Relationship between Automation and Integration of Construction Information Systems and Labor Productivity

Information technology (IT) has been used to increase automation and integration of information systems on construction projects for over two decades. However, evidence that overall costs have been reduced or project performance has been improved with IT in construction is limited and mostly focused on application specific studies. A comprehensive understanding of the relationship between IT and project performance helps industry practitioners better understand the likely outcomes of implementation of IT application and likewise benefits researchers in improving the effectiveness in their IT development efforts. An opportunity to examine new evidence exists with the emergence of the Construction Industry Institute’s Benchmarking and Metrics database on construction productivity and practices. This article presents an analysis of that data to determine if there is a relationship between labor productivity and level of IT implementation and integration. Data from industrial construction projects are used to measure the relationships between the automation and integration of construction information systems with productivity. Using the independent sample t-test, the relationship was examined between jobsite productivity across four trades (concrete, structural steel, electrical, and piping) and the automation and integration of various work functions on the sampled projects. The results showed that construction labor productivity was positively related to the use of automation and integration on the sampled projects.     

Role of ASCE in CICE Implementation—Recommendations

The Business Roundtable, an association of some 200 construction company owners, instituted a study called the Construction Industry Cost Effectiveness (CICE) project. CICE is the first industry‐wide effort to define productivity problem areas. Five major areas in which construction cost reductions are possible were identified: project management, construction technology, labor effectiveness, labor supply and training, and regulations and codes. A Task Committee on Productivity of the Construction Division of ASCE was formed to review recommendations presented in the CICE study. The Task Committee recommendations on ASCE's role in implementing solutions to construction productivity problem areas are presented.     

Factors Affecting Engineering Productivity

Engineering performance has a major impact on subsequent project phases, such as procurement and construction, and thus, has the potential to affect the overall project outcome. This study utilizes metrics and a database from the Construction Industry Institute (CII) benchmarking and metrics program to investigate relationships between factors thought to affect direct engineering labor productivity during detailed engineering. Collaborating with industry practitioners, quantitative assessments were analyzed with industry input through various CII committee meetings and industry forums. Significant correlations are found between engineering productivity and project size, project type, project priority, and phase involvement. Correlations are also found between degree of modularization, funded front-end planning effort, and quality management and engineering productivity. These findings extend and, in some cases, contradict previous research.     

Is Construction Labor Productivity Really Declining?

Macroeconomics data suggest that labor productivity declined significantly in the construction industry during the 1979–1998 period. However, microeconomic studies indicate the contrary. This paper critically examines the construction labor productivity macroeconomic data in the United States from 1979 to 1998 to determine their validity and reliability. Data collection, distribution, manipulation, analysis, and interpretation are reviewed and problems are identified. The paper also presents a comparison of construction and manufacturing labor productivity during this period. The main conclusion of the study is that the raw data used to calculate construction productivity values at the macroeconomic level and their further manipulation and interpretation present so many problems that the results should be deemed unreliable. The uncertainty generated in the process of computing these values is such that it cannot be determined if labor productivity has actually increased, decreased, or remained constant in the construction industry for the 1979–1998 period.     

Tailoring Competitive Advantages Derived from Innovation to the Needs of Construction Firms

Innovation is often classified as a cost intensive investment in the construction industry with indefinite returns. Due to the clients’ tendency to award projects based on the lowest costs, innovation is often seen as an unfeasible strategy toward the competitiveness that construction firms are seeking. This study questions whether it is indeed ineffective for construction firms to develop their competitive advantage through innovation. By the application of statistical data across 18 Organization for Economic Cooperation and Development (OECD) countries and expert interviews in Singapore, innovation systems models are developed for both manufacturing and construction firms, respectively. Through comparison of both models, the results suggest that the peculiarities of the construction industry deem innovation as a poor competitive instrument for direct profits. Instead, construction firms can develop their competitive advantage through manipulating innovations that consumers are willing to pay for and innovations that would reduce construction costs. It is recommended that construction firms first utilize quality improvements to exploit consumers’ willingness to pay for innovative products. This initiative would enable construction firms to improve their finances for innovation and develop their “brand” in construction products. Sustainable competitive advantage could then be firmly established when construction firms engage in productivity improvements that lead to lower construction costs and/or faster completion times. This study concludes that innovation can be a useful competitive tool if construction firms aptly strategize it in according to its competitive environment.     

