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System dynamics review 8건

  1. [해외논문]   Issue Information   SSCI


    System dynamics review v.33 no.3/4 ,pp. 181 - 182 , 2017 , 0883-7066 ,

    초록

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  2. [해외논문]   Newton's laws as an interpretive framework in system dynamics   SSCI

    Hayward, John (Department of Computing and Mathematics, University of South Wales, Pontypridd, CF37 1DL, U.K.) , Roach, Paul A. (Department of Computing and Mathematics, University of South Wales, Pontypridd, CF37 1DL, U.K.)
    System dynamics review v.33 no.3/4 ,pp. 183 - 218 , 2017 , 0883-7066 ,

    초록

    Abstract This paper proposes an interpretative framework for system dynamics models using concepts from Newtonian mechanics. By considering the second derivative form of a model, it is shown that Newton's three laws of motion have their equivalent in system dynamics, with forces between stocks being determined using the loop impact method. The concepts of mass, inertia, momentum and friction are explored as to their usefulness in understanding model behaviour. The Newtonian framework is applied to two standard system dynamics models—inventory–workforce and economic long‐wave—where their behaviour is analyzed using force dominance on the stocks. Results show improved intuitive understanding of system behaviour compared with existing dominance methods, particularly for models with exogenous effects, oscillations and many loops. The framework is commended for further exploration. Copyright ⓒ 2018 System Dynamics Society

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  3. [해외논문]   Dell's SupportAssist customer adoption model: enhancing the next generation of data‐intensive support services   SSCI

    Ghaffarzadegan, Navid (Department of Industrial and Systems Engineering, Virginia Tech, 430 Northern Virginia Center, Falls Church, VA, 22043, U.S.A.) , Rad, Armin A. (Department of Industrial and Systems Engineering, Virginia Tech, 231 Durham, Blacksburg, VA, 24060, U.S.A.) , Xu, Ran (Department of Industrial and Systems Engineering, Virginia Tech, 430 Northern Virginia Center, Falls Church, VA, 22043, U.S.A.) , Middlebrooks, Sam E. (Dell EMC—Global Support and Deployment Product Group, One Dell Way, MS RR7‐01, Round Rock, TX, 78682, U.S.A.) , Mostafavi, Sarah (Department of Industrial and Systems Engineering, Virginia Tech, 430 Northern Virginia Center, Falls Church, VA, 22043, U.S.A.) , Shepherd, Michael (Dell EMC—Global Support and Deployment Product Group, One Dell Way, MS RR7‐01, Round Rock, TX, 78682, U.S.A.) , Chambers, Landon (Dell EMC—Glob) , Boyum, Todd
    System dynamics review v.33 no.3/4 ,pp. 219 - 253 , 2017 , 0883-7066 ,

    초록

    Abstract We developed a decision support system to model, analyze, and improve market adoption of Dell's SupportAssist program. SupportAssist is a proactive and preventive support service capability that can monitor system operations data from all connected Dell devices around the world and predict impending failures in those devices. Performance of such data‐intensive services is highly interconnected with market adoption: service performance depends on the richness of the customer database, which is influenced by customer adoption that in turn depends on customer satisfaction and service performance—a reinforcing feedback loop. We developed the SupportAssist adoption model (SAAM). SAAM utilizes various data sources and modeling techniques, particularly system dynamics, to analyze market response under different strategies. Dell anticipates improving market adoption of SupportAssist and revenue from support services, as results of using this analytical tool. Copyright ⓒ 2018 The Authors System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

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  4. [해외논문]   Supply constraints and waitlists in new product diffusion   SSCI

    Keith, David R. (Sloan School of Management, Massachusetts Institute of Technology, Room E62‐432, 100 Main St, Cambridge, MA, 02142, U.S.A.) , Sterman, John D. (Sloan School of Management, Massachusetts Institute of Technology, Room E62‐432, 100 Main St, Cambridge, MA, 02142, U.S.A.) , Struben, Jeroen (emlyon Business School, Strategy and Organisation Department, Lyon, France)
    System dynamics review v.33 no.3/4 ,pp. 254 - 279 , 2017 , 0883-7066 ,

    초록

    Abstract Constraints on production capacity adjustment present a strategic and operational problem for managers launching products when demand is uncertain. Stock‐outs can be costly if demand exceeds available product supply, as sales are deferred or lost when prospective customers are waitlisted. Recent research on diffusion under supply constraints has analyzed launch strategies to minimize the chance of stock‐outs. Others suggest that waitlisted buyers generate social exposure that can boost customer demand and shape the diffusion process. We develop a generalized model of new product diffusion under supply constraints that explicitly accounts for endogenous customer waitlisting and waitlist‐generated word‐of‐mouth. We estimate the model for a prominent example of waitlisting, the launch of the Toyota Prius hybrid‐electric vehicle in the U.S.A., finding evidence of positive word‐of‐mouth from waitlisted buyers. Inclusion of endogenous supply constraints and waitlisting also alters the estimated contribution of marketing and adopter word‐of‐mouth. Copyright ⓒ 2018 System Dynamics Society

