Improved Measured Mile Analysis Technique

Quantifying lost labor productivity on construction projects is difficult and sometimes subjective. A widely accepted way to quantify losses is the “measured mile” approach. It compares periods of a project that have been impacted by change to those that have not been impacted. As currently practiced the measured mile relies on subjectively identifying that reference period. In this paper the measured mile and a variant, the baseline method, are analyzed and compared to a new, proposed statistical clustering method. This new approach is advocated because it determines its reference period using objective criteria. A case study is included to show how the three methods work, and advantages and disadvantages of each method are presented in this paper.     

Case Law and Variations in Cumulative Impact Productivity Claims

Proving and quantifying lost productivity due to cumulative impacts of multiple changes are difficult tasks. This paper presents the most acceptable methods from case law and demonstrates their applications for analyzing the loss of productivity. These methods include earned value analysis, measured mile analysis, and combinations of these two. They are either well established or drawn from recent court and board decisions. A case study is used to illustrate and compare the use of these methods. These methods result in considerably different loss of productivity values though the actual amount (i.e., inefficiency in labor hours) is unique for a particular case and though these methods are often thought to be similar or even the same. How a measured mile analysis and its variants are employed affects the amount of lost productivity estimated. The variants can avoid some drawbacks of measured mile and earned value studies. Nevertheless, which method is more accurate and reliable is difficult to provide for a particular claim. Practitioners should choose between them based on the availability of project records and the nature of changes and cumulative impacts. Practitioners may also employ two or more methods to perform a “sensitivity analysis” of the chosen methods and persuade the other party and/or the jury that their estimate of lost productivity is sufficiently certain.     

Quantification of Losses of Labor Efficiencies: Innovations in and Improvements to the Measured Mile

This paper describes a rigorous procedure for quantifying damages arising from loss of labor inefficiency. Throughout, deficiencies with the measured mile concept are cited. Perhaps one of the least recognized deficiencies is that the measured mile is a concept, not a procedure. Thus, every analyst is left to conduct the study as he/she pleases. This paper defines a step-by-step process for doing a damage study, and cites rules to follow when performing the study. Following the procedure and rules will lead to more rigorous and defensible analyses. A baseline analysis is described and presented as a way of adhering to the measured mile concept. The principle difference is in how best performance is defined. In a baseline study, the focus is on periods of high output (production), not good productivity. The baseline periods need not be continuous. Also, the analysis is performed on combined data (one account), not many accounts. The calculations used to perform a baseline study are illustrated using a simple case study project. Reasons for recovering damages because of losses of labor efficiency are also cited.     

Reconciliation of Owner and Contractor Views in Heavy Construction Projects

Delay and loss of productivity are the two main types of damage experienced by the contractor when the owner issues a change order. Courts have recognized critical path method schedule analysis as the preferred method of identifying and quantifying critical delays. As for the inefficiency damages, there is no direct way of measuring inefficiency due to its qualitative nature and the difficulty of linking the cause of the productivity loss to the damage. Most of the scholarly work published in this area was based on data supplied by the contractors; and that explains why there are discrepancies between what the contractor asks for and what the owner believes the contractor is entitled to. This study addresses the need for a statistical model to quantify the productivity loss from verifiable site data such as owner’s daily reports, change orders, drawings, and specifications, rather than rely solely on contractor surveys. A model is developed and validated to quantify the productivity loss in pipe work in roadway projects due to the change orders. The productivity loss study analyzed two sets of data that include: (1) variables that predict which of the two parties, the owner and the contractor, contributed to the productivity loss; and (2) variables that predict, from the legal viewpoint, productivity losses which only the owner is responsible for. The study showed the difference between what the contractor asked for and what he/she is actually entitled to. This model can be used by both the owner and the contractor to quantify the productivity loss due to change orders, and to offer an objective approach to reconcile their differences. This study concludes with an example to demonstrate the use of the model.     

