Abstract
Selecting a better performing contractor at the procurement stage is crucial in achieving a successful outcome for a construction project. The construction industry lacks a systematic and purpose driven method to assess performance of contractors using objective metrics. There are many approaches to measuring construction performance, but most are complicated and have high dependency on data that is difficult to attain. This paper aims to create a model for evaluating construction contractors’ performance based on directly attributable measures that are quantitative and easy to gather. This makes such a model more attractive and easier to use. Initially, a detailed literature review revealed different categories of measures of performance (MoP) and corresponding critical measures of performance (CMoP). Through a series of Delphi-based expert forums, the set of measures were fine-tuned and shortlisted. Fuzzy analytic hierarchy process-based comparisons were then used for developing a contractors’ performance model to quantify their level of performance based on a limited set of organisation-specific and project-specific measures. The results indicate a shift from traditional measures and a higher preference towards non-price measures. The performance model can be further developed to systematically rank the prospective contractors at the procurement stage based on seven non-price measures.
Highlights
Contributing around 13 percent of Gross Domestic Product (GDP), construction is one of the largest industry sectors with an annual spending of about USD 10 trillion worldwide [1]
To provide more context on the capabilities/issues of each critical measures of performance (CMoP), they were assessed based on three criteria: (1) accessibility of data, (2) ability to compute and measure, and (3) fairness in reflecting contractor’s performance
Categories of measures of performance (MoP) that have CMoPs that do not fulfil all three assessment criteria were marked as dropouts from the final list of MoPs
Summary
Contributing around 13 percent of Gross Domestic Product (GDP), construction is one of the largest industry sectors with an annual spending of about USD 10 trillion worldwide [1]. It significantly lags in performance compared to other industries with nearly 70% of the projects suffering time and cost overruns and flat lined productivity growth [1,2]. Performance reflects the success of a project and is judged and quantified through performance measurement [3]. To achieve project success, selecting a better performing contractor is pivotal [5,6]. In the traditional procurement system, the adversity and uncertainty experienced in any construction project, coupled with an unsuitable choice of a contractor, are known to be causes of underperformance [7]
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