Key performance indicators for measuring construction success

The construction industry is dynamic in nature. The concept of project success has remained ambiguously defined in the construction industry. Project success is almost the ultimate goal for every project. However, it means different things to different people. While some writers consider time, cost and quality as predominant criteria, others suggest that success is something more complex. The aim of this paper is to develop a framework for measuring success of construction projects. In this paper, a set of key performance indicators (KPIs), measured both objectively and subjectively are developed through a comprehensive literature review. The validity of the proposed KPIs is also tested by three case studies. Then, the limitations of the suggested KPIs are discussed. With the development of KPIs, a benchmark for measuring the performance of a construction project can be set. It also provides significant insights into developing a general and comprehensive base for further research.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Decision Matrix Compared with Partnering Principles

The ideas for this CRC research project are based directly on Sidwell, Kennedy and Chan (2002). That research examined a number of case studies to identify the characteristics of successful projects. The findings were used to construct a matrix of best practice project delivery strategies. The purpose of this literature review is to test the decision matrix against established theory and best practice in the subject of construction project management.

Model Based Estimating System for Civil Concrete Structures

An earlier CRC-CI project on ‘automatic estimating’ (AE) has shown the key benefit of model-based design methodologies in building design and construction to be the provision of timely quantitative cost evaluations. Furthermore, using AE during design improves design options, and results in improved design turn-around times, better design quality and/or lower costs. However, AEs for civil engineering structures do not exist; and research partners in the CRC-CI expressed interest in exploring the development of such a process. This document reports on these investigations. The central objective of the study was to evaluate the benefits and costs of developing an AE for concrete civil engineering works. By studying existing documents and through interviews with design engineers, contractors and estimators, we have established that current civil engineering practices (mainly roads/bridges) do not use model-based planning/design. Drawings are executed in 2D and only completed at the end of lengthy planning/design project management lifecycle stages. We have also determined that estimating plays two important, but different roles. The first is part of project management (which we have called macro level estimating). Estimating in this domain sets project budgets, controls quality delivery and contains costs. The second role is estimating during planning/design (micro level estimating). The difference between the two roles is that the former is performed at the end of various lifecycle stages, whereas the latter is performed at any suitable time during planning/design.

Experimental methodology report

In architectural design and the construction industry, there is insufficient evidence about the way designers collaborate in their normal working environments using both traditional and digital media. It is this gap in empirical evidence that the CRC project, “Team Collaboration in High Bandwidth Virtual Environments” addresses. The project is primarily, but not exclusively, concerned with the conceptual stages of design carried out by professional designers working in different offices. The aim is to increase opportunities for communication and interaction between people in geographically distant locations in order to improve the quality of collaboration. In order to understand the practical implications of introducing new digital tools on working practices, research into how designers work collaboratively using both traditional and digital media is being undertaken. This will involve a series of empirical studies in the work places of the industry partners in the project. The studies of collaboration processes will provide empirical results that will lead to more effective use of virtual environments in design and construction processes. The report describes the research approach, the industry study, the methods for data collection and analysis and the foundation research methodologies. A distinctive aspect is that the research has been devised to enable field studies to be undertaken in a live industrial environment where the participant designers carry out real projects alongside their colleagues and in familiar locations. There are two basic research objectives: one is to obtain evidence about design practice that will inform the architecture and construction industries about the impact and potential benefit of using digital collaboration technologies; the second is to add to long term research knowledge of human cognitive and behavioural processes based on real world data. In order to achieve this, the research methods must be able to acquire a rich and heterogeneous set of data from design activities as they are carried out in the normal working environment. This places different demands upon the data collection and analysis methods to those of laboratory studies where controlled conditions are required. In order to address this, the research approach that has been adopted is ethnographic in nature and case study-based. The plan is to carry out a series of indepth studies in order to provide baseline results for future research across a wider community of user groups. An important objective has been to develop a methodology that will produce valid, significant and transferable results. The research will contribute to knowledge about how architectural design and the construction industry may benefit from the introduction of leading edge collaboration technologies. The outcomes will provide a sound foundation for the production of guidelines for the assessment of high bandwidth tools and their future deployment. The knowledge will form the basis for the specification of future collaboration products and collaboration processes. This project directly addresses the industry-identified focus on cultural change, image, e-project management, and innovative methods.