Statistical-Fuzzy Approach to Quantify Cumulative Impact of Change Orders

This paper presents a hybrid approach to quantify the impact of change orders on construction projects using statistical regression and fuzzy logic. There are many qualitative variables affecting the impact of change orders on labor productivity; statistical analysis falls short of addressing the fuzziness of those variables. Because of their complementary nature, fuzzy logic and regression analysis can be integrated; regression analysis is used to determine the membership functions of the input linguistic values. In this paper, each input variable is statistically treated before entering a general rule relating its space to the space of loss in labor productivity. The relative weight of each input variable is determined by its coefficient of determination (R2) value. The expected loss of labor productivity and its standard deviation are then determined from the output fuzzy membership function. The proposed methodology is general and can be applied in areas of system analysis and decision making when a complex input-output function is to be predicted in the presence of some fuzzy knowledge and a large number of real input-output data.     

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.     

Delay Analysis Method Using Delay Section

The most common cause of construction claims is delay. Moreover, delay claims are often extremely complex and difficult to resolve. For this reason, the construction industry requires an effective and reliable method for analyzing the causes and effects of construction delay. Presently, the methods of analysis in common use do not adequately account for several commonly encountered situations. As a result, project time extensions are often considered without rigorous analysis. Therefore the objective of this study is to propose and describe an effective and logical method for evaluating construction delays that adequately accounts for commonly encountered situations. To achieve this objective, the writers propose a new methodology called “delay analysis method using delay section” (DAMUDS) as a means of overcoming two limitations of existing methods: (1) inadequate accounting of concurrent delay and (2) inadequate accounting of time-shortened activities. The DAMUDS method builds upon the widely used method of contemporaneous period analysis. The writers’ points are illustrated through the use of an example case.     

Decision Tree Approach to Classify and Quantify Cumulative Impact of Change Orders on Productivity

Multiple or unusual change orders often cause productivity losses through a “ripple effect” or “cumulative impact” of changes. Many courts and administrative boards recognize that there is cumulative impact above and beyond the change itself. However, determination of the impact and its cost is difficult due to the interconnected nature of construction work and the difficulty in isolating causal factors and their effects. As a result, it is very difficult for owners and contractors to agree on equitable adjustments for the cumulative impact. What is needed is a reliable method (model) to identify and quantify the loss of productivity caused by the cumulative impact of change orders. A number of studies have attempted to quantify the impact of change orders on the project costs and schedule. Many of these attempted to develop regression models to quantify the loss. However, traditional regression analysis has shortcomings in dealing with highly correlated multivariable data. Moreover, regression analysis has shown limited success when dealing with many qualitative or noisy input factors. Classification and regression tree methods have the ability to deal with these complex multifactor modeling problems. This study develops decision tree models to classify and quantify the labor productivity losses that are caused by the cumulative impact of change orders for electrical and mechanical projects. The results show that decision tree models give significantly improved results for classification and quantification compared to traditional statistical methods in the field of construction productivity data analysis, which is characterized by noisiness and uncertainty.     

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.     

Benchmarking of Construction Productivity

Construction productivity has been a cause of great concern in both the construction industry and academia. Even though many companies have developed their own productivity tracking systems based on their experiences and accounting systems, none have been successful in establishing common definitions and developing a survey tool that collects standard productivity data at the appropriate levels. This research was initiated to establish a common set of construction productivity metrics and their corresponding definitions. As a result of this research effort, the Construction Productivity Metrics System (CPMS), which contain a list of direct and indirect accounts and 56 data elements grouped into seven major categories, was developed. The Construction Productivity Metrics System is a standard construction productivity data collection tool and provides a framework to report industry norms to benchmark construction productivity. Input from 73 industry experts was used in determining the 56 measuring elements and their corresponding definitions. Preliminary findings from initial sample of 16 industrial projects indicate that the productivity metrics can be produced and should be meaningful for construction productivity benchmarking. Because of the small sample size, more than general preliminary conclusion would be inappropriate. Based on the analyses, the developed CPMS is believed to be a reasonable productivity data collection tool and when sufficient data are available should be capable of producing reasonable industry benchmarks.     

