Journal of Construction Engineering and Management

The low-bid method, typically used for competitive bidding in the United States, may result in a contract with a firm that submits either accidentally or deliberately an unrealistically low-bid price. Such an occurrence hurts both the owner and the contractor by promoting disputes, increased costs, and schedule delays. To address this problem, other countries have adopted bidding methods based on the average of the bids submitted. One such approach is the below-average method where the winning bid is closest to but below the average of all bids. A competitive bidding model for the below-average-bid method is presented and its merits relative to the average-bid method and the low-bid method are explored. The below-average-bid process is investigated analytically and through Monte Carlo simulation. The results of bidding models for the below-average, the average, and the low-bid methods are presented in four easy-to-use nomograms which allow contractors to determine the optimal lump-sum bid price for each method without the need for complicated analysis. A comparison of the three methods provides information and insights to help owners with the difficult choice of a suitable bidding method for the project at hand.

The need to identify the critical factors in the successful development and management of the price competitive alliance model has become a priority for public sector clients, as there is an increasing need to obtain better value for money (VfM) for its infrastructure projects. Yet, there has been limited research undertaken about the nature and use of price competitive alliances. This research determines the success factors (SFs) for price competitive alliances during their relationship development phases as this form of alliance contract is being used extensively by public sector clients to procure critical infrastructure projects in Western Australia (WA). A review of the literature of partnering and alliance contracting is used to develop a conceptual model of potential SFs. Interviews with 21 practitioners who participated in alliances on behalf of a WA public sector client were conducted to determine their perceived SF for price competitive alliances throughout the relationship development process. Contrary to the literature, each of the SFs identified was required in each stage of the relationship development process. In particular, the development of a leadership enriched culture (where people view the project as an extension of themselves and feel good about what they personally achieve through cooperation) was deemed necessary for the successful implementation of a price competitive alliance. Moreover it was revealed that the establishment of trust for the client began to arise at the point when a realistic target outturn cost that was able to deliver VfM, as well as an innovative sustainable outcome could be attained. Price competitive alliance models are considered to be an appropriate procurement method, albeit during the current economic climate an alternative to public private partnerships, for delivering large complex infrastructure projects.

This paper introduces a conceptual scoring-based contractor evaluation system to address the problems created by the subjective nature of surety underwriting process. The methodology presented in this paper provides the surety underwriters with an objective tool for estimating the probability that the evaluated contractor would perform in a satisfactory manner with respect to the main decision factors considered in the bond underwriting process. The proposed model could be used by decision makers to prescreen contractors and make underwriting decisions. The paper first discusses the construction bond underwriting process and provides a brief introduction to the scoring technique as the modeling environment used in this paper. Then, the three steps included in the development of the proposed scoring system are presented in detail followed by an example demonstrating the application of the final product.

Lean construction and building information modeling (BIM) are quite different initiatives, but both are having profound impacts on the construction industry. A rigorous analysis of the myriad specific interactions between them indicates that a synergy exists which, if properly understood in theoretical terms, can be exploited to improve construction processes beyond the degree to which it might be improved by application of either of these paradigms independently. Using a matrix that juxtaposes BIM functionalities with prescriptive lean construction principles, 56 interactions have been identified, all but four of which represent constructive interaction. Although evidence for the majority of these has been found, the matrix is not considered complete but rather a framework for research to explore the degree of validity of the interactions. Construction executives, managers, designers, and developers of information technology systems for construction can also benefit from the framework as an aid to recognizing the potential synergies when planning their lean and BIM adoption strategies.

The transient, unique, and complex nature of construction projects makes safety management exceptionally difficult. Most construction safety efforts are applied in an informal fashion under the premise that simply allocating more resources to safety management will improve site safety. Currently, there is no mechanism by which construction-site safety professionals may formally evaluate safety risk and select safety program elements for implementation. This paper introduces and validates a risk-based safety and health analytical model that can be used to evaluate expected risk given specific worker activities, strategically select highly effective safety program elements for implementation when resources are limited, and quantify resulting risk once the identified safety elements have been implemented. Specifically, the paper has three primary objectives: (1) introduce a risk-based construction safety and health analytical model; (2) validate relevant data used to populate the model; and (3) illustrate the applications of the model in practice. The findings of this research indicate that the values used to populate the model are reliable and that the model has the potential to significantly improve construction safety management.

Appropriate training can only be developed if training needs for specific skills are carefully identified. This paper, further to an aggregate model developed previously, aims to forecast the occupational share of the aggregate manpower demand for the construction industry of Hong Kong. The forecast, based on existing manpower statistics, is divided into two levels: broad occupations and detailed occupations. The broad occupational demand forecasting model is formulated using a time-series regression analysis to derive the relationship between the occupational share and the construction output cycle, technology, and various work-mix variables; whereas exponential smoothing technique is used to forecast the share of detailed occupations. This occupational demand estimation can provide solid information to facilitate manpower planning. It enables the policymakers to foresee the trends of occupational manpower demand and formulate policies and training and retraining programs tailored to deal effectively with the industry’s human resource requirements in this critical sector of the economy. Although the study focuses on developing models for the Hong Kong construction labor market, the adopted methodology can be applied in other labor markets to develop such models for manpower planning.

