Simulation and the monte carlo method

The purpose of this paper is to provide a systematic classification for frameworks, methods, and models of in-project quantitative risk analysis (IQRA) for the last 30 years.,An extensive literature review is conducted to identify pertinent IQRA works. Identified IQRA frameworks/methods/models are then classified on the basis of commonalities in key attributes and assumptions. Linkages between each category of IQRAs and dimensions of complexity are also observed.,Around 70 key publications on IQRAs are identified. Major attributes for each work are described. Five distinct categories of IQRAs emerge with unique linkages to complexity dimensions. An analytical framework in the form of a matrix is presented to illuminate evolution on modeling characteristics and to indicate a relationship between respective category and dimensions of project complexity.,The research coverage is intended to be comprehensive but it is by no means exhaustive. This study highlights research opportunities in IQRAs and the possible extension toward in-project quantitative complexity analysis (IQCA).,The proposed matrix provides guidance to practitioners to select the appropriate category of IQRAs for a specific project complexity type in a contingency fashion. The study highlights lessons from development and utilization of IQRAs. Outstanding issues from IQRAs are discussed to avoid similar drawbacks for IQCAs.,This study provides an original framework/matrix to classify extant works in IQRAs. It also establishes an association between IQRAs and the emerging conceptual works of complexity.

From project risk to complexity analysis: a systematic classification

Projects in their early design include many uncertainties. They are not properly specified, their scope is not fully defined and they involve a lot of uncertainties in quantities, time durations, costs and activities. Rigid figures like CPM and other deterministic methods do not permit to make good uncertainty management, which at the end brings along a lot of mistakes and waste of limited resources. Conventional projects management has tried to give both a solution and an approximation to the problem with different tools like PERT, in order to avoid problems of conventional figures. However, some of these methods are insufficient to model variables and process behavior, which represents a lack of crucial information to make good forecasts. Monte Carlo simulation avoids many of these inconveniences. Using the probabilistic distributions of both internal and external variables and processes, Monte Carlo simulation can model their behavior, thus showing activities and different project scenarios. This paper describes an ongoing research effort to develop a methodology as a tool for project management and planning, in which the uncertainty is tackled in order to obtain an overview of the project and its environment, which results in saving costs, time and effort in planning and control.

A. Torres

Papers

Project planning under uncertainty using Monte Carlo simulations