Partial Factor Productivity and Equipment Technology Change at Activity Level in U.S. Construction Industry

Despite a decrease in industry level measures in construction productivity, there has been a steady increase in construction productivity at the activity level. This research examines equipment technology as one factor that may explain that increase. The relationship between changes in equipment technology and partial factor productivity is examined for 200 activities over a 22 year time period. Specifically, the paper examines the relative impact of different types of equipment technology for five technology factors: energy, control, functional range, information processing, and ergonomics. Through ANOVA and regression analyses, it is found that activities that experienced a significant change in equipment technology also witnessed substantially greater long-term improvements in partial factor productivity than those that did not experience a change.     

Long-Term Impact of Equipment Technology on Labor Productivity in the U.S. Construction Industry at the Activity Level

In general, U.S. industries have witnessed dramatic changes in core processes over the past 25 years. Well understood technological and managerial advances have allowed the manufacturing sector, for example, to steadily increase its productivity and its profit margins. Similar changes are far less well understood in construction. This research examines 200 construction activities for the effect of technology, specifically equipment technology, on their labor productivity from 1976 to 1998. During that time period, changes in equipment technology are measured using a technology index consisting of five technology change factors. Through analysis of variance and regression analyses, it is found that activities experiencing significant changes in equipment technology have witnessed substantially greater long-term improvements in labor productivity than those that have not experienced a change in equipment technology. This research also reveals that changes in (1) energy, (2) control, and (3) functional range are significantly and positively correlated with improvements in labor productivity.     

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.     

Latent Structures of the Factors Affecting Construction Labor Productivity

At any moment in time, a multitude of factors simultaneously impact construction productivity. Utilizing the knowledge of thousands of construction craft workers, the writers quantitatively analyzed the underlying structure of the factors affecting construction productivity and identified which factors the craft workers consider to be more relatively important as well. This research identified 83 factors affecting construction labor productivity through 18 focus groups with craft workers and their immediate supervisors on nine jobsites throughout the U.S. Next, a nationwide survey was administered to 1,996 craft workers to assess the impact of these factors on construction labor productivity. Principal factor analyses identified 10 latent factors to represent the underlying structure of 83 productivity factors. In addition, the relative importance of the factors’ impact on construction productivity was examined based on the crafts’ union status, trade, and position (craft worker versus foreman). The writers also compared their results to similar previous efforts, and more importantly, identified significant differences that may impact future productivity improvement strategies. This research will help industry and the research community better understand the factors affecting construction labor productivity and more effectively direct future efforts to improve its performance.     

Comparative Study of Activity-Based Construction Labor Productivity in the United States and China

This research compares construction labor productivity (CLP) of the United States with its Chinese counterpart at the activity level to evaluate productivity differences between the two countries from an operational perspective. Supplementing other comparative construction studies measuring productivity by output value per person, this research examined CLP—measured by physical quantity installed per labor hour—based upon published national average productivity data. Sampled activities included earthwork, concrete, masonry, structural steel, waterproofing, and interior finishes. Paired comparisons (United States–China) of these selected activities were then analyzed and evaluated. The source of the U.S. labor productivity data was RSMeans Building Construction Cost Data, which was cross validated by data from the Walker’s Building Estimator’s Reference Book. The source of Chinese labor productivity data was mainly the Beijing construction quota, which was cross validated by Chinese quotas from several other cities and provinces in China. In terms of hourly output, significant differences were observed in many operational categories. To test the hypothesis that the labor-equipment compositions of the Chinese construction crews contributed to the labor-productivity gaps, a labor intensive factor (LIF) was introduced to measure the intensity of labor usage in a construction activity. Statistical analysis indicated that modest to strong correlations exist between the productivity differences and LIFs of the sampled activities. Chinese CLP significantly lags behind its U.S. counterpart in equipment-intensive construction activities. Smaller CLP gaps or comparable CLPs between the two countries were observed for labor-intensive activities. According to these findings, construction equipment efficiency appears to be a major factor contributing to the productivity difference between the two countries.     