The objective of this research is to explain why actors are not using interorganizational information and communication technology (ICT) in construction projects in the intended way, by determining the mechanisms that influence the way actors use this ICT over time during a construction project. This explorative research results in a theoretical model that contains four key categories that explains the way actors use ICT in construction projects: (1) personal motivation; (2) external motivation; (3) knowledge and skills; and (4) acting opportunities. The model is compared with three existing influential models about the adoption and use of ICT: the unified theory of acceptance and use of technology, the theory of planned behavior, and the technology acceptance model. In these models missing elements are found. In addition, it is found that fundamental characteristics of construction projects, such as the fragmented and temporarily nature of construction projects, and the different working practices, resources, and objectives of the organizations involved cause most of the barriers to the intended use of interorganizational ICT.

Change orders are very common in almost every construction project nowadays, often resulting in increases of 5–10% in the contract price. Understanding the consequences of such trends, several studies have attempted to quantify the impact of change orders on the project cost. Most of the studies aimed at the quantification of the change orders were sponsored by contractors’ organizations, where statistical models used to quantify the impact of the change orders on the project cost were based on data supplied by the contractors; a situation that can lead to owner-contractor disagreements related to the quantification method used. In addition, most of the studies tackled commercial and electromechanical work, and very rare studies tackled the field of heavy construction; a field that suffers from change orders because of errors and omissions, scope of work changes, or changes because of unforeseen conditions. This study addresses the need for a statistical model to quantify the increase of the contract price due to change orders in heavy construction projects in Florida. The model is based on data collected from 16 Florida DOT projects with contract values that ranged between $10–$25 million, and that encountered an increase in the contract price from 0.01 to 15%. Eleven variables were analyzed to test their impact on the cost of the change orders. The study concluded that most significant variables that impact the value of the change order, which are (1) the timing of the change order and (2) when the reason for issuing the change order is unforeseen conditions. Two regression models are developed and validated as follows: (1) a model to quantify the percentage increase in the contract price due to the change orders that increase the contract price from 0.01 to 5% and (2) a model to quantify the percentage increase in the contract price due to the change orders that increase the contract price from 5 to 15%. Those models will provide the owner with a retrospective or forward pricing of the change orders, and hence, allow the owner to estimate and utilize contingency amounts.

Failure mode and effect analysis (FMEA) is recognized as one of the most beneficial techniques in reliability programs. FMEA is a structured technique that can help in identifying all failure modes within a system, assessing their impact, and planning for corrective actions. Although this technique has been widely used in many industries, it has some limitations. The purpose of this paper is to extend the application of FMEA to risk management in the construction industry. Fuzzy logic and fuzzy analytical hierarchy process (AHP) are used to address the limitations of traditional FMEA. In essence, this method explores the concept of fuzzy expert systems to map the relationship between impact (I), probability of occurrence (P), and detection/control (D) and the level of criticality of risk events. A case study is presented to validate the concept. The results obtained confirm the capability of fuzzy FMEA and fuzzy AHP to address several drawbacks of the traditional FMEA application. The use of this approach can support the project management team to establish corrective actions in a timely manner.

Frequent and regular prediction of productivity is needed to effectively manage construction operations in progress. This need is proven by significant deviations in productivity between estimated values and actual values, and the dynamic and stochastic changes in productivity over time. Five statistical methodologies that are appropriate for contemporaneous time series were cross validated in the present study. Validation was conducted by comparing the performance of forecasting models constructed using the methodologies. Performance was measured by evaluating the residual sum of squares and the correlation coefficients. As a result, univariate time series analysis was found to be the best-performing methodology. The univariate time series model is particularly beneficial in two respects. Using a single series of contemporaneous productivity data in numeric form, it reduces the effort expended in collecting and analyzing data, and improves the objectivity of analysis.

Productivity has a profound impact on projects that depend on time and cost of construction operations. In addition, time and cost estimates are derived from productivity. Thus, accurate prediction of productivity is essential to effectively plan and control construction operations. Predicting productivity of ongoing operations, however, is challenging. Due to dynamic and stochastic changes in productivity over time during construction, frequent and regular forecasting of short-term productivity is critical in managing ongoing operations. The present research investigated the characteristics of series of periodic productivity that should be taken into consideration to effectively predict short-term productivity continually and proactively. Given the identified characteristics, this study reviewed a few potential statistical methodologies that can make full use of contemporaneous time series data related to production for the purpose of predicting short-term productivity by using trend analysis. The methodologies were demonstrated in this paper using an example case, through which data processing and modeling procedure for modeling contemporaneous series data were explained.

This research proposes an innovative critical chain method (ICCM) for project planning and control under resource constraints and uncertainty. An improved genetic algorithm is developed to identify the critical chain and to obtain the optimal start time of each activity under the most optimistic duration of each activity and resource constraints. Furthermore, a feeding buffer is added in an insert point in order to deal with uncertainties. The benefits of applying this ICCM are demonstrated in an example project.