System model for analyzing design productivity

Andrew S. Chang, William Ibbs

研究成果: Article

13 引文 (Scopus)

摘要

Many engineering design companies collect data such as person hours to manage projects. But the relationships between operational variables and performance are usually not thoroughly analyzed and interpreted. This paper proposes a system model and procedure to relate influence variables to project productivity. The model was tested by analyzing 190 projects of an engineering consulting company. The relationships between design productivity and various input and process variables were identified and interpreted. For example, project size has a negative relationship with productivity, while the effect of quality assurance/quality control on productivity is not clear. Based on documented data and derived information, this model can help companies gain operational insight and thus improve productivity and profitability.

原文English
頁(從 - 到)27-34
頁數8
期刊Journal of Management in Engineering
22
發行號1
DOIs
出版狀態Published - 2006 五月 22

指紋

Productivity
Industry
Quality assurance
Quality control
Profitability
System model

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Industrial and Manufacturing Engineering

引用此文

@article{405d4a110d154e70a68a2fbb3c88f95d,
title = "System model for analyzing design productivity",
abstract = "Many engineering design companies collect data such as person hours to manage projects. But the relationships between operational variables and performance are usually not thoroughly analyzed and interpreted. This paper proposes a system model and procedure to relate influence variables to project productivity. The model was tested by analyzing 190 projects of an engineering consulting company. The relationships between design productivity and various input and process variables were identified and interpreted. For example, project size has a negative relationship with productivity, while the effect of quality assurance/quality control on productivity is not clear. Based on documented data and derived information, this model can help companies gain operational insight and thus improve productivity and profitability.",
author = "Chang, {Andrew S.} and William Ibbs",
year = "2006",
month = "5",
day = "22",
doi = "10.1061/(ASCE)0742-597X(2006)22:1(27)",
language = "English",
volume = "22",
pages = "27--34",
journal = "Journal of Management in Engineering - ASCE",
issn = "0742-597X",
publisher = "American Society of Civil Engineers (ASCE)",
number = "1",

}

System model for analyzing design productivity. / Chang, Andrew S.; Ibbs, William.

於: Journal of Management in Engineering, 卷 22, 編號 1, 22.05.2006, p. 27-34.

研究成果: Article

TY - JOUR

T1 - System model for analyzing design productivity

AU - Chang, Andrew S.

AU - Ibbs, William

PY - 2006/5/22

Y1 - 2006/5/22

N2 - Many engineering design companies collect data such as person hours to manage projects. But the relationships between operational variables and performance are usually not thoroughly analyzed and interpreted. This paper proposes a system model and procedure to relate influence variables to project productivity. The model was tested by analyzing 190 projects of an engineering consulting company. The relationships between design productivity and various input and process variables were identified and interpreted. For example, project size has a negative relationship with productivity, while the effect of quality assurance/quality control on productivity is not clear. Based on documented data and derived information, this model can help companies gain operational insight and thus improve productivity and profitability.

AB - Many engineering design companies collect data such as person hours to manage projects. But the relationships between operational variables and performance are usually not thoroughly analyzed and interpreted. This paper proposes a system model and procedure to relate influence variables to project productivity. The model was tested by analyzing 190 projects of an engineering consulting company. The relationships between design productivity and various input and process variables were identified and interpreted. For example, project size has a negative relationship with productivity, while the effect of quality assurance/quality control on productivity is not clear. Based on documented data and derived information, this model can help companies gain operational insight and thus improve productivity and profