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 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.     

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.     

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.     

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.     

Method for Calculating Schedule Delay Considering Lost Productivity

A delay claim often occurs when a difference between the actual completion date and the contract completion date exists. The duration of a delay is an essential piece of information required for determining the cause of a delay. However, it is difficult to analyze a delay claim due to the fact that numerous factors that cause this delay, thereby making it a very complex issue. One of such factors is the lost productivity or loss of productivity. Despite the fact that it is one of the major causes of delay, there have been only a few studies that focus on converting lost productivity into delay duration carried out to date. Claims for productivity losses are generally the result of tension between the contractor and the owner. This tension arises due to the great difficulty involved in quantifying disruption effects. Thus, to calculate accurately the delay duration, a logical method for analyzing schedule delay caused by lost productivity is necessary. Therefore, in this study, we propose a method for analyzing construction schedule delay where this lost productivity is taken into consideration. This methodology was implemented on a case project to ascertain its practicability, and to decide whether it can be utilized in the case of a delay claim related to lost productivity. The significance of this paper is twofold. One is the method to convert the lost productivity into the delay duration, which can be applied to reasonable delay claim settlement. The other is the process to analyze the construction schedule delay considering lost productivity.     

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.     

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.     

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.     

Role of the Fabricator in Labor Productivity

This paper describes the results of three case studies in which the subcontractor-fabricator relations had a significant negative effect on labor productivity of the subcontractor. Late vendor deliveries, fabrication or construction errors, and out-of-sequence deliveries plagued each of the three projects. On two projects, there were work stoppages because there were no materials. Unsequenced deliveries caused unnecessary crane movements and rework. On the third project, the output of the fabrication shop was not compatible with the output of the erection crew. The baseline productivity was calculated, and the loss of labor efficiency in each case was estimated to be 16.6, 28.4, and 56.8%. These percentages were compared with similar percentages calculated from other published articles that described inefficient site storage conditions and delivery methods. The labor inefficiencies caused by material management range from a low of 5.4% to a high of 56.8%. The schedule slippage on the three case study projects was estimated to be between 50 and 129%.     

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.     

Productivity Scheduling Method: Linear Schedule Analysis with Singularity Functions

This paper describes a new integrated method of linear schedule analysis using singularity functions. These functions have previously been used for structural analysis and are newly applied to scheduling. Linear schedules combine information on time and amount of work for each activity. A general model is presented with which activities and their buffers can be mathematically described in detail. The algorithm of the new method forms the body of the paper, including the steps of setting up initial equations, calculating pairwise differences between them, differentiating these to obtain the location of any minima, and deriving the final equations. The algorithm consolidates the linear schedule under consideration of all constraints and, thus, automatically generates the minimum overall project duration. The model distinguishes time and amount buffers, which bears implications for the definition and derivation of the critical path. Future research work will address float and resource analysis using the new model.     

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.     

Symbiotic Crew Relationships and Labor Flow

This paper introduces a concept heretofore unrecognized in multiple crew relationships: symbiotic crew relationships. A symbiotic relationship occurs when the work pace of one crew depends on the pace of a preceding crew. Data from steel reinforcement activities from six commercial and residential projects in Brazil are used to demonstrate the negative effects of symbiotic relationships. The steel reinforcement on the case study projects was cut and bent on site. In this context, the pace of the installation crew’s work (and hence performance) was dictated by the pace of the cutting and bending crew. The performance of crews with symbiotic relationships is shown to be consistently worse than when symbiotic relationships are not present. Symbiotic relationships are also tied to time buffers. There is better performance as the time buffer between crews approaches 5?days.     

U.S. Construction Labor Productivity Trends, 1970–1998

Construction productivity trends carry immense consequences for the economy as a whole. However, there is little scholarly consensus concerning even the direction of such trends. The main objectives of this paper are to (1) present an approach to studying long-term productivity trends in the U.S. construction industry; and (2) provide a preliminary indication of such trends over the past 25–30 years. Subsequent, extended statistical studies are suggested that may be based on the approach of the selected work presented here. Labor cost and output productivity trends are tracked for tasks that represent different trades and differing levels of technological intensity within the building construction sector. Specific tasks dealt with a range from a zero technology impact task, such as hand trenching, to compaction with a sheepsfoot roller. Means's cost manuals were used to trace the benchmark values for these tasks. These values reflect productivity trends. Unit labor costs in constant dollars and daily output factors were compared over decades for each task. Direct work rate data from 72 projects in Austin, Tex., over the last 25 years were also examined. Increasing the direct work rate usually increases construction productivity. The combined data indicate that productivity has increased in the 1980s and 1990s. Depressed real wages and technological advances appear to be the two biggest reasons for this increase. The data also indicate that management practices were not a leading contributor to construction productivity changes over time. Subsequent studies are required to add weight to these observations and can be based on the approach presented here.     

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.     

Estimating Labor Productivity Using Probability Inference Neural Network

This paper discusses the derivation of a probabilistic neural network classification model and its application in the construction industry. The probability inference neural network (PINN) model is based on the same concepts as those of the learning vector quantization method combined with a probabilistic approach. The classification and prediction networks are combined in an integrated network, which required the development of a different training and recall algorithm. The topology and algorithm of the developed model was presented and explained in detail. Portable computer software was developed to implement the training, testing, and recall for PINN. The PINN was tested on real historical productivity data at a local construction company and compared to the classic feedforward back-propagation neural network model. This showed marked improvement in performance and accuracy. In addition, the effectiveness of PINN for estimating labor production rates in the context of the application domain was validated through sensitivity analysis.