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Modeling and Simulation of Project Management through the PMBOK® Standard Using Complex Networks

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  • Luz Stella Cardona-Meza
  • Gerard Olivar-Tost

Abstract

Discussion about project management, in both the academic literature and industry, is predominantly based on theories of control, many of which have been developed since the 1950s. However, issues arise when these ideas are applied unilaterally to all types of projects and in all contexts. In complex environments, management problems arise from assuming that results, predicted at the start of a project, can be sufficiently described and delivered as planned. Thus, once a project reaches a critical size, a calendar, and a certain level of ambiguity and interconnection, the analysis centered on control does not function adequately. Projects that involve complex situations can be described as adaptive complex systems, consistent in multiple interdependent dynamic components, multiple feedback processes, nonlinear relations, and management of hard data (process dynamics) and soft data (executive team dynamics). In this study, through a complex network, the dynamic structure of a project and its trajectories are simulated using inference processes. Finally, some numerical simulations are described, leading to a decision making tool that identifies critical processes, thereby obtaining better performance outcomes of projects.

Suggested Citation

  • Luz Stella Cardona-Meza & Gerard Olivar-Tost, 2017. "Modeling and Simulation of Project Management through the PMBOK® Standard Using Complex Networks," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    • Handle: RePEc:hin:complx:4791635
    DOI: 10.1155/2017/4791635
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    2. P. Urgilés & J. Claver & M. A. Sebastián, 2019. "Analysis of the Earned Value Management and Earned Schedule Techniques in Complex Hydroelectric Power Production Projects: Cost and Time Forecast," Complexity, Hindawi, vol. 2019, pages 1-11, April.
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    4. Dorota Kuchta & Stanisław Stanek, 2020. "Application of Simulation to Selecting Project Strategy for Autonomous Research Projects at Public Universities," Administrative Sciences, MDPI, vol. 10(1), pages 1-23, March.

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