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.     

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.     

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.     

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.     

Union and Open Shop Labor Cost Differences

One area within the construction industry that has dramatically changed is the growth of open shop construction. This growth suggests that open shop general contractors may have a competitive advantage over union general contractors. This paper investigates productivity and wage rate variances between Colorado open shop and union general contractors on projects completed since January 1981. Though there is little productivity variance, a significant variance of wage rates indicate the Colorado open shop general contractor presently has a clear overall labor advantage. The productivity and wage rate variance of 29 individual project job tasks were investigated. Data was collected from 35 union and 20 open shop contractors' project labor costs reports. Additional information was collected during interviews with representatives of eleven general contractors. The productivity and wage rate variances are described by individual job tasks; by the union craft jurisdictions of laborers, carpenters, ironworkers, and cement finishers; and by an overall comparison between open shop and union general contractors.     

Measuring and Modeling Labor Productivity Using Historical Data

Labor productivity is a fundamental piece of information for estimating and scheduling a construction project. The current practice of labor productivity estimation relies primarily on either published productivity data or an individual’s experience. There is a lack of a systematic approach to measuring and estimating labor productivity. Although historical project data hold important predictive productivity information, the lack of a consistent productivity measurement system and the low quality of historical data may prevent a meaningful analysis of labor productivity. In response to these problems, this paper presents an approach to measuring productivity, collecting historical data, and developing productivity models using historical data. This methodology is applied to model steel drafting and fabrication productivities. First, a consistent labor productivity measurement system was defined for steel drafting and shop fabrication activities. Second, a data acquisition system was developed to collect labor productivity data from past and current projects. Finally, the collected productivity data were used to develop labor productivity models using such techniques as artificial neural network and discrete-event simulation. These productivity models were developed and validated using actual data collected from a steel fabrication company.     

Role of Inventory Buffers in Construction Labor Performance

Buffers of material stockpile (inventory) are formed at the work level in construction to help manage production. The size of material stockpiles often has an important bearing on construction project performance. In construction projects, where conditions are often uncertain and variable, some have suggested that buffers be sized and located according to the conditions. New management thinking like lean construction and theory of constraints suggests that the size of buffers needs to be managed carefully, because when oversized, buffers are wasteful, impede workflow, and hinder performance. Research has shown that project variability can be affected with the careful deployment of buffers but has not really evaluated the impact on construction labor performance. This paper reports an exploratory analysis of the relationship between inventory (buffers) and construction labor performance with data collected from three commercial projects in Brazil. In this study, the size of the buffer between rebar fabrication and installation in the construction of a structural system is compared to the labor performance of the fabrication and installation crews. The results show that some buffer helps achieve the best labor performance in the construction operations studied.     

Benchmarking Productivity Indicators for Electrical/Mechanical Projects

Labor-intensive industries such as the electrical and mechanical trades are considered high risk due to the high percentage of labor costs. Because of this high risk, it is important for contractors in these industries to closely track labor costs on projects and compare these costs to industry benchmarks. In this paper, benchmark indicators for these industries are established on the basis of actual project data. These benchmarks include the relationship between the percent complete or percent time and cumulative work hours or cost, project size and duration, project size and average man power, project size and peak man power, and average versus peak man power. These relationships were developed using regression analysis. Man power loading charts and the related S-curves were developed from actual project data. The man power loading charts and the related S-curves are useful for resource planning and for tracking progress on a construction project. They can be used to show the cause-and-effect relationship between projects impacted by outside factors and normal labor productivity.     

Role of Workforce Management in Bridge Superstructure Labor Productivity

There have been many studies on different aspects of the construction process in regard to how they each impact construction productivity. In reviewing the documentation of this research, very few articles were located that dealt with heavy/highway construction in general, and even fewer were found that dealt with bridges in particular. In addition, very little was found in the literature dealing with the effect that the quality of workforce management has on construction productivity. This paper describes the results of four case studies of highway bridge construction performed by established contractors with little bridge building experience, in which workforce management had a significant negative effect on labor productivity. The contractors’ lack of experience in bridge construction seemed to be the cause of several problems that plagued each of the four projects. The baseline productivity of each project was calculated, and the loss of labor efficiency was estimated to be 80, 75, 32, and 70%, respectively. The schedule slippage on the four case study projects was estimated to be between 127 and 329%.     

Firm Performance and Information Technology Utilization in the Construction Industry

This paper, which is written to both researchers and practitioners, examines the impact of information technology (IT) on construction firm performance. Based on data collected from 74 construction firms, regression analysis is used to test the relationship between performance and IT. Analysis provides empirical evidence that IT is positively associated with firm performance, schedule performance, and cost performance. Firm performance is a composite score of several metrics of performance: schedule performance, cost performance, customer satisfaction, safety performance, and profit. The regression analysis shows that for every 1?unit increase in IT utilization, there is an increase of about 2, 5, and 3% in firm performance, schedule performance, and cost performance, respectively. No relationship is found between IT use and customer satisfaction, safety performance, and profitability.     

Information Technology Planning Framework for Japanese General Contractors

Developments in information technology (IT) continue to have a significant impact on the architectural/engineering/construction (A/E/C) industry. However, the issue of whether A/E/C organizations are receiving adequate returns from their IT investments remains an important managerial concern. Recently, Pe?a-Mora, Vadhavkar, Perkins, and Weber introduced a strategic IT planning framework for construction projects that includes four steps: environmental scan, internal scrutiny, IT diffusion analysis, and IT investment modeling. It analyzes the external and internal IT conditions of a project/firm, identifies the diffusion phase of IT in the project/firm, and finally evaluates the effects of IT investments on the project/firm. Although this framework was originally intended to plan future IT investments, we assume that the framework can also be used to evaluate ongoing or past IT investments in projects/firms. Following this idea, this paper presents an application of the Pe?a-Mora et al. framework to an ongoing IT project in Kajima Corporation, one of the largest Japanese general contractors. Kajima is currently developing an IT system called LINCS (Linkage of Information for a New Construction System). This synthetic information network system has been designed to share building data among design and construction stages and streamline design/build activities. By using the framework proposed in this paper, we analyzed how LINCS is justified in terms of its strategic and operational value for Kajima and its construction projects.     

