IDEAS home Printed from https://ideas.repec.org/a/prg/jnlaip/v2017y2017i1id96p4-19.html
   My bibliography  Save this article

Project as a System and its Management
[Projekt jako systém a jeho řízení]

Author

Listed:
  • Jiří Skalický
  • Jiří Vacek
  • Marek Čech
  • Martin Januška

Abstract

The contribution aims to describe project as a system, to define project control goal and strategy, control variables and their relationships. Three common control variables represented by the project triangle, are extended by two other important variables - project risk and quality. The control system consists of two components: social one - project manager and project team - and technical one - project dynamic simulation model as a decision making support of project manager in project milestones. In the project planning phase, the project baseline with planned controlled variables is created. In milestones after project launch, the actual values of these variables are measured. If the actual values deviate from planned ones, corrective actions are proposed and new baseline for the following control interval is created. Project plan takes into account the actual project progress and optimum corrective actions are determined by simulation, respecting control strategy and availability of resources. The contribution presents list of references to articles dealing with project as a system and its simulation. In most cases, they refer to the project control using the Earned Value Management method and its derivatives. Using of the dynamic simulation model for project monitoring and control, suggested in this contribution, presents a novel approach. The proposed model can serve as departure point to future research of authors and for development of appropriate and applicable tool.

Suggested Citation

  • Jiří Skalický & Jiří Vacek & Marek Čech & Martin Januška, 2017. "Project as a System and its Management [Projekt jako systém a jeho řízení]," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 2017(1), pages 4-19.
  • Handle: RePEc:prg:jnlaip:v:2017:y:2017:i:1:id:96:p:4-19
    DOI: 10.18267/j.aip.96
    as

    Download full text from publisher

    File URL: http://aip.vse.cz/doi/10.18267/j.aip.96.html
    Download Restriction: free of charge

    File URL: http://aip.vse.cz/doi/10.18267/j.aip.96.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.18267/j.aip.96?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    2. Pfeifer, Jeremy & Barker, Kash & Ramirez-Marquez, Jose E. & Morshedlou, Nazanin, 2015. "Quantifying the risk of project delays with a genetic algorithm," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 34-44.
    3. Nguyen, Trong-Hung & Marmier, François & Gourc, Didier, 2013. "A decision-making tool to maximize chances of meeting project commitments," International Journal of Production Economics, Elsevier, vol. 142(2), pages 214-224.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lewe, J.-H. & Hivin, L.F. & Mavris, D.N., 2014. "A multi-paradigm approach to system dynamics modeling of intercity transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 188-202.
    2. Mahsa Parsaei Motamed & Shahrooz Bamdad, 2022. "A multi-objective optimization approach for selecting risk response actions: considering environmental and secondary risks," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 266-303, March.
    3. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    4. Ellinas, Christos & Allan, Neil & Johansson, Anders, 2016. "Project systemic risk: Application examples of a network model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 50-62.
    5. Cowan, Kelly R. & Daim, Tugrul U., 2011. "Review of technology acquisition and adoption research in the energy sector," Technology in Society, Elsevier, vol. 33(3), pages 183-199.
    6. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    7. Liu, Zhixue & Ding, Ronggui & Wang, Lei & Song, Rui & Song, Xinyi, 2023. "Cooperation in an uncertain environment: The impact of stakeholders' concerted action on collaborative innovation projects risk management," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    8. Santos, Mário & Bastos, Rita & Cabral, João Alexandre, 2013. "Converting conventional ecological datasets in dynamic and dynamic spatially explicit simulations: Current advances and future applications of the Stochastic Dynamic Methodology (StDM)," Ecological Modelling, Elsevier, vol. 258(C), pages 91-100.
    9. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    10. Lei Xu & Ronggui Ding & Lei Wang, 2022. "How to facilitate knowledge diffusion in collaborative innovation projects by adjusting network density and project roles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1353-1379, March.
    11. Jan Kwakkel & Willem Auping, 2021. "Reaction: A commentary on Lustick and Tetlock (2021)," Futures & Foresight Science, John Wiley & Sons, vol. 3(2), June.
    12. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201704300700001022, Iowa State University, Department of Economics.
    13. Abedi, Vahideh Sadat, 2019. "Compartmental diffusion modeling: Describing customer heterogeneity & communication network to support decisions for new product introductions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    14. Lixin Zhou & Jie Lin & Yanfeng Li & Zhenyu Zhang, 2020. "Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    15. Amini, Mehdi & Wakolbinger, Tina & Racer, Michael & Nejad, Mohammad G., 2012. "Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach," European Journal of Operational Research, Elsevier, vol. 216(2), pages 301-311.
    16. Teglio, Andrea, 2020. "On the typicality of the representative agent," MPRA Paper 105407, University Library of Munich, Germany.
    17. Rixen, Martin & Weigand, Jürgen, 2014. "Agent-based simulation of policy induced diffusion of smart meters," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 153-167.
    18. Yu Zhao & Shaopeng Wei & Yu Guo & Qing Yang & Xingyan Chen & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks," Papers 2202.03874, arXiv.org, revised Jul 2022.
    19. Hai-hua Hu & Jun Lin & Wen-tian Cui, 2015. "Intervention Strategies and the Diffusion of Collective Behavior," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(3), pages 1-16.
    20. Matthew Oremland & Reinhard Laubenbacher, 2014. "Using difference equations to find optimal tax structures on the SugarScape," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(2), pages 233-253, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:prg:jnlaip:v:2017:y:2017:i:1:id:96:p:4-19. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stanislav Vojir (email available below). General contact details of provider: https://edirc.repec.org/data/uevsecz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.