Six Sigma-Based Approach to Improve Performance in Construction Operations

Many researchers and project managers have attempted to improve project performance by applying new philosophies such as lean principle, just-in-time, pull scheduling, and last planner. However, very little research has been conducted on setting definite quantitative goals for performance improvement while considering the defect rate involved in the construction operations. This research explores practical solutions for construction performance improvement by applying the six sigma principle. This principle provides the metrics required to establish performance improvement goals and a methodology for measuring and evaluating improvement. The proposed approach is expected to achieve more reliable workflows by reducing process variability to fit in a desirable range—thereby improving the overall performance through the evaluation of the quality level in current construction operations. To verify the suggested methodology, two case studies have been presented and process simulation analyses are performed to observe the performance changes based on the six sigma principle. Critical total quality control, as the sigma level rises, is also discussed.     

Runtime User Interaction in Concurrent Simulation-Animations of Construction Operations

Current state-of-the-art tools allow for the accurate modeling of complex construction operations using discrete-event simulation and their realistic postprocessed three-dimensional animation. Due to the postprocessed nature of these animations it is not possible to interact with them so as to affect the remaining course of actions. The next logical step in the evolution of simulation modeling and visualization in construction is for simulations and animations to run concurrently and in a manner that allows interaction with the animation to affect the course events in the simulation. This effectively enables the creation of virtual environments with logic based on discrete-event simulation. This paper presents the user interaction architecture that accomplishes this. In particular, the paper presents: (a) the conceptualization developed to design the components for user interaction and (b) the design of the components with a focus on (i) empowering model developers to enable user interaction in their models and (ii) extensibility to enable the development of more advanced user interaction techniques.     

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.     

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.     

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.     

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.     

Learning Curve Models of Construction Productivity

Current research into learning curve models of construction productivity is presented. Five mathematical models are identified and each of these are used to model unit rates for 65 sets of data. The correlation between predicted and actual unit rates is determined, and on this basis, it is concluded that the best predictor is a cubic model. The often cited straight‐line model is only marginally adequate. The validity of the straight‐line model is further undermined by showing that the learning rate is not a constant value. Time data for erecting and setting 466 precast concrete floor planks is used to support the conclusion that the straight‐line model is not a reliable model for predicting future performance.     

Construction Productivity Improvement

Construction productivity has been on the decline in the last decade. The results are presented on a survey of the Engineering News‐Record 400 largest contractors to obtain their views on where productivity improvements would most help and to compare the trends with a similar survey carried out in 1979. Data were collected on the general company characteristics of the responding contractors, and on the contractors' opinions on potential areas for productivity improvement in the office and in the field. Findings indicate that immediate research should concentrate on improving marketing practices, planning and scheduling, labor‐management relations, site supervision, industrialized building systems, equipment policy and engineering design; and that governmental regulations have lost the immediate urgency attached to them in 1979.It is also recommended that similar surveys be conducted every 3 to 4 years to identify new trends and to steer research in the appropriate direction.     

Generic Process Mapping and Simulation Methodology for Integrating Site Layout and Operations Planning in Construction

The present research is intended to address dynamic construction-process simulation methods, with a focus on how to effectively model resource transit among various activity locations in the site system. Following a review of basic simulation paradigms and recent research developments, we propose a new process mapping and simulation methodology for modeling construction operations. The simulation algorithm is presented and the process mapping procedure is illustrated step by step using an earth-moving example featuring technology and resource constraints. It is straightforward to convert the resultant process mapping model describing workflows and resource flows over site locations into a simulation model. A STROBOSCOPE model is formed for the same problem definition to contrast and cross-validate our methodology with the established activity cycle diagram-based modeling approach. One additional case of modeling the concreting site operations by the hoist and barrow method is also given to demonstrate the application of the proposed methodology in practical settings.     

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

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

Particle Swarm Optimization-Supported Simulation for Construction Operations

This study proposes an integration of particle swarm optimization (PSO) and a construction simulation so as to determine efficiently the optimal resource combination for a construction operation. The particle-flying mechanism is utilized to guide the search process for the PSO-supported simulation optimization. A statistics method, i.e., multiple-comparison procedure, is adopted to compare the random output performances resulting from the stochastic simulation model so as to rank the alternatives (i.e., particle-represented resource combinations) during the search process. The indifference zone and confidence interval facilitate consideration of the secondary performance measure (e.g., productivity) when the main performance measures (e.g., cost) of the competing alternatives are close. The experimental analyses demonstrate the effectiveness and efficiency of the proposed simulation optimization. The study aims to providing an alternative combination of optimization methodology and general construction simulation by utilizing PSO and a statistics method so as to improve the efficiency of simulation in planning construction operations.     

Framework for Building Intelligent Simulation Models of Construction Operations

Modeling and analyzing construction operations using simulation techniques allows researchers to capture the uncertainty and randomness usually associated with these operations and can thus be an effective tool for analysis and improvement. However, the effort and knowledge required to build simulation models and experiment with them tend to limit the use of simulation in construction. A common recommendation for removing this obstacle found in the literature leans towards developing simulation tools that reduce model development and experimentation time on the construction engineer’s side by packaging most of the knowledge required into the tool itself. Such “intelligent” simulation modeling tools may significantly impact the way construction engineers use simulation techniques in day-to-day decision?making. This paper presents a framework that extends and formalizes this recommendation by providing the foundation for building intelligence into simulation objects. The proposed framework provides the structure necessary for building intelligence and autonomy into simulation objects and permits a further reduction in the knowledge required to experiment with simulation models. This approach also automates model modification, not only through changes in numeric parameters, but through topological model changes as well, which may assist the model user in making many decisions throughout the different phases of simulation experimentation.     