Impact of Shift Work on Labor Productivity for Labor Intensive Contractor

Generally, a contractor has three options in accelerating a construction schedule: working longer hours, increasing the number of workers, or creating an additional shift of workers. There has been a significant amount of research conducted on scheduled overtime on construction labor productivity. However, little information has been found in the literature addressing the labor inefficiency associated with working a second shift. This paper has qualitative and quantitative components. The qualitative part details why and how shift work affects labor productivity, and then addresses the appropriate use of shift work. The quantitative component determines the relationship between the length of shift work and labor efficiency. The results of the research show that shift work has the potential to be both beneficial and detrimental to the productivity of construction labor. Small amounts of well-organized shift work can serve as a very effective response to schedule compression. The productivity loss, obtained from the quantification model developed through this study, ranges from ?11 to 17% depending on the amount of shift work used.     

Work Flow Variation and Labor Productivity: Case Study

Different types of flow variation and how they affect construction project performance have been studied by previous researchers. One aspect that has not been well researched is how work flow variation and labor productivity are related in construction practice. To study that issue, 134?weeks of project production data were collected and analyzed to explore this relationship. Labor productivity was found to be positively correlated with Percent Plan Complete (PPC), a measure of work flow variation. The relationship between productivity and the ratio of total task completion to planned tasks, weekly workload, weekly work output, and weekly work hours was also studied, and no significant correlation was found. The results suggest that productivity is not improved by completing as many tasks as possible regardless of the plan, nor from increasing workload, work output, or the number of work hours expended. In contrast, productivity does improve when work flow is made more predictable. These findings can help project managers focus on actual drivers of productivity. It can also help consulting companies pinpoint responsibility for productivity losses in claims.     

Impact of Change’s Timing on Labor Productivity

There are many types of construction changes and each type can have an effect on labor productivity. To a certain extent though the specific type of change is not as important as the mere presence of the change and, as analyzed in this paper, the timing of that change. The research reported in this paper reaffirms that project change is disruptive and detrimental to labor productivity. Data from 162 construction projects were statistically analyzed and a series of three curves are presented in this paper, representing the impact that change has on the labor productivity for early, normal, and late timing situations. The projects are a representative sample of the industry, involving a wide range of sizes, different delivery systems, and industry sectors. Late change is more disruptive of project productivity than early change, all other things being equal. The implications and benefits of this research are clear: if changes are necessary, they should be recognized and incorporated as early as possible. Practitioners can use these data and curves for either forward pricing or retrospective pricing of changes. Other researchers can use these findings to test their own findings and to explore timing issues in further detail.     

Improved Baseline Productivity Analysis Technique

Previous studies have aimed to develop effective methods to derive baseline productivity (BP) for labor-intensive activities in construction sites. However, there are two different definitions of BPs: one is defined as a performance benchmark of best practice and the other as a standard reflecting a contractor’s normal operating performance. It is necessary to clarify the difference between the two definitions and their corresponding BPs. This research introduces data envelopment analysis (DEA) as a new method for deriving BP and compares DEA with the other four BP deriving methods. DEA is concluded as the best method in terms of objectivity, effectiveness, and consistency to find BP that represents the best performance a contractor can possibly achieve. With the capability of deriving productivities of multi-input and multi-output activities, the proposed DEA has raised the scale of labor productivity from the level of single factor productivity to total factor productivity which will help construction researchers and managers to evaluate performances of interests in a much more effective way.     

