• Korean Journal of Construction Engineering and Management
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• v.9 no.4
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• pp.92-100
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• 2008

Productivity is acknowledged as a very important factor for successful construction projects. Various data items collected daily form a construction site can be used for monitoring its productivity by analyzing them. However, no analytical methods for that purpose have been established in the domestic construction industry yet. Previous researches that utilized OLAP and data mining to analyze the factors that affect the productivity did not do well with predicting future cases with sufficient reliability. This research therefore proposes a new analytical process which is capable of figuring out the factors that would affect the productivity of future projects, through qualitative and quantitative analysis of the data collected from past projects.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.422-426
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• 2006

Productivity is one of the very important index that measures efficiency of production activities in industry, enterprises and the building industry as well. None the less, the concept of construction productivity is not so clear that productivity management in the building industry have been performed by experience or intuition, productivity related data have not been analyzed through effective productivity management, because structured definition and classification of factors influencing construction productivity did not exist so that it has not been known what information explain each of them. In order to solve this problem, at first construction productivity and factors influencing construction productivity are defined and classified into three groups; (1)Project factors influencing construction productivity (2)Management factors influencing construction productivity (3)Activity factors influencing construction productivity. To find out relation between construction productivity and factors influencing construction productivity, a questionnaire survey for construction managers in the building industry has been conducted.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.548-553
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• 2006

Activity-based productivity data can be used as an significant reference in many areas of project management such as performance evaluation and project planning. However, the existence of various factors influencing construction productivity makes it difficult to collect and analyze the productivity data. In the most of the domestic construction sites, there is no systematic method to collect and analyze the productivity data along with information on influencing factors; it is common to heavily rely on experience and intuition of field managers when dealing with construction productivity data. Therefore it is necessary to develop a management system for collecting and utilizing the productivity data as well as the factors influencing construction productivity. The main objective of this research is to define the construction productivity and its influencing factors at the activity level. In addition, methodologies on how to analyze the productivity data and to estimate productivity of future projects are proposed.

• Journal of the Korea Institute of Building Construction
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• v.11 no.2
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• pp.100-107
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• 2011

As construction duration significantly influences the performance and the success of construction projects, it is necessary to appropriately manage the impact factors affecting construction duration. Recently, interest in the construction industry has been rising due to the recent change in the construction legal system, and the competition among the construction companies on construction time. However, the impact factors are extremely diverse. The existing productivity data on impact factors is not sufficient to properly identify the impact factor and measure the productivity from various perspectives, such as subcontractor, time, crew, work and so on. In this respect, a multidimensional analysis by a data warehouse is very helpful in order to view the manner in which productivity is affected by impact factors from various perspectives. Therefore, this research proposes a method that effectively takes the diverse productivity data of impact factors, and generates a multidimensional analysis. Decision tree analysis, a data mining technique, is also applied in this research in order to supply construction managers with appropriate productivity data on impact factors during the construction management process.

• Korean Journal of Construction Engineering and Management
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• v.21 no.1
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• pp.99-110
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• 2020

Productivity is also a very important indicator in the construction industry as it can measure economic growth and the efficiency of each related production activity, either at the industrial level or at the corporate level. Technical factors indeed have a great impact on productivity, but in order to improve productivity in the actual construction industry, various productivity factors must be analyzed first, and the efforts to improve productivity at the project level are more important than the efforts to improve productivity at the construction industry level, which are addressed from a macro perspective. This study was designed to provide basic data for efficient productivity management activities of the project by selecting priority management factors through the causal analysis of the factors and the elicitation of the productivity degradation factors at the construction site to improve the quality of the construction industry.

• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.751-760
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• 2023

Enhancing productivity in earthwork projects is crucial, significantly affecting both time and cost efficiencies. However, existing research in this domain predominantly relies on qualitative data and methodologies, which may not suffice given its critical significance. This study employed the fuzzy DEMATEL method to conduct a structural analysis of variables affecting productivity in construction projects. The findings reveal that plan changes possess the most substantial overall influence on earthwork productivity, with a comprehensive strength rating of 4.58. Additionally, it was observed that precipitation data exerted the most pronounced positive impact, with a rating of 0.48. These insights are anticipated to aid in identifying and prioritizing areas for productivity enhancement in construction projects.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.571-580
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• 2018

The purpose of this study is to evaluate the productivity of elderly employees over 60 years old and to analyze factors that directly or indirectly affect the productivity of elderly employees, based on empirical facts of HR managers. As a result, the factors that have a positive effect on the productivity of elderly employees were attitude and professionalism. The decline in physical capacity due to aging was recognized as a key factor that negatively affected the productivity evaluation of elderly employees. Among the management strategies to improve the work performance, it has been shown that work placement or job redesign according to physical competency contributes to improving the productivity of elderly employees. Also, providing safety education and communication opportunities among workers has a positive effect on productivity improvement. The results of this study suggest that it is important to improve worker productivity by arranging older workers' work experience to be able to demonstrate their accumulated career and expertise and to create a working environment that takes into account physical competence.

• Journal of the Korea Institute of Building Construction
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• v.14 no.4
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• pp.314-321
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• 2014

Nuclear power plant projects are being increased all over the world. The construction of nuclear power plants needs huge money and time, which makes conducting a detailed analysis of productivity through the whole process. Reinforced-concrete works productivity field data was collected for more than one year and analyzed from a nuclear-power-plant project in Korea. The productivities of formwork, rebar-work, and concrete pouring were $0.54m^2/man{\cdot}day$, $0.06ton/man{\cdot}day$, $1.98m^3/man{\cdot}day$, respectively. Moreover, it is revealed that 'Day of the Week' is a driver of the formwork activity and 'Overtime' is for all of the three. The results will be a great interest of industry personnel estimating time and cost of a new nuclear power plant.

• Korean Journal of Construction Engineering and Management
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• v.8 no.1 s.35
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• pp.132-140
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• 2007

There is an increase in interest and investment in high-rise housing as it is perceived to be a new value-added market in the construction industry. In constructing a high-rise housing, the finishing works are executed in accompaniment with many other activities that are progressed repeatedly and spontaneously on each floor. It was reported that the duration of finishing works differs according to the management ability of the executing company and has a significant effect on the entire project duration. We suggest a need to concentrate on important management factors by analyzing the factors affecting the productivity of finishing works based on the site characteristics in high-rise housing. There are various complex productivity-affecting factors including the technical factors involved in planning and managing the processes of finishing works. From the viewpoint of planning and management factors, the importance of productivity-affecting factors was analyzed using the Analytic Hierarchy Process (AHP). A continuous examination of the management of high-importance factors will make it possible to improve productivity by enhancing the understanding of productivity-affecting factors of finishing works and suggesting a practical management direction.

• Journal of Korea Technology Innovation Society
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• v.13 no.2
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• pp.231-251
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• 2010

The numbers of SCI paper or patent in science and technology are expected to be related with the number of researcher and knowledge stock (R&D stock, paper stock, patent stock). The results of the regression model showed that severe multicollinearity existed and errors were made in the estimation and testing of regression coefficients. To solve the problem of multicollinearity and estimate the effect of the independent variable properly, principal component regression model were applied for three cases with S&T knowledge production. The estimated principal component regression function was transformed into original independent variables to interpret properly its effect. The analysis indicated that the principal component regression model was useful to estimate the effect of the highly correlate production factors and showed that the number of researcher, R&D stock, paper or patent stock had all positive effect on the production of paper or patent.