Empirical Perspective on Activity Durations for Project-Management Simulation Studies

Abstract

Simulation has played an important role in project-management studies of the last decades, but in order for them to produce practical results, a realistic distribution model for activity durations is indispensable. The construction industry often has needed historical records of project executions, to serve as inputs to the distribution models, but a clearly outlined calibration procedure is not always readily available, nor are their results readily interpretable. This study seeks to illustrate how data from the construction industry can be used to derive realistic input distributions. Therefore, the Parkinson simulation model with a lognormal core is applied to a large empirical dataset from the literature and the results are described. From a discussion of these results, an empirical classification of project executions is presented. Three possible uses are presented for the calibration procedure and the classification in project management simulation studies. These were validated using a case study of a construction company.

On the use of multivariate regression methods for longest path calculations from earned value management observations

“…The two previously mentioned weaknesses, and surely the inability of using historical data in a new theoretical framework as the one presented here, is probably the main reason why the research of this paper is not immediately ready for practical implications. However, it should be mentioned that in a previous paper [39], a general framework has been presented that shows how historical data can be used to derive distributions for MCS, which can also be used for the methodology presented in this paper. Moreover, this study also presents a way of reducing effort of project control by avoiding superfloues drill-downs in the WBS, which is both attractive and necessary for project managers in charge of real projects.…”

Success evaluation model for project management

“…In the opinion of the authors, the metrics differ according to the type of project, the phase in which the project is found, the method of expression: absolute or relative (Samset, 1998), and individual interest groups which often have their own way of perceiving project success (Davis, 2014). The traditional measure of project success is based on what is known as the triple imperative of a project and is associated with the fulfi lment of the time period, costs and goals of the project (de Carvalho et al, 2015)cost, and margins. We adopt a contingency approach that evaluates the complexity of the project, according to 4 categories, the effect of industry sector and countries.…”

“…The author of the RIPRAN method is Associate Professor Branislav Lacko. The RIPRAN method can be used in all phases of the project (Cooper et al, 2014). The method was originally created for risk analysis automation projects within the framework of a research project at the Technical University in Brno.…”

“…Due to the turbulent and inter-cultural environment of contemporary, often highly complex, projects, these efforts are extremely complicated. Modelling enables the project team to obtain valuable information for future decisionmaking (Colin & Vanhoucke, 2016;Wauters & Vanhoucke, 2016). For example, modelling enhances knowledge of the functioning of the system for projects that are still in the proposal phase (model -original).…”

“…The risks associated with deadlines or budgets are extremely serious. Problems of project modelling and simulation are highly topical at the present time, because the application of project management principles is expanding into further areas -change management (Cummings et al, 2016;Yin-xiang, 2013), crisis management (Wimelius & Engberg, 2015), innovation management (Balkienė, 2013;Dudzevičiūtė & Tvaronavičienė, 2011;Grossmann, 2009), etc. -which have only very basic features of projects.…”