Modeling the Effect of Subjective Factors on Productivity of Trenchless Technology Application to Buried Infrastructure Systems

Trenchless technology (TT) includes a large family of methods utilized for installing and rehabilitating underground utility systems with minimal surface disruption and destruction resulting from conventional excavation. Productivity of TT techniques is affected by a number of subjective factors that need to be evaluated. A productivity index (PI) model is developed in order to represent this subjective effect in refining productivity assessment. The analytic hierarchy process and fuzzy logic are used to develop the proposed PI model that relies on the actual performance of 12 subfactors under three main categories: management, environmental, and physical conditions. The developed PI model resulted in PI equal to 0.7323 and 0.7251 for microtunneling and horizontal directional drilling (HDD) projects, respectively. Multiattribute decision support system software is developed to determine the PI for a specific TT technique using Visual Basic. The PI model is tested, which shows reasonable results. This research is relevant to both industry practitioners and researchers. It provides practitioners with a model that justifies their productivity calculation by quantifying subjective factors effect, which will affect their schedule and cost estimation for trenchless projects. In addition, it provides researchers with the development methodology for the PI model.     

Factors Affecting Absenteeism in Electrical Construction

Construction contractors continue to be faced with the challenge of improving productivity in order to remain successful in an increasingly competitive industry. One factor that contributes to lower productivity is absenteeism. Yet, little is known or understood about the reasons electricians miss work, and very few studies have been conducted on absenteeism in the construction industry. As a positive step to reduce the problems associated with voluntary and involuntary absences, a study was initiated to identify why workers miss work and what steps should be taken to minimize absenteeism. The main objectives of the study were to learn the reasons for absenteeism and to quantify the impacts so that solutions can be developed to help contractors improve their productivity. The study determined that managers and electricians agreed that illnesses and medical appointments were two common reasons workers missed work. However, managers also believed workers were absent because of a lack of interest or irresponsibility, while electricians reported injuries and unsafe working conditions as reasons for missing work. Furthermore, a quantitative analysis of the data revealed that when the absenteeism rate was between 0 and 5%, there was no loss in productivity. However, when the absenteeism rate was between 6 and 10%, a 24.4% loss in productivity was experienced. By understanding what causes electricians to miss work, and the effect of absences on productivity, a company can manage and control absenteeism on electrical construction projects.     

Economic Value of Combined Best Practice Use

This paper measures the value of best practices based on data taken from the Construction Industry Institute Benchmarking and Metrics database. A three-step process provides the basis for measuring the potential benefits of increased best practice use. First, a practice use index is derived to model the way in which best practices are utilized on actual projects. The index combines data from eight best practices to create a single measure of practice utilization. Second, a project performance index, combining cost and schedule metrics, is derived as an indicator of overall project performance. Third, the practice use index is correlated with cost, schedule, and overall project performance metrics. Both owners and contractors benefit from increased practice use. Potential cost savings for owners range from $1.7 to $3.4 million, depending on industry group and project size. Potential cost savings for contractors are higher, averaging $7.2 million for the typical $88 million heavy industrial project. Benefits from schedule reductions are most apparent for owners. On large building projects, schedule reductions for owners average 27 weeks. Finally, improvements in overall project performance on the order of 30% are observed for both owners and contractors.     

Simulation as a Tool for Pile Productivity Assessment

Subsurface obstacles, lack of contractor experience, site planning in addition to other factors complicated the installation process of pile foundation. Productivity and cost estimates for such a process are affected by these difficulties. Therefore this study focuses on the piling process productivity and cost assessment using simulation. Data were collected for this study through designated questionnaires, site interviews, and telephone calls to experts in different construction companies. Many variables have been considered in the piling construction process. Two simulation models have been designed and validated to assess piling process productivity and cost. Consequently, two sets of charts have been developed based upon the validated models to provide the decision-maker with a solid planning, scheduling, and control tool for piling process projects.     

