Search results

The purpose of this chapter is first to establish a state of the art about cargo tram, in order to understand its opportunities and constraints. In a second step, the aim is to add an often overlooked spatial dimension in the analysis, which may offer a renewed insight into the issue and extend the understanding of the potential use of urban light rail for freight.

The first step of the analysis is carried out through a study of the literature. In particular, the potential use of cargo tram for the various urban supply chains is evaluated. The aim is to establish a European state of the art on urban light rail use for freight. Then, to concretise these elements and to add an empirical geographical approach, the Brussels situation is studied in detail.

This combination of a theoretical analysis and a field approach of the case of Brussels allows us to lead an original study. Obstacles, opportunities and interest for the implementation of cargo trams are specified. In addition, the use of geographical approach gives a new point of view since most of the publications in this field are based on economic or technical approaches.

This study may help public and private actors involved in urban freight matters, as well as public transport companies, to better understand the issues related to cargo trams. In a context where this transport mode is promoted and discussed in a growing number of cities and urban supply chains, it is useful to get an objective synthesis and a prospective analysis on this topic. This research may also have social implications in the way it helps to define a more sustainable urban logistics.

The originality of this research is related to the combination of a theoretical and empirical approach, to the link that is made between urban supply chains and capabilities of cargo trams and finally to the prospective study on the Brussels case. Indeed, because of the novelty of the topic, there are very few studies, either ex-post or ex-ante.

The inherent risks and their interactive impacts in megaproject development have been found in numerous cases worldwide. Although risk management standards have been recommended for the best practice in engineering construction projects, there is still a lack of systematic approaches to describing the interactions. Interactions such as social, technical, economic, ecological and political (STEEP) risks have complex and dynamic implications for megaproject construction. For a better understanding and effective management of megaprojects such as the Edinburgh Tram project, the dynamic interaction of concomitant risks must be studied.

A systems dynamic methodology was adopted following the comprehensive literature review. Documentary data were gathered from the case study on Tram Network Project in Edinburgh.

A casual loop of typical evolution of key indicators of risks was then developed. A hypothesised model of social and ecological (SE) risks was derived using the system dynamics (SD) modelling technique. The model was set up following British Standards on risk management to provide a generic tool for risk management in megaproject development. The study reveals that cost and time overruns at the developmental stage of the case project are caused mainly by the effects of interactions of risk factors from the external macro project environment on a timely basis.

This article presented a model for simulating the socio-ecological risk confronting the management and construction of megaprojects. The use of SD provided the opportunity to explain the nature of all risks, particularly the SE risks in the past stages of project development.

The purpose of the paper is to put forward a research‐based argument on the benefits of simulation approach in managing design at an early stage of a project. Having selected an optimal design configuration, the operational uncertainties can be removed and investment decisions are fully justified over the lifecycle of projects.

A simulation‐based methodology embodying balanced scorecard (BSC) for measuring the operational and business performance has been synthesised in the research. Multi‐criteria decision analysis (MCDA) has been employed to evaluate the trade‐offs between feasible design alternatives and to select the optimum design configuration.

The findings show that the integrated framework developed in this research by integrating simulation technology, BSC and MCDA adds significant contributions in improving the current body of knowledge in the design management practices.

The framework should further be tested by applying to large engineering projects in order for realising the benefits in the decision‐appraisal process. Access to data in large projects before implementation would be the greatest challenge from a commercial‐in‐confidence perspective.

The framework will help the practitioner's understanding and management of the design configuration in highly complex modern projects. This will allow the decision makers to manage interdependency of complex processes and select optimal designs upfront. The resulting framework will significantly contribute to reducing scope creeps and cost variations and thereby reducing contractual disputes in projects.

The original design of the integrated framework of this kind for managing design complexity in projects adds significant value in the design management practices. The use of simulation embodying BSC and MCDA adds significant novelty in theoretical advancement of contemporary knowledge in the design management profession.

Drawing on mainstream arguments in the literature, the paper presents a coherent and holistic view on the causes of cost overruns, and the dynamics between cognitive dispositions, learning and estimation. A cost prediction model has also been developed using data mining for estimating final cost of projects. The paper aims to discuss these issues.

A mixed-method approach was adopted: a qualitative exploration of the causes of cost overrun followed by an empirical development of a final cost model using artificial neural networks.

A conceptual model to distinguish between the often conflated causes of underestimation and cost overruns on large publicly funded projects. The empirical model developed in this paper achieved an average absolute percentage error of 3.67 percent with 87 percent of the model predictions within a range of ±5 percent of the actual final cost.

The model developed can be converted to a desktop package for quick cost predictions and the generation of various alternative solutions for a construction project in a sort of what-if analysis for the purposes of comparison. The use of the model could also greatly reduce the time and resources spent on estimation.

A thorough discussion on the dynamics between cognitive dispositions, learning and cost estimation has been presented. It also presents a conceptual model for understanding two often conflated issues of cost overrun and under-estimation.