Modelling risk effect using Monte Carlo Technique: an application for innovative projects

Ibrahim Motawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution book


The need to gain competitive advantage stimulates many construction organisations to exploit innovative products and processes. However, the high level of uncertainty associated with innovative construction leads many organisations to focus on the application of traditional construction process and products. Implementing construction innovation often involves experimentation, iteration and refinement of activities that are reliant on volatile information. The acceptance of any innovation in construction often only comes after very significant advantages of this innovation on several projects. Therefore, construction organisations should exhibit specific characteristics to promote new technology and to overcome the expected barriers to innovation in order to achieve the desired competitive advantage, pursue new markets and improve productivity. Because of the considerable unknown risks, implementing innovative technologies creates a greater need for co-operation among businesses to address the planning, development and implementation of technological capabilities to shape and accomplish the strategic and operational objectives of an organisation. This chapter introduces a model to simulate the risk effect on the process of implementing technological innovations in construction. It includes the model hypothesis, techniques and its application on a case study. The model identifies sources of risks within the innovation process and schedules activities taking into account stochastic analysis of the information influencing implementation. The proposed model is a decision support tool that simulates different scenarios to control the implementation phase.
Original languageEnglish
Title of host publicationRisk Management in Engineering and Construction
Subtitle of host publicationTools and Techniques
EditorsStephen Ogunlana, Prastanta Kumar Dey
Place of PublicationAbingdon, Oxon
Number of pages26
Publication statusPublished - 26 Sept 2019


  • desicion support
  • Monte Carlo techniques
  • modelling risks


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