Effect of Safety and Environmental Variables on Task Durations in Steel Erection

In spite of the efforts by government agencies, labor organizations, and researchers in the field of health and safety, injuries and fatalities continue to affect the construction industry. In 2002 the construction industry had the undesirable distinction of having two of the most dangerous occupations in the United States, with fatalities among structural steel workers at 58.2 per 100,000 workers (fourth highest rate) and among construction laborers at 27.7 per 100,000 workers (ninth highest rate). Costs associated with construction accidents, such as increased insurance premiums and medical expenses, and loss of productivity are also concerns in the industry. It has not been demonstrated how unsafe working conditions affect worker performance, and the impact of unsafe work practices on worker performance has not been quantified. This paper describes a methodology that included direct observation of steel erection activities and statistical analysis of task duration data. The data collected at steel erection sites included safety conditions such as the use of personal protective equipment (PPE), elevation of the work area, environmental conditions such as temperature and humidity, and worker performance in the form of task durations. Analysis of variance (ANOVA) analysis of 186 of steel erection task durations collected over a six-month period showed that the use of personal protective equipment (PPE), the time of day during which the operation was being performed, the elevation at which the work was being performed, and the presence of decking below the work area had statistically significant effects on the durations of steel erection tasks.     

FMS Approach to Construction Automation

The need to increase safety, productivity, quality, and resource efficiency in construction is a challenge to researchers that requires bold innovative changes. Technological adaptation of successful concepts from other industries to construction offers an important path for innovative progress. This paper provides insight into some central aspects of flexible manufacturing systems (FMS) that show the greatest transferability to construction. A flexible manufacturing system is a production facility consisting of computer‐controlled machines, or work stations, connected by an automated material‐handling system, which is used to produce a variety of parts in low and medium volume. The state of the art in automated material handling and process planning is presented as a basis for construction automation, a new field of civil engineering. Ongoing research in flexible construction systems is described, and directions for future research needed to establish a knowledge base of principles relating to construction automation are outlined.     

Reducing Variability to Improve Performance as a Lean Construction Principle

Variability is common on construction projects and must be managed effectively. New management thinking, like that of lean production, has suggested that better labor and cost performance can be achieved by reducing output variability. Efforts to utilize lean thinking in construction, so far, have generated limited evidence to support this claim. This paper investigates the relationship between variability and project performance to test the notion that reducing output variability will result in improved labor performance. Using productivity data from concrete formwork activities on multiple projects, various measures of output variability are tested against construction performance. It is shown that variability in output is inevitable and that there is little correlation between output variability and project performance, but that variability in labor productivity is closely correlated to project performance. It is concluded that lean improvement initiatives should be redirected to adaptable workforce management capabilities to reduce variability in labor productivity instead of output in order to improve project performance.     

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.     

Predicting Industrial Construction Labor Productivity Using Fuzzy Expert Systems

The objective of this technical note is to illustrate the application of fuzzy expert systems to the modeling of a practical problem—that of predicting the labor productivity of two common industrial construction activities: rigging pipe and welding pipe. This note illustrates how to develop and test such a model, given the realistic constraints of subjective assessments, multiple contributing factors, and limitations on data sets. The factors that affect the productivity of each activity are identified, and fuzzy membership functions and expert rules are developed. The models are validated using data collected from an actual construction project. The resulting models are found to have high linguistic prediction accuracies. This note is of relevance to researchers by demonstrating how a fuzzy expert system can be developed and tested. It is of relevance to industry practitioners by illustrating how fuzzy logic and expert systems modeling can be exploited to help them solve real world problems.     

Causes of Variation in Construction Project Task Starting Times and Duration

In this research, variation is defined as the time difference between what was planned and what actually happened in terms of task starting times and duration. Variation in construction tasks is important as it can impact productivity performance. Construction projects consist of a large number of interdependent tasks. When the starting time and/or duration of one task varies, it can affect other downstream tasks and result in disruptions to the schedule and/or decreased productivity. The construction process is complex and involves numerous people with different levels of responsibility, which makes identifying the root causes of the variation difficult. A nationwide survey was administered to workers, foremen, and project managers to identify the most prevalent causes and magnitude of both starting time and task duration variation. Fifty individual causes of variation were divided into eight categories: prerequisite work, detailed design/working method, labor force, tools and equipment, material and components, work/job site conditions, management/supervision/information flow, and weather or external conditions. This research examined the similarities and differences in perceptions between craft workers, foremen, and project managers in terms of starting time and task duration variation. The top eight causes of starting time variation and top nine causes of task duration variation were identified. The research also quantitatively analyzed the underlying structure of the causes of variation using factor analysis. This was done by grouping the 50 individual causes into nine orthogonal factors that represent the underlying structure of the affecting causes. The findings will help construction project managers and field managers focus on the root causes of variation during planning in order to develop effective strategies to reduce variation and improve project productivity performance.     