Poisson Model of Construction Incident Occurrence

Construction incidents are essentially random events because they have a probabilistic component that causes their occurrence to be indeterministic. Thus, as with most random events, one of the best ways to understand and analyze construction incidents is to apply statistical methods and tools. Consequently, this paper presents a statistical framework based on the modified loss causation model (MLCM). Even though the MLCM has been used for the framework, the approach can be readily adapted for other incident causation models. The MLCM is separated into two basic components: random and systematic. The random component is represented by a probability density function (PDF), which has parameters influenced by the systematic component of the MLCM, while the systematic component is represented by the situational variables and quality of the safety management system. In particular, this paper proposes that the PDF can be represented by the Poisson distribution. Besides being a convenient and simple distribution that can be easily used in applications, the Poisson distribution had been used in various industries to model random failures or incidents. The differences in contexts and the undesirable effects of adopting an unrepresentative distribution will require formal analysis to determine the suitability of the Poisson distribution in modeling the random component of construction incident occurrence. Incident records for 14 major projects were used in the analysis. Hypothesis testing using the chi-square goodness-of-fit and dispersion tests shows that the incident occurrences can be modeled as a Poisson process characterized by some mean arrival rate. The paper also presents some applications of the proposed Poisson model to improve construction safety management, focusing on two specific concepts: the Bayesian approach and the partitioned Poisson.     

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%.     

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.     

A new SAS procedure for latent transition analysis: Transitions in dating and sexual risk behavior.

The set of statistical methods available to developmentalists is continually being expanded, allowing for questions about change over time to be addressed in new, informative ways. Indeed, new developments in methods to model change over time create the possibility for new research questions to be posed. Latent transition analysis, a longitudinal extension of latent class analysis, is a method that can be used to model development in discrete latent variables, for example, stage processes, over 2 or more times. The current article illustrates this approach using a new SAS procedure, PROC LTA, to model change over time in adolescent and young adult dating and sexual risk behavior. Gender differences are examined, and substance use behaviors are included as predictors of initial status in dating and sexual risk behavior and transitions over time. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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.     

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.     

Multiagent System for Construction Claims Negotiation

Negotiation is an important aspect of the construction process. Different negotiation theories and models are available and have been deployed in a variety of situations. In some cases, these have been incorporated into multiagent systems that address a number of engineering problems. This paper is concerned with the approach adopted in the development of a multiagent system for construction claims negotiation (MASCOT). It reviews the key negotiation theories, outlines the peculiarities of the domain (construction claims negotiation), and discusses the choice of an appropriate negotiation mechanism for MASCOT. Of particular interest is the integration of Zeuthen’s bargaining model with a Bayesian learning mechanism, which addresses the characteristics of construction claims negotiation. An example is presented to demonstrate the application of the MASCOT model in a real construction claim case.     

Comparison of the Monod and Droop Methods for Dynamic Water Quality Simulations

The Monod method is widely used to model nutrient limitation and primary productivity in water bodies. It offers a straightforward approach to simulate the main processes governing eutrophication and it allows the proper representation of many aquatic systems. The Monod method is not able to represent the nutrient luxury uptake by algae, which consists of the excess nutrient uptake during times of high nutrient availability in the water column. The Droop method, which is also used to model nutrient limitation and primary productivity, takes into account the luxury uptake of nutrients. Because of the relative complexity of the Droop method, it has not been systematically adopted for the simulation of large stream networks. The Water Quality Analysis Simulation Program (WASP) version 7.1 was updated to include nutrient luxury uptake for periphyton growth. The objective of this paper is to present the new nutrient limitation processes simulated by WASP 7.1 and to compare the performance of the Droop and the Monod methods for a complex stream network where periphyton is the main organism responsible for primary productivity. Two applications of WASP 7.1 with the Droop and Monod methods were developed for the Raritan River Basin in New Jersey. Water quality parameters affecting the transport and fate of nutrients were calibrated based on observed data collected for the Raritan River total maximum daily load. The dissolved oxygen and nutrients simulated with WASP 7.1, obtained with the Droop and Monod methods, were compared at selected monitoring stations under different flows and nutrient availability conditions. The comparison of the WASP 7.1 applications showed the importance of using the Droop method when periphyton was the main organism responsible for primary productivity. The data simulated with the Droop method resulted in good agreement with the observed data for dissolved oxygen, ammonia-nitrogen, nitrate-nitrogen, and dissolved orthophosphate at the selected stations. The Monod method was not able to capture the diel dissolved oxygen variation when nutrients were scarce, and it resulted in unrealistic diel variations of nutrients at times of strong primary productivity at some locations.     

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%.     

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.