Uptake of an OHS code of practice by Australian construction firms

The Australian construction industry is moving towards the implementation of a voluntary code of practice (VCP) for occupational health and safety (OHS). The evidence suggests that highly-visible clients and project management firms, in addition to their subcontractors, will embrace such a code, while smaller firms not operating in high-profile contracting regimes may prove reticent. This paper incorporates qualitative data from a research project commissioned by Engineers Australia and supported by the Australian Contractors’ Association, Property Council of Australia, Royal Australian Institute of Architects, Association of Consulting Engineers Australia, Australian Procurement and Construction Council, Master Builders Australia and the Australian CRC for Construction Innovation. The paper aims to understand the factors that facilitate or prevent the uptake of the proposed VCP by smaller firms, together with pathways to adoption.

Uptake of an OHS code of practice by construction firms : barriers and enablers in an Australian industry

The Australian construction industry, reflecting a global trend, is moving towards the implementation of a voluntary code of practice (hereafter VCP) for occupational health and safety. The evidence suggests that highlyvisible clients and project management firms, in addition to their subcontractors, look set to embrace such a code. However, smaller firms not operating in high-profile contracting regimes may prove reticent to adopt a VCP. This paper incorporates qualitative data from a high-profile research project commissioned by Engineers Australia and supported by the Australian Contractors’ Association, Property Council of Australia, Royal Australian Institute of Architects, Association of Consulting Engineers Australia, Australian Procurement and Construction Council, Master Builders Australia and the Australian CRC for Construction Innovation. The paper aims to understand the factors that facilitate or prevent the uptake of the VCP by smaller firms, together with pathways to the adoption of a VCP by industry.

Construction projects immediate health check : a CSF & KPI approach

Many factors have the potential to influence human health. These factors need to be monitored to maintain health. As is the case with human health, construction projects have a number of critical factors that can facilitate a broad evaluation of project health. In order to use these factors as an indication of health, they need to be assessed. This assessment can help to achieve desired outcomes for the project. This paper discusses the approach of assessing Critical Success Factors (CSFs) using Key Performance Indicators (KPIs) to ascertain the immediate health of a construction project. This approach is applicable to all phases of construction projects and many construction procurement methods. KPIs have been benchmarked on the basis of industry standards and historical data. The robustness of the KPIs to assess the immediate health of a project has been validated using Australian and international case studies.

Decision support system architecture for consultant selection

Decision Support System (DSS) has played a significant role in construction project management. This has been proven that a lot of DSS systems have been implemented throughout the whole construction project life cycle. However, most research only concentrated in model development and left few fundamental aspects in Information System development. As a result, the output of researches are complicated to be adopted by lay person particularly those whom come from a non-technical background. Hence, a DSS should hide the abstraction and complexity of DSS models by providing a more useful system which incorporated user oriented system. To demonstrate a desirable architecture of DSS particularly in public sector planning, we aim to propose a generic DSS framework for consultant selection. It will focus on the engagement of engineering consultant for irrigation and drainage infrastructure. The DSS framework comprise from operational decision to strategic decision level. The expected result of the research will provide a robust framework of DSS for consultant selection. In addition, the paper also discussed other issues that related to the existing DSS framework by integrating enabling technologies from computing. This paper is based on the preliminary case study conducted via literature review and archival documents at Department of Irrigation and Drainage (DID) Malaysia. The paper will directly affect to the enhancement of consultant pre-qualification assessment and selection tools. By the introduction of DSS in this area, the selection process will be more efficient in time, intuitively aided qualitative judgment, and transparent decision through aggregation of decision among stakeholders.

Monitoring the Performance of Complex Projects from Multiple Perspectives over Multiple Time Frames

When complex projects go wrong they can go horribly wrong with severe financial consequences. We are undertaking research to develop leading performance indicators for complex projects, metrics to provide early warning of potential difficulties. The assessment of success of complex projects can be made by a range of stakeholders over different time scales, against different levels of project results: the project’s outputs at the end of the project; the project’s outcomes in the months following project completion; and the project’s impact in the years following completion. We aim to identify leading performance indicators, which may include both success criteria and success factors, and which can be measured by the project team during project delivery to forecast success as assessed by key stakeholders in the days, months and years following the project. The hope is the leading performance indicators will act as alarm bells to show if a project is diverting from plan so early corrective action can be taken. It may be that different combinations of the leading performance indicators will be appropriate depending on the nature of project complexity. In this paper we develop a new model of project success, whereby success is assessed by different stakeholders over different time frames against different levels of project results. We then relate this to measurements that can be taken during project delivery. A methodology is described to evaluate the early parts of this model. Its implications and limitations are described. This paper describes work in progress.