively though it is not common in domestic situation. So, this case study shows a way in carrying out value engineering effectively. As the way is to apply QFD(Quality Function Deployment) in pre-study work of design value engineering, this is expected to contribute as function analysis and reflect the requirement of users or owners.

Developing Environmental Quality Deployment for Designing Environmentally Friendly Product

  • Lee, Dong-Won;Kim, Youn-Sung
    • Journal of Korean Society for Quality Management
    • /
    • v.31 no.2
    • /
    • pp.40-50
    • /
    • 2003
  • This study proposes Environmental Quality Deployment (EQD) by combining an instrument for measuring customer satisfaction (ENVIROQUAL) with a standard tool of product design in manufacturing called quality function deployment (QFD). The EQD presents the conceptual map of House of Environmental Quality as a means to implementation to help a company know what customers perceive as important in making environmentally friendly product and provide a framework for the translation of customer satisfaction into identifiable and measurable conformance specifications for environmentally friendly product design.

Machine Learning-based Optimal VNF Deployment Prediction (기계학습 기반 VNF 최적 배치 예측 기술연구)

  • Park, Suhyun;Kim, Hee-Gon;Hong, Jibum;Yoo, Jae-Hyung;Hong, James Won-Ki
    • KNOM Review
    • /
    • v.23 no.1
    • /
    • pp.34-42
    • /
    • 2020
  • Network Function Virtualization (NFV) environment can deal with dynamic changes in traffic status with appropriate deployment and scaling of Virtualized Network Function (VNF). However, determining and applying the optimal VNF deployment is a complicated and difficult task. In particular, it is necessary to predict the situation at a future point because it takes for the process to be applied and the deployment decision to the actual NFV environment. In this paper, we randomly generate service requests in Multiaccess Edge Computing (MEC) topology, then obtain training data for machine learning model from an Integer Linear Programming (ILP) solution. We use the simulation data to train the machine learning model which predicts the optimal VNF deployment in a predefined future point. The prediction model shows the accuracy over 90% compared to the ILP solution in a 5-minute future time point.

System Requirement Analysis of Guided Missile using Quality Function Deployment(QFD) and Analytic Hierarchy Process(AHP) (Quality Function Deployment(QFD)와 Analytic Hierarchy Process(AHP)를 이용한 유도무기의 시스템 요구도 분석)

  • Noh, Kyung-Ho;Hwang, Sung-Hwan;Lee, Ki-Seung;Kang, Dong-Seok;Kim, Ji-Eok
    • Journal of the Korean Society of Systems Engineering
    • /
    • v.5 no.1
    • /
    • pp.67-72
    • /
    • 2009
  • User Requirements are analyzed and quantified by decision making models and system engineering methods to select alternative concepts which satisfy the various requirements. In this study, the design concepts for guided missile are derived using Quality Function Deployment(QFD) and Analytic Hierarchy Process(AHP). The design alternatives that satisfy the user requirements are extracted by QFD and Morphological Matrix, then the best design concept are obtained using AHP and Pugh concept Selection.

  • PDF

An Application of Quality Function Deployment on the Site Selection (입지선정에 있어 품질기능전개 방법론 적용)

  • Oh, Jin-Seok;Lee, Sang-Jin
    • Journal of the military operations research society of Korea
    • /
    • v.36 no.1
    • /
    • pp.65-76
    • /
    • 2010
  • This study is to apply the Quality Function Deployment Methodology to select the Navy Battle Lab location among possible location sites. The Quality Function Deployment is known to be one of the good methodology in reflecting users' need. Thus the Navy Battle Lab location should b e selected on the basis of satisfying the Navy and ADD requirments. The data to be applied in QFD has been collected from the Navy users and ADD researchers. After carrying out QFD procedures, Jinhae was selected as the best site of the Navy Battle Lab.

Intelligent Hospital Concept Definition by Implementing Quality Function Deployment And System Requirement Analysis (QFD(Quality Function Deployment)와 시스템 요구분석 기법을 이용한 지능형 병원 시스템 개념 정립)

  • Lee, Jun Ho;Kim, Dae Hong;Jin, Kyung Hoon;Ham, Jae Bok;Lee, Jae Woo
    • Journal of the Korean Society of Systems Engineering
    • /
    • v.2 no.1
    • /
    • pp.24-30
    • /
    • 2006
  • In this study, the design concepts for Intelligent Hospital are derived using the Quality Function Deployment(QFD) and System Requirement Analysis Method. First, requirements for important elements of Intelligent hospital are defined. Second, similar systems are compared and user requirement are refined. Through this process, operational requirement for Intelligent Hospital are defined by combining user requirements and similar systems. To analyze operational requirement, the QFD of the system engineering approach are implemented. Alternative design specifications are constructed by implementing the QFD results by building the Morphological Matrix. Various concepts that satisfy the system requirement are derived. Finally the best design concept are obtained using the Pugh concept selection matrix.

  • PDF

A Study of Safety Function Deployment for Using QFD (QFD를 활용한 안전기능 전개에 관한 연구)

  • 김건호;김윤성;권상면;이강복;박주식;강경식
    • Journal of the Korea Safety Management & Science
    • /
    • v.6 no.1
    • /
    • pp.25-35
    • /
    • 2004
  • We achieved a Quantitative economic growth through rapid developed in 1970' and 1980'. It was confronted after IMF crisis that we needed to improve the past economic policy and the industrial structure. In line of that, the problem of the industrial structure like huge accidents of 1990' converted the recognition of safety. And to improve the conversion, the research of a safety management was needed. In this paper, we thought 4M1E(Man, Machine, Method, Material, Environment) as the cause of accident using the principal of process, assuming that output is accident. Applying 4M1E to the structure of QFD(Quality Function Deployment), we propose the safety function deployment, which has the flow line as followed; demand safety, safety characteristic, direct cause, 4M1E and safety management.