Management of Construction Firm Performance Using Benchmarking

In today’s highly competitive construction industry, there is a critical need for managers to continuously improve their firm’s efficiency and effectiveness. More specifically, managers need to know which performance measures are most critical in determining their firm’s overall success. Benchmarking, when done properly, can accurately identify both successful companies and the underlying reasons for their success. However, rigorous benchmarking within the construction industry still remains an embryonic field. This paper analyzes and critiques both the performance measures and metrics used traditionally in the construction industry and the benchmarking models developed to date for the industry. Based on the results of this analysis, benchmarking models using data envelope analysis are proposed that offer significant improvements over current models. The proposed models measure construction firm performance on a company-wide basis, foster trade-off analyses among various performance metrics, and tie the resources expended by construction firms to how well those firms perform overall. The models also provide managers guidance in determining how specific company resources can be reallocated to improve overall company performance.     

Analysis of Trends in Construction Research: 1985–2002

This paper provides a historical perspective on construction research trends as reflected in the American Society of Civil Engineer’s Journal of Construction Engineering and Management (JCEM). The paper reports the results of an 18-year analysis of JCEM between 1985 and 2002. The analysis focused on such data as author information (affiliations and home countries), as well as the research topics of the technical papers. The paper summarizes the data extracted from the journal and use it to analyze construction research trends. Construction trends reveal that U.S. writers contributed the most papers by that there has been an increasing number of international submissions to the journal. The top research topical areas relate to scheduling, productivity, constructability, simulation, cost control, planning, safety, and computer systems as described in this paper show that research is increasing throughout the world and topics are changing as current needs change. However, there is a need to increase research collaboration between industry and academia, government and academia, and industry and government to advance the construction industry.     

Lean Construction: From Theory to Implementation

This article compares the techniques developed for lean construction with those developed for lean manufacturing. Lean manufacturing and lean construction techniques share many common elements despite the obvious differences in their assembly environments and processes. Manufacturing plants and construction sites are different in many ways that might explain why lean production theories and practices do not fully fit the construction industry. Though many lean construction tools and elements are still in an embryonic state, lean construction techniques are gaining popularity because they can affect the bottom line of projects. Additionally, this paper presents a study of a construction project in which specific lean construction elements were tested. Each technique was evaluated in terms of its impact on the performance of the project. Based on the findings of the study, a new “lean assessment tool” is proposed to quantify the results of lean implementations. The assessment tool evaluates six lean construction elements: last planner, increased visualization, huddle meetings, first-run studies, five S’s, and fail safe for quality. This paper provides a simple and comprehensive approach that is transferable to any construction project.     

Contractor Process Improvement for Enhancing Construction Productivity

This paper presents a regression model that relates job site productivity to process improvement initiatives (PIIs) executed both before and during construction. Applied during early project stages, this model helps industry practitioners to predict the expected value of labor productivity based on certain inputs related to preconstruction planning and construction execution. The model demonstrates the strong relationship of project performance to a variety of PIIs including design completeness, definition of a project vision statement, testing oversight, and project manager experience and dedication. The correlational research methodology targeted 75 projects representing approximately $274.53 million in civil construction. The data collection effort considered 45 PIIs (independent variables) using quantitative and qualitative measures. The modeling technique involved the use of multiple linear regression, a method that exploits available data from multiple, independent sources to focus on specific outcomes. The model was developed directly from contractor specific information and subjected to rigorous statistical analysis. The model provides project managers as front line industry practitioners with a deliberate yet practical approach to project management and productivity enhancement. The modeling results include verification analysis and a discussion of the model’s usefulness and limitations.     

