Isabel M. Horta

Performance Assessment of Construction Companies Integrating Key Performance Indicators and Data Envelopment Analysis

The web benchmarking systems broadly used in the construction industry (CI) are designed to provide results based on key performance indicators (KPIs). No insights concerning organization overall performance and improvements targets are available. This research aims to fulfill this gap using data envelopment analysis (DEA) as a method to complement the information provided by a set of KPIs. The methodology proposed is useful to all organizations involved in benchmarking routines. To enable a more realistic assessment of CI companies, two types of DEA models were used, one allows factor weights to vary freely and the other includes weight restrictions. These models assign an efficiency score to each organization, identifying efficient organizations and providing performance improvements targets for the others. To enable suggesting targets for all organizations, expert opinion was used to specify virtual units which were included in the efficiency assessment to define a practical frontier located beyond the productivity levels of the original DEA frontier. Based on a sample of 20 Portuguese leading contractors, the Portuguese web benchmarking system for CI, icBench, was used to demonstrate the advantages of integrating the DEA method with KPIs benchmark scores.

Company failure prediction in the construction industry

This paper proposes a new model to predict company failure in the construction industry. The model includes three major innovative aspects. The use of strategic variables reflecting the key specificities of construction companies, which are critical to explain company failure. The use of data mining techniques, i.e. support vector machine to predict company failure. The use of two different sampling methods (random undersampling and random oversampling with replacement) to balance class distributions. The model proposed was empirically tested using all Portuguese contractors that operated in 2009. It is concluded that support vector machine, with random oversampling and including strategic variables, is a very robust tool to predict company failure in the context of the construction industry. In particular, this model outperforms the results obtained with logistic regression.

Competitive positioning and performance assessment in the construction industry

The purpose of this paper is to characterize the competitive positioning of the construction industry companies and evaluate their financial performance. The methodology proposed involves three major stages. The first stage concerns the identification of the competitive positioning of companies within the construction sector. This is achieved using a hierarchical clustering algorithm suitable for large datasets and mixed type variables. The second stage is the analysis of performance of the different clusters. This is done using the Data Envelopment Analysis technique. To characterize in detail the main performance features of each cluster, a decision tree is used to extract the main rules concerning the performance spread within each cluster and the gap between the cluster best practices and the national benchmarks. The third stage concerns the analysis of the strengths, weaknesses and areas of potential improvement for contractors in each competitive positioning. This required the analysis of benchmark companies of each cluster. The methodology proposed was applied for the analysis of performance of all contractors that operate in the Portuguese construction industry.

A nonparametric methodology for evaluating convergence in a multi-input multi-output setting

This paper presents a novel nonparametric methodology to evaluate convergence in an industry, considering a multi-input multi-output setting for the assessment of total factor productivity. In particular, we develop two new indexes to evaluate σ-convergence and β-convergence that can be computed using nonparametric techniques such as Data Envelopment Analysis. The methodology developed is particularly useful to enhance productivity assessments based on the Malmquist index. The methodology is applied to a real world context, consisting of a sample of Portuguese construction companies that operated in the sector between 2008 and 2010. The empirical results show that Portuguese companies tended to converge, both in the sense of σ and β, in all construction activity segments in the aftermath of the financial crisis.

Design of Performance Assessment System for Selection of Contractors in Construction Industry E-Marketplaces

AbstractThis paper presents a framework to facilitate the selection of the most appropriate company to be contracted among competitive bids. This framework is intended to be integrated in e-marketplaces to comply with the major technological advances in the construction industry. A novel feature of the system is that it allows bilateral evaluations between companies to better understand general contractor–subcontractor relationships and to improve the level of transparency within the construction sector. The performance assessment system incorporates other innovative features, such as the ability to specify a set of performance indicators suitable for inclusion in e-marketplaces covering three different perspectives: company reliability, operation performance, and bid attributes. The system also allows the integration of the preferences of the decision maker concerning the selection of the best company for a given work.

Downscaling Aggregate Urban Metabolism Accounts to Local Districts

Summary Urban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio-based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste.

The impact of internationalization and diversification on construction industry performance

This paper investigates the impact of internationalization and diversification strategies on the financial performance of construction industry companies. The results obtained can guide the design of strategies to pursue company growth and achieve competitive advantage. The evaluation of companies’ performance is based on the use of the Data Envelopment Analysis technique to aggregate financial indicators using optimized weights. The impact of internationalization and diversification on company performance is explored using truncated regression, controlling for the effect of contextual factors such as company age, size and time. Data Envelopment Analysis and truncated regression were complemented with bootstrapping to ensure the robustness of the results obtained. The activity of Portuguese and Spanish contractors in the period 2002 to 2011 is used as case study. The empirical results show that internationalization has a positive impact on financial performance, although this effect is only statistically significant for Spanish contractors. Diversification has a nonlinear relationship with performance, benefiting companies with either a small number of core activities or companies with a broad scope of activities.

Evaluation of Strengthening Techniques Using Enhanced Data Envelopment Analysis Models

This research intends to develop a model to support the selection of the best strengthening technique to be adopted in rehabilitation projects. This methodology is particularly useful for project teams that need to select the most suitable strengthening technique among several solutions. The model proposed includes the typical variables that capture the main technical characteristics of the strengthening solution and also economic variables associated to the costs of the intervention. The model proposed is based on Data Envelopment Analysis specified with a directional distance function. It has the ability of calculating an overall performance score for each solution showing it in the best possible light. To demonstrate the advantages of the methodology developed, it were used the results of a study conducted in Portugal. From the empirical application, it was possible to conclude that the best strengthening solution may vary depending on whether the costs of the interventions are or not included in the model.

The Assessment of Municipal Services: Environmental Efficiency of Buildings Construction

This paper develops an innovative methodology to assess municipal performance concerning the environmental efficiency of new buildings construction, focusing on the consumption of different types of materials. This study aims to support local governments in the definition of policies for improvements in service provision based on the results of a benchmarking study. The methodology developed includes two stages. The first step concerns the evaluation of municipal environmental efficiency using Data Envelopment Analysis and the identification of factors that may explain different levels of performance. The second step enables the classification of municipalities in terms of the efforts required to achieve environmental efficiency. For this purpose, we used clustering analysis, namely the k-means algorithm. To illustrate the methodology developed, we analyzed the data of the major materials used in the construction of new buildings (metals, non-metallic minerals, fossil fuels, and biomass) in the municipalities of Lisbon metropolitan area between 2003 and 2009. The study revealed that the environmental efficiency of new buildings construction varies considerably among municipalities, suggesting a high potential for performance improvement.