Modeling Time-Constraints in Construction Operations through Simulation

Construction operations often face time constraints that influence the execution of activities, which are not addressed enough when modeling through general discrete-event simulation. This paper describes a simulation-based methodology to handle the time constraints including the cyclical break, preemption, and overtime use. In consideration of the variable number of breaks or variable break duration for different activities, an algorithm to determine the execution of the time-constrained activities is proposed, in which the concepts of time cycle and time window are introduced. The proposed algorithm is incorporated with an activity scanning simulation strategy to develop a construction simulation for modeling the time-constrained construction operations. Some examples are presented to illustrate and validate the algorithm, and highlight the effectiveness of the developed construction simulation. The study provides an alternative to improve construction simulation in modeling of the time-constrained construction operations and is expected to assist researchers or practitioners at analyzing or planning construction operations.     

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.     

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.     

Application of Fuzzy Logic to Simulation for Construction Operations

This paper describes the application of fuzzy logic to discrete event simulation in dealing with uncertainties of construction operations. The uncertainties in the quantity of resources required to activate an activity are modeled with fuzzy sets in linguistic terms. The fuzzy logic if-then rule is built to control the activation of activities. The duration of the activity that varies with the quantities of resources involved is determined through the fuzzy logic rule-based model. The fuzzy logic control of activities is incorporated with the activity scanning simulation strategy to implement the fuzzy simulation system for construction operations. In addition, the fuzzy activity element is adopted in the graphical modeling process. Examples are given that illustrate uses of the fuzzy simulation system and the impact of flexible demand of resources on productivity.     

Model to Predict the Impact of a Technology on Construction Productivity

Although some new technologies promise to improve construction productivity, their ability to deliver is not always realized. Building on a great deal of prior research, a four-stage predictive model was developed and validated to estimate the potential for a technology to have a positive impact on construction productivity. The four stages examine the costs, feasibility, usage history, and technical impact of a technology. The predictive model combines results from historical analyses to formalize how selected technologies with improved construction productivity can be used as a predictor of how future technologies might do the same. Each of the stages of a predictive model was subdivided into a series of categories and questions, which were weighted by importance by using the analytic hierarchy process and historical analysis to generate a performance score for the analyzed technology. The predictive model was then validated by using 74 previous and existing construction technologies. Statistical analysis confirmed that average performance scores produced by the model were significantly different across the categories of successful, inconclusive, and unsuccessful in the actual implementation experience of technologies.     

Improving Productivity Estimates by Work Sampling

Theoretical aspects are presented to evaluate the adequacy of work sampling as a surrogate productivity measure. A 10‐week study is described in which work sampling data were gathered simultaneously with earned value information for a 10‐man, small‐bore pipefitter crew. Two forms of work sampling data were gathered that differed principally in the definition of direct work. The direct work percentages are statistically correlated to the ratio of earned to actual man‐hours, and it is shown that the Pearson product‐moment correlation coefficient could be improved by as much as 86% if the definition of direct work is restricted. The probability of a Type I statistical error is also greatly reduced. Data summarized according to 5‐ or 7‐day moving averages is found to be more reliable than daily or weekly averages. It is shown how a prediction equation could be developed. The slope of this equation suggests that rather significant changes in the earned to actual man‐hour ratio are reflected by rather small changes in direct work.     

Slip-Form Application to Concrete Structures

Because of superior speed and productivity, slip forms were extensively utilized as a potential formwork candidate in constructing concrete structures for the past few decades. Typical projects that employ this formwork technique are: Core of high-rise buildings, silos, telecommunication towers, cooling towers, heavy concrete offshore platforms, etc. The research presented in this paper aims at studying slip-form application to cores and silos, assessing its productivity, and determining its appropriate speed as well as auxiliary resource combinations. Simulation models are developed in which the potential control units in a slip-form system are described for cores and silos. Data are collected from several case study projects. A set of charts has been developed to predict productivity considering different stoppages, core cross section area, slipping (jacking) rate, and concrete placing methods. These charts play an essential role in managing slip-form application to cores and silos. Results show that the developed simulation models predict the productivity of case study projects with 99.70 and 99.30% accuracy for cores and silos, respectively. The presented research is relevant to both researchers and practitioners. It provides practitioners with charts that assist in scheduling and managing the required resources for slip-form application. In addition, it provides researchers with simulation models and framework for implementing slip forms to core and silo construction.     

Productivity and Cost Regression Models for Pile Construction

The estimating process of pile construction productivity and cost is intricated because of several factors: unseen subsurface obstacles; lack of contractor experience; site planning; and pile equipment maintainability. This study intends to assess cycle time, productivity, and cost for pile construction considering the effect of the above factors using regression technique. Data were collected through designated questionnaires, site interviews, and telephone calls to experts in different construction companies. Many variables have been considered in the pile construction process. Seven regression linear models have been designed and validated to assess productivity, cycle time, and cost. Consequently, three sets of charts have been developed based upon the validated models to provide the decision maker with a solid planning, scheduling, and control tool for pile construction projects. This research is relevant to both industry practitioners and researchers. It provides sets of charts and models for practitioners’ usage to schedule and price out pile construction projects. In addition, it provides the researchers with the methodology of designating regression models for the pile construction process, its limitations, and future suggestions.