Optimizing Resource Leveling in Construction Projects

Construction schedules, generated by network scheduling techniques, often cause undesirable resource fluctuations that are impractical, inefficient, and costly to implement on construction sites. This paper presents the development of two innovative resource leveling metrics to directly measure and minimize the negative impact of resource fluctuations on construction productivity and cost. The first metric quantifies the total amount of resources that need to be temporarily released during low demand periods and rehired at a later stage during high demand periods. The second measures the total number of idle and nonproductive resource days that are caused by undesirable resource fluctuations. The two new metrics are incorporated in a robust and practical optimization model that is capable of generating optimal and practical schedules that maximize the efficiency of resource utilization. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities. The results of this analysis show that the present model and metrics are capable of outperforming existing metrics and eliminating undesirable resource fluctuations and resource idle time.     

Center for Excellence in Construction Safety

The National Institute for Occupational Safety and Health (NIOSH) has funded the establishment of a Center for Excellence in Construction Safety at West Virginia University. The overall objectives of the Center include: (1) The promotion of hazard control components in engineering curricula; (2) the promotion of hazard awareness and safety‐related knowledge and skills specific to the construction industry; and (3) the promotion of the consideration of safety issues during project design for the purpose of reducing injury during construction. Specific tasks to be accomplished by the Center include: (1) The development of course materials and instruction on construction safety for civil engineering students at the undergraduate and graduate levels; (2) the promotion of such course materials for adoption by other civil engineering academic institutions; (3) the design and conduct of projects related to the improvement of current construction practices that would develop design parameters to reduce trauma during the construction phase; and (4) the establishment of techniques for the collection and dissemination of construction safety information, educational materials, developed guidelines, and design criteria to engineers, architects, contractors, and trade unions.     

Contemporaneous Time Series and Forecasting Methodologies for Predicting Short-Term Productivity

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.     

Quantifying the Impact of Schedule Compression on Labor Productivity for Mechanical and Sheet Metal Contractor

In a typical construction project, a contractor may often find that the time originally allotted to perform the work has been severely reduced. The reduction of time available to complete a project is commonly known throughout the construction industry as schedule compression. Schedule compression negatively impacts labor productivity and consequently becomes a source of dispute between owners and contractors. This paper examines how schedule compression affects construction labor productivity and provides a model quantifying the impact of schedule compression on labor productivity based on data collected from 66 mechanical and 37 sheet metal projects across the United States. The model can be used in a proactive manner to reduce productivity losses by managing the factors affecting productivity under the situation of schedule compression. Another useful application of the model is its use as a litigation avoidance tool after the completion of a project.     

Measuring the Productivity of the Construction Industry in China by Using DEA-Based Malmquist Productivity Indices

Data envelopment analysis (DEA) measures the relative efficiency of decision-making units and avoids any functional specification to express production relationship between inputs and outputs. DEA-based Malmquist productivity index (MPI) measures the productivity change over time. In this paper, the MPI is used to measure the productivity changes of Chinese construction industry from 1997 to 2003. The results of analyses indicate that productivity of the Chinese construction industry experienced a continuous improvement from 1997 to 2003 except for a decline from 2001 to 2002. It is found that there are gaps in productivity development level among western, midland, eastern, and northeastern regions in the Chinese construction industry. The DEA-based MPI approach provides a good tool to support setting up policies and strategic decisions for improving the performance of the Chinese construction industry and promoting the sustainable development of the industry between different regions.     

Design Review Checking System with Corporate Lessons Learned

Design reviews are critical to the success of a construction project. They eliminate costly rework and conflicts, and promote creative and innovative design and construction. This paper discusses a unique way to improve design reviews by gathering and including direct corporate experience that can be used company- and industry-wide. The Design Review Checking System (DrChecks) and the system called Corporate Lessons Learned (CLL) were developed by the U.S. Army Construction Engineering Research Laboratory to collect personal experiences and lessons learned on projects and incorporate these data into corporate knowledge, expressly for the design review process. DrChecks provides a framework for a standardized review process. Typically, exchanges of personal experience and knowledge have occurred informally by word of mouth. With DrChecks and CLL, direct personal experience can be collected into a database while the design review process is on-going. Lessons learned, success stories, and good work practices, which can be identified easily by experienced staff members, can then be shared throughout the organization. DrChecks and CLL both take advantage of the Internet and facilitate the management of design review process and the collection and reuse of corporate lessons learned asynchronously and remotely.