Construction Baseline Productivity: Theory and Practice

In this paper, the theoretical basis for construction labor productivity measurement is presented. In particular, the theoretical basis for baseline productivity measurements is developed by examining a productivity database consisting of 23 projects involving masonry construction. An important hypothesis is presented showing that as the design becomes more complex, the baseline productivity worsens. It is also hypothesized that higher values of the coefficient of variation indicates a higher variability in management and craft skills and in the use of technology. Two measures are proposed to measure the performance of individual projects: The disruption index and the project management index. These two measures identify the best and worst performing projects. Cumulative probability distributions of the disruption index and the project management index were also developed to evaluate the 23-project database and compare it with other databases. The hypotheses developed from the 23-masonry project database were tested against an 8-project database of concrete formwork and a 12-project database of structural steel erection. Strong support for each hypothesis was found using the two additional databases.     

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.     

Empirical Analysis of Construction Enterprise Information Systems: Assessing System Integration, Critical Factors, and Benefits

Attaining higher levels of system integration is seen as the primary goal of construction enterprise information systems (CEIS). Increased system integration resulting from CEIS implementation is expected to lead to numerous benefits. These benefits encompass information technology infrastructure and strategic, operational, organizational, and managerial aspects of the firm. By adopting CEIS, firms seek tangible and intangible benefits, such as cost reduction, improved productivity, enhanced efficiency, and business growth. Through the use of statistical analysis, this study quantifies the critical success factors that impact CEIS integration and the ensuing benefits. Furthermore, it analyzes the effects of system integration on CEIS induced benefits. It also investigates the impact of CEIS strategy on CEIS induced benefits and identifies the relationship between CEIS strategy and system integration. Finally, it assesses the effects of CEIS induced benefits on user satisfaction and provides a CEIS implementation guide map for construction firms.     

Site Management of Work-in-Process Buffers to Enhance Project Performance Using the Reliable Commitment Model: Case Study

Buffers have been commonly used as a production strategy to protect construction processes from the negative impact of variability. Construction practitioners and researchers have proposed several buffering approaches for different production situations and contexts. However, these solutions have been impractical for managing buffers. To overcome this, this study proposes a new site methodology for managing work-in-process (WIP) buffer in repetitive projects, on the basis of the reliable commitment model (RCM). RCM is a decision-making tool based on lean principles, which uses statistical models to develop more reliable work plans at the operational level. RCM helps to manage WIP buffer in work plans by using site information and planning reliability indicators that result in improved project performance, such as labor productivity and process progress. A repetitive building project was used as a case study. The main finding was that labor productivity, process progress, and waiting times improved when using larger WIP buffers than those typically used among crews. This shows the potential of RCM as a practical tool to manage WIP buffer sizes and to promote the use of lean production strategies at the operational level.     

System Model for Analyzing Design Productivity

Many engineering design companies collect data such as person hours to manage projects. But the relationships between operational variables and performance are usually not thoroughly analyzed and interpreted. This paper proposes a system model and procedure to relate influence variables to project productivity. The model was tested by analyzing 190 projects of an engineering consulting company. The relationships between design productivity and various input and process variables were identified and interpreted. For example, project size has a negative relationship with productivity, while the effect of quality assurance/quality control on productivity is not clear. Based on documented data and derived information, this model can help companies gain operational insight and thus improve productivity and profitability.     

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.     

Improving Labor Flow Reliability for Better Productivity as Lean Construction Principle

New management thinking, like that of lean production, has suggested that better labor and cost performance can be achieved by improving the reliability of flows. In this context, lean thinking portrays reliable flows as the timely availability of resources, i.e., materials, information, and equipment. Little attention has been given to labor as a flow. Further, little discussion can be found related to flexible capacity management strategies. Efforts to utilize lean thinking in construction, so far, have generated limited evidence to support the need for more reliable labor flows. This paper investigates the lean principle that more reliable flows lead to better labor performance. Actual data from three bridge construction projects are examined to document the instances of poor flow (resource) reliability and its effect on labor performance. Inefficient labor hours are calculated. The results show that there is strong support that more reliable material, information, and equipment availability contributes to better performance. However, the projects showed considerable deficiencies in the utilization of the labor resource. It is concluded that lean improvement initiatives should focus more on workforce management strategies to improve labor utilization that will lead to better labor performance.     

Developing ERP Systems Success Model for the Construction Industry

Recently, a significant number of major construction companies embarked on the implementation of integrated information technology solutions such as enterprise resource planning (ERP) systems to better integrate various business functions. However, these integrated systems in the construction sector present a set of unique challenges, different from those in the manufacturing or other service sectors. There have been many cases of failure in implementing ERP systems in the past, so it is critical to identify and understand the factors that largely determine the success or failure of ERP implementation in the construction industry. This paper presents the process of developing an ERP systems success model to guide a successful ERP implementation project and to identify success factors for ERP systems implementation. The paper identifies factors associated with the success and failure of ERP systems, and develops a success model to analyze the relationships between key factors and the success of such systems. The proposed ERP systems success model adapts the technology acceptance model and DeLone and McLean’s information systems success model and integrates those with key project management principles. The goal of the ERP systems success model is to better evaluate, plan, and implement ERP projects and help senior managers make better decisions when considering ERP systems in their organization.     

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.