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.     

Process versus Data Oriented Techniques in Pile Construction Productivity Assessment

A large number of problems faces the installation of pile (drilled shaft) foundations: unseen subsurface obstacles, lack of contractor experience, site planning, etc. These problems make it difficult for the estimator to assess the pile construction productivity and cost. Several techniques might be good candidates for this assessment problem. A fundamental question arises: which technique is the most appropriate to solve this assessment problem? This study focuses on answering this fundamental research question. Data were collected through designed questionnaires, site interviews, and telephone calls to experts in different construction companies. Four different techniques were listed as candidates to solve this problem: deterministic, simulation, multiple regression, and artificial neural network (ANN). They were categorized into two groups: process oriented techniques, deterministic and simulation; and data oriented techniques (DOT), regression and ANN. All techniques were used to assess productivity and cost of pile construction. Their results were compared to determine the closest assessment to real world practice. Research results show that the DOT techniques were the most appropriate whereas they had the lowest deviation from real world practice.     

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.     

Using Agent-Based Modeling to Study Construction Labor Productivity as an Emergent Property of Individual and Crew Interactions

Lean construction research has shown that managing work flow effectively and maintaining labor flow on site can improve construction labor performance. Related research also shows that congestion on construction sites often leads to lowered efficiency. Using these findings as a point of departure, we use the agent-based modeling method to represent the construction site as a system of complex interactions and explore whether labor efficiency can be treated as an emergent property resulting from individual and crew interactions in space. This allows us to use a “bottom-up” approach to analyzing labor efficiency, which supplements existing “top-down” approaches to modeling the impacts of space congestion on labor efficiency. A pilot implementation of the agent-based model, and preliminary results illustrating the relationships between congestion and labor efficiency are presented. The empirical studies exhibit system behavior that support published principles of work-force management. The primary contribution of this paper is that it provides a method that can be used to efficiently utilize construction space, and develop plans and schedules that account for congestion arising from crew interactions in space.     

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.     

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.     

Change Orders Impact on Labor Productivity

This paper describes a study conducted to investigate the impact of change orders on construction productivity and introduces a new neural network model for quantifying this impact. The study is based on a comprehensive literature review and a field investigation of projects constructed in Canada and the USA. The field investigation was carried out over a 6-month period and encompassed 33 actual cases of work packages and contracts. Factors contributing to the adverse effects of change orders on labor productivity are identified and a model presented earlier is expanded to account primarily for the timing of change orders, among other factors. The developed model, as well as four models developed by others, have been incorporated in a prototype software system to estimate the loss of labor productivity due to change orders. A numerical example is presented to demonstrate the use of the developed model, and illustrate its capabilities.     

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.     

Productivity Analysis of Construction Operations

An interactive system for analysis of construction operations is proposed. The analysis is carried out in the context of various work modules which address quantity development, resource definition, and production and cost analysis. The quantity work module generates quantities based on information available in the design documents. The resource definition module receives and stores data regarding the labor∕equipment combination to be used to execute work tasks. This module provides the user with a set of standard useful construction process models. For each construction operation to be analyzed, the terminal describes the standard models. The user makes input of a set of parameters for process keyname, quantity, work task durations, number of resources, production capacity of each unit, and cost per hour of each unit to the standard model to be used. Using input from the resource definition module, the productivity and cost analysis module generates production rates and unit costs based on process simulation using CYCLONE methodology.     

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.     

Estimating the Acceptability of New Formwork Systems Using Neural Networks

Continual development in construction techniques results in emergence of specialized formwork systems. A new system will have to compete with in-use systems for adoption in a target operation. Thus, it is essential that decision makers anticipate the acceptability of new systems before making decisions to acquire them. Estimating acceptability basically assesses how features of a new system are comparable to that of in-use systems. Therefore, analogy is a focal factor for the acceptability estimating process. Neural networks (NNs) are more suitable to model construction problems requiring analogy-based solutions. A NN-based approach was employed to anticipate the acceptability of new formwork systems. The study collected data from a group of 40 users in Egypt. A set of six performance characteristics that mostly pertain to acceptability estimating were identified. The study used the analytical hierarchy process to produce pairs of a performance characteristics’ vector and the corresponding acceptability value, and utilized the developed pairs to train NNs. Finally, tests on trained NNs using unseen data indicated satisfactory performance.     