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  5. [해외논문]   Impact of drug supply chain on the dynamics of infectious diseases   SSCI

    Paul, Siddhartha (Department of Industrial Engineering and Operations Research, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 Maharashtra, India) , Venkateswaran, Jayendran (Department of Industrial Engineering and Operations Research, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 Maharashtra, India)
    System dynamics review v.33 no.3/4 ,pp. 280 - 310 , 2017 , 0883-7066 ,

    초록

    Abstract The effect of drug shortages on estimating the infectivity of antiviral‐treatable disease epidemics is evaluated using an illustrative dataset. Simulation‐based analysis shows that a given outbreak can be caused by either (i) a high infectivity parameter even with sufficient and timely supply of medicines, or (ii) a low infectivity parameter and poor supply of medicines. Also, the use of a stand‐alone epidemic model is found to overestimate disease transmissibility. A compartmental epidemic model is integrated with multi‐echelon supply chain models to further investigate the impact of medicine supply chain on the epidemic dynamics. In integrated models, medicine demands for the supply chain are generated from the disease model, and the medicine supply rate controls the recovery rate of patients in the disease model. It is found that supply chain aspects have a significant effect on epidemic dynamics. Some improvement schemes for supply chain management are also highlighted.

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  6. [해외논문]   Application of a variance‐based sensitivity analysis method to the Biomass Scenario Learning Model   SSCI

    Jadun, Paige (National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, U.S.A.) , Vimmerstedt, Laura J. (National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, U.S.A.) , Bush, Brian W. (National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, U.S.A.) , Inman, Daniel (National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, U.S.A.) , Peterson, Steve (National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, U.S.A.)
    System dynamics review v.33 no.3/4 ,pp. 311 - 335 , 2017 , 0883-7066 ,

    초록

    Abstract Variance‐based sensitivity analysis can provide a comprehensive understanding of the input factors that drive model behavior, complementing more traditional system dynamics methods with quantitative metrics. This paper presents the methodology of a variance‐based sensitivity analysis of the Biomass Scenario Learning Model, a published STELLA model of interactions among investment, production, and learning in an emerging competitive industry. We document the methodology requirements, interpretations, and constraints, and compute estimated sensitivity indices and their uncertainties. We show that application of variance‐based sensitivity analysis to the model allows us to test for non‐additivity, identify influential and interactive variables, and confirm model formulation. To enable use of this type of sensitivity analysis in other system dynamics models, we provide this study's R code, annotated to facilitate adaptation to other studies. A related paper describes application of these techniques to the much larger Biomass Scenario Model.

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  7. [해외논문]   On the growth of the system dynamics field   SSCI

    Homer, Jack B. (Homer Consulting and MIT Research Affiliate, 72 Station Hill Road, Barrytown, NY, 12507, U.S.A.) , Richardson, George P. (University at Albany, Rockefeller College of Public Affairs and Policy, 135 Western Avenue, Albany, NY, 12222, U.S.A.)
    System dynamics review v.33 no.3/4 ,pp. 336 - 346 , 2017 , 0883-7066 ,

    초록

    Abstract Variance‐based sensitivity analysis can provide a comprehensive understanding of the input factors that drive model behavior, complementing more traditional system dynamics methods with quantitative metrics. This paper presents the methodology of a variance‐based sensitivity analysis of the Biomass Scenario Learning Model, a published STELLA model of interactions among investment, production, and learning in an emerging competitive industry. We document the methodology requirements, interpretations, and constraints, and compute estimated sensitivity indices and their uncertainties. We show that application of variance‐based sensitivity analysis to the model allows us to test for non‐additivity, identify influential and interactive variables, and confirm model formulation. To enable use of this type of sensitivity analysis in other system dynamics models, we provide this study's R code, annotated to facilitate adaptation to other studies. A related paper describes application of these techniques to the much larger Biomass Scenario Model.

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  8. [해외논문]   First eight years: a case study of starting a social system design lab   SSCI

    Hovmand, Peter S. (CB 1196, One Brookings Drive, Brown School of Social Work, Washington University in St Louis, St Louis, MO 63130, U.S.A.)
    System dynamics review v.33 no.3/4 ,pp. 347 - 358 , 2017 , 0883-7066 ,

    초록

    Abstract Variance‐based sensitivity analysis can provide a comprehensive understanding of the input factors that drive model behavior, complementing more traditional system dynamics methods with quantitative metrics. This paper presents the methodology of a variance‐based sensitivity analysis of the Biomass Scenario Learning Model, a published STELLA model of interactions among investment, production, and learning in an emerging competitive industry. We document the methodology requirements, interpretations, and constraints, and compute estimated sensitivity indices and their uncertainties. We show that application of variance‐based sensitivity analysis to the model allows us to test for non‐additivity, identify influential and interactive variables, and confirm model formulation. To enable use of this type of sensitivity analysis in other system dynamics models, we provide this study's R code, annotated to facilitate adaptation to other studies. A related paper describes application of these techniques to the much larger Biomass Scenario Model.

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지

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