Quantitative Effects of Construction Changes on Labor Productivity

Thomas, H. Randolph; Napolitan, Carmen L.

doi:10.1061/(asce)0733-9364(1995)121:3(290)

Cited by 143 publications

(68 citation statements)

References 4 publications

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“…This method provides information on the amount of time workers spend performing productive, supportive, and non-productive work (Jenkins and Orth, 2003). Work sampling is a technique that provides valuable information to a construction manager regarding areas of low productivity in need of corrective action (Thomas and Napolitan, 1999). Work sampling can be conducted by anyone with a basic knowledge of both construction and work sampling methods.…”

Section: Research Objectives Scope and Methodologymentioning

confidence: 99%

Analyzing electrical installation labor productivity through work sampling

Hajikazemi

Andersen

Langlo

2017

IJPPM

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. The construction industry is a labor-intensive industry, hence, the presence of labor with high productivity at each stage of the project plays a significant role in achieving project success. The main purpose for carrying out this study was to provide real-life empirical data about the current level of construction productivity, in this case electrical installation work, within construction projects in Norway. In addition, it was of interest to identify the areas which have the highest potential for improvement of labor productivity. Design/methodology/approachThis study considers the construction labor productivity through an elaboration on productive vs. unproductive time within construction projects. It is based on a "frequency study" done on eight construction projects in Norway. The "work sampling" method has been used for collection of empirical data. For each project, four electricians were observed an entire working day and the amount of time they spent on performing each activity was recorded every 60 th seconds. The activities observed were based on a predefined set of activities. FindingsThe results of the observations show that on average, 61.1 percent of the time was direct value-added work. This number is significantly better than what is normally discussed as being productive time. However, the findings also show that there is still potential for improvement. The activities that have the highest potential for improvement include "material transfer", "amendments to already executed installation", "personal needs" and "waiting times". Practical implicationsThe study results will be of immense benefit to managers of construction projects as well as managers of construction organizations in enhancing their project performance and productivity. Originality/valueThis paper contributes both theoretically and empirically to the current discussion and findings on labor productivity and its relation to project success. The results presented in this paper have important implications of labor productivity in construction projects and future studies in the area of project performance.

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Section: Research Objectives Scope and Methodologymentioning

confidence: 99%

Analyzing electrical installation labor productivity through work sampling

Hajikazemi

Andersen

Langlo

2017

IJPPM

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“…DSMs (Design Structure Matrices) are often applied in order to capture phase dependencies (Browning & Eppinger, 2002;Browning, 1998Browning, , 2001Cho & Eppinger, 2005;Eppinger et al, 1994;Yassine et al, 2001) and thus to establish the quantification of process concurrence relationships in System Dynamics models (Ford & Sterman, 2003;Kasperek et al, 2014;Le, 2013;Lin et al, 2008;Ruutu et al, 2011). Different quantification approaches for staff allocation are explored by Black & Repenning (2001); Joglekar & Ford (2005); Kasperek, Lindinger et al (2014); Repenning (2000) and Taylor & Ford (2006) For the quantification of the influences on productivity and thus, work rates, non-System Dynamics literature may prove helpful (Brunies & Emir, 2001;Kernan et al, 1994;Kvâlseth, 1978;Maynard & Hakel, 1997;Nepal et al, 2006;Rosenbaum & Rosenbaum, 1971;Thomas & Napolitan, 1995;Thomas & Raynar, 1997). However, these heuristics from the field of social science have not been applied in System Dynamics models.…”

Section: Heuristics For Quantificationmentioning

confidence: 99%

Structure‐Based System Dynamics Analysis of Engineering Design Processes

Kasperek

Schenk

Kreimeyer³

et al. 2016

Systems Engineering

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The dynamic process behavior of Engineering Design Processes (EDPs) needs to be understood in order to distribute resources appropriately as well as for cost and schedule calculation. Currently, structural and behavioral modeling approaches are used in the context of EDP management. In many use cases, it is essential to have at hand both the static structural view and the dynamic behavioral one. Currently, however, the two modeling approaches cannot be sufficiently combined. Within this work, a framework for the structure‐based System Dynamics analysis of EDPs is presented. It uses structural models as a basis for constructing System Dynamics models to simulate the process behavior and thereby supports understanding the correlation of process structure and behavior. This framework was iteratively developed by conducting case studies and consists of three layers, a five‐step procedure and special support for the transformation of the models. For the validation, a subset of the design process at a large German commercial vehicle manufacturer has been chosen. Based on the application of the framework during a six‐month project at the company, the industrial experts rated the framework as beneficial for the purpose of optimizing EDPs. Furthermore, they stated that the transparent representation of the sequence of the process, influencing factors and control variables in combination with the quantitative data had improved their understanding of the overall process.

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“…Thomas and Napolitan [9] quantify the impact of changes on field-labor efficiency and determine the relationship between changes and various types of disruption. Riley et al [10] investigate 598 change orders issued by a company in 120 projects and analyze the trends in the cost and frequency of change orders.…”

Section: Literature Reviewmentioning

confidence: 99%

Collar Option Model for Managing the Cost Overrun Caused by Change Orders

Lee

Kim

2015

Sustainability

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Abstract:Effective change order management is very important in maintaining the financial sustainability of various stakeholders related to construction projects by minimizing cost overruns. In this study, we propose a zero-cost risk management approach based on the collar option model in order to control for the loss caused by change orders, the main cause of cost overruns in construction projects. We apply this model to actual projects for empirical analysis. The analysis, based on 237 projects, indicates that insurance buyers benefit from the collar option model in 46% of the cases, while insurance sellers do so in 53% of the cases. In most cases, the insurance buyer is the owner. According to the model, the owner experiences a loss when the cost overrun caused by change orders is lower than what was expected. In such cases, it is appropriate to conclude that the loss is not caused by the collar option model, but by the absence of additional revenue. However, the insurance seller suffers a loss if the cost overrun is higher than the strike price of the call option. Thus, the insurance seller needs to have expertise in construction management.

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Quantitative Effects of Construction Changes on Labor Productivity

Cited by 143 publications

References 4 publications

Analyzing electrical installation labor productivity through work sampling

Analyzing electrical installation labor productivity through work sampling

Structure‐Based System Dynamics Analysis of Engineering Design Processes

Collar Option Model for Managing the Cost Overrun Caused by Change Orders

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