Productivity Improvement: The Influence of Labor

Labor has a significant influence on construction productivity. The level of productivity is a result of the driving, induced, and restraining forces acting upon workers. These forces act positively and negatively with regard to productivity improvement. A framework for analyzing fhe influence of each of these forces on four major labor related determinants of construction productivity is presented. Approaches to productivity improvement are analyzed in terms of reducing the negative forces and strengthening the positive forces.     

Field Studies in Construction Equipment Economics and Productivity

Earthmoving and construction equipment have evolved significantly during the past century. During the same time, our ability to study and understand equipment economics and productivity has also gained significant ground. This paper relates some of the recent academic research to industry practices. In doing so, it validates some parts of the research and makes new observations in three areas: repair costs, residual values, and total cost of ownership (TCO) and productivity. It also provides a few pointers for future research.     

Analysis of Factors Influencing Productivity Using Craftsmen Questionnaires: Case Study in a Chilean Construction Company

Improvement of productivity in construction has been a major industry challenge, given its high impact on project results. It has received increased attention from construction researchers promoting different enhancement actions, since analyzing factors affecting labor productivity is an instrumental part in this process. This paper focuses on identifying and understanding the productivity factors affecting projects in a Chilean construction company on the basis of questionnaires administered to both direct workers and midlevel employees. Analysis of the questionnaire results helped to determine organizational and managerial weaknesses and facilitated comparison of the findings with previous productivity studies. The results proved to be useful in developing recommendations for productivity improvements. The main findings indicate that the critical areas affecting construction productivity were related to materials, tools, rework, equipment, truck availability, and the workers’ motivational dynamics. These results are similar to those obtained in previous studies in the United States and in Chile. Salary expectations were found to be the main reason for turnover in the studied company, which was an aspect not mentioned in previous studies. Finally, additional analyses seem to show that some factors affecting productivity are common to construction projects across boundaries, therefore validating data aggregation and the possibility of learning from experiences in different locations and even separated in time of occurrence.     

Impact of Extended Overtime on Construction Labor Productivity

This paper presents an analysis of the impacts of extended duration overtime on construction labor productivity. The results show a decrease in productivity as the number of hours worked per week increase and/or as project duration increases. The research focuses on labor intensive trades such as the electrical and mechanical trades. Overtime in this research is defined as the hours worked beyond the typical 40 h scheduled per week. The paper begins by presenting the effects of overtime and the need for an updated overtime productivity model. Data for the quantitative analysis was collected from 88 projects located across the United States by means of a questionnaire. Various statistical analysis techniques were performed to develop quantitative relationship curves, including multiple regression, P-value tests, and analysis of variance.     

Work Sampling Can Predict Unit Rate Productivity

Various methods are employed to measure labor utilization during power plant construction, one of which is work sampling. Work sampling attempts to evaluate how the work force spends its time at work. This provides timely information to management in order to determine whether corrective action or detailed study is needed to achieve a higher degree of efficiency. However, the effectiveness of work sampling in demonstrating true labor performance has not been statistically verified using data collected at construction sites. This study collects 45 work sampling data points from 11 nuclear power projects and 4 fossil fuel power projects. the relationship between work sampling and productivity has been strongly supported by this statistical analysis. This study also verifies that work sampling is a good labor productivity indicator as well as a useful predictor in a productivity projection model.