International Labor Productivity Factors

The construction industry is increasingly involved with international projects requiring the services of nonskilled and skilled workers throughout the world. Data concerning the productivity of these workers, therefore, is a critical need for the industry. The purpose of this article is to present national labor productivity factors for 47 various countries in Central and South America, Europe, the Middle East, and Asia. In addition, specific variables affecting national and international productivity are reviewed.     

Quantifying Engineering Project Scope for Productivity Modeling

A poor scope definition in an engineering design project disrupts project rhythm, causes rework, increases project time and cost, and lowers the productivity and morale of the workforce. A quantitative measurement of the project scope is the basis for productivity modeling that involves the measurement, estimation, control, and evaluation of productivity. This paper proposes a conceptual model, the quantitative engineering project scope definition (QEPSD), to standardize the measurement of engineering project scope in construction projects, within a computer aided design environment. The QEPSD quantitatively measures engineering project scope, in terms of the complexity of design items by defining design categories and complexity functions appropriate to the particular discipline. The proposed method was originally verified and implemented specifically for steel drafting projects. Actual data was analyzed and used to demonstrate the benefits of historical data prepared using QEPSD for project scope definition. It was found that the new method led to increased utilization of previously untapped values in historical data, improving the accuracy of project scope definition, and productivity modeling. The paper concludes with a discussion of the potential benefits of adopting the QEPSD method, and its implications upon various project management functions.     

Labor Productivity Drivers and Opportunities in the Construction Industry

The study of labor productivity in the construction industry is gaining increasing attention as the industry faces multiple problems related to its workforce. This paper presents the results of a survey instrument applied to determine the relative level of relevance of construction labor productivity drivers and opportunities. Owners, general contractors, electrical contractors, mechanical contractors, consultants, and others participated in this survey. Management skills and manpower issues were identified as the two areas with the greatest potential to affect productivity according to survey respondents. Surprisingly, external factors, which are often cited as a major cause for reduced productivity in the construction industry, were considered to be one of the least relevant productivity drivers. These results suggest that respondents consider the improvement of labor productivity within their reach and control rather than determined by external conditions.     

Pile Construction Productivity Assessment

Bored piles are vital elements for highway bridge foundation. A large number of factors oversees productivity and cost estimation processes for piles, which creates many problems for the time and cost estimators of such process. Therefore, current study is designed to diagnose these problems and assess productivity, cycle time, and cost for pile construction using the artificial neural network (ANN). 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 to manage the piling construction process. Three-layer, feed forward, and fully connected ANNs were trained with an architecture of seven input neurons, five output neurons, and different hidden layer neurons. The ANN models were validated and proved their robustness in output assessments. Three sets of charts have been developed to assess productivity, cycle time, and cost. This research is relevant to both industry practitioners and researchers. It provides sets of charts for practitioners’ usage to schedule and price out pile construction projects. In addition, it provides researchers with a methodology of applying ANN to pile construction process, its limitation, and future suggestions.     

Critical Investigation into the Applicability of the Learning Curve Theory to Rebar Fixing Labor Productivity

The learning phenomenon has proved applicable in various industries, especially those associated with mass productions such as aircraft and automobile assembly. The learning process in labor-intensive industries is assumed to be more significant in the sense that automated work is constrained by the fact that machines cannot benefit from previous experience and therefore would not “learn” to run any faster or increase the rate of production. Several previous investigations into the applicability of learning curve theory to the construction industry proved the importance of this concept to labor productivity. Nonetheless, a thorough examination of the literature revealed a dearth of research into the effect of learning on the rebar fixing trade. Therefore, the objective of this research is to explore the influence of recurring building floor configurations on rebar fixing labor productivity. To achieve this objective, fixing labor inputs of beams and slabs from 21 multistorey residential building floors were collected and analyzed using the straight-line learning curve model. Fixing labor inputs for each cycle, i.e., floor, and its associated cycle number were modeled using the least-squares method. According to the learning curve theory, labor inputs are expected to decrease by a certain percentage as the cycle number increases. Contrary to the basic principle underlying the theory, the majority of buildings observed exhibited either an increase or a negligible reduction in labor inputs as the cycle number of recurring floors increased.     

Computer Vision-Based Video Interpretation Model for Automated Productivity Analysis of Construction Operations

Videotaping is an effective and inexpensive technique that has long been used in construction to conduct productivity analyzes. However, as schedules of modern construction projects become more and more compressed, the limitation of video-based analysis—intensive manual reviewing process—contrasts sharply with the need for effortless data analysis methods. This paper presents a study on developing a video interpretation model to interpret videos of construction operations automatically into productivity information. More specifically, this research formalizes key concepts and procedures of video interpretation within the construction domain. It focuses on designing a mechanism for furthering the crosstalk between the prior knowledge of construction operations and computer vision techniques. It uses this mechanism to guide the detection and tracking of project resources as well as work state classifications and abnormal production scenario identifications. The resulting approach has the potential to provide a common base for developing automated video interpretation procedures that can greatly improve current data collection and analyzes practices in construction. Experimental results from preliminary studies have shown the potential of the proposed video interpretation method as an improved productivity data analysis method.     

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.     

Impact of Overmanning on Mechanical and Sheet Metal Labor Productivity

This paper details the impacts of overmanning on labor productivity for labor-intensive trades, namely mechanical and sheet metal contractors. Overmanning, as used in the following research, is defined as an increase of the peak number of workers of the same trade over the actual average manpower used throughout the project. The paper begins by reviewing literature on the effects of overmanning on labor productivity. Via a survey to various contractors, data were collected from 54 mechanical and sheet metal projects located across the United States. Various statistical analysis techniques are then performed to determine quantitative relationship between overmanning and labor productivity. These techniques include the stepwise method, T-test, P-value tests, analysis of variance, and multiple regression analysis. The results indicate a 0–41% loss of productivity depending on the level of overmanning and the peak project manpower. Cross-validation is performed to validate the final model. Finally, a case study is provided to demonstrate the application of the model.     

Sensing and Field Data Capture for Construction and Facility Operations

Collection of accurate, complete, and reliable field data is not only essential for active management of construction projects involving various tasks, such as material tracking, progress monitoring, and quality assurance, but also for facility and infrastructure management during the service lives of facilities and infrastructure systems. Limitations of current manual data collection approaches in terms of speed, completeness, and accuracy render these approaches ineffective for decision support in highly dynamic environments, such as construction and facility operations. Hence, a need exists to leverage the advancements in automated field data capture technologies to support decisions during construction and facility operations. These technologies can be used not only for acquiring data about the various operations being carried out at construction and facility sites but also for gathering information about the context surrounding these operations and monitoring the workflow of activities during these operations. With this, it is possible for project and facility managers to better understand the effect of environmental conditions on construction and facility operations and also to identify inefficient processes in these operations. This paper presents an overview of the various applications of automated field data capture technologies in construction and facility fieldwork. These technologies include image capture technologies, such as laser scanners and video cameras; automated identification technologies, such as barcodes and Radio Frequency Identification (RFID) tags; tracking technologies, such as Global Positioning System (GPS) and wireless local area network (LAN); and process monitoring technologies, such as on-board instruments (OBI). The authors observe that although applications exist for capturing construction and facility fieldwork data, these technologies have been underutilized for capturing the context at the fieldwork sites as well as for monitoring the workflow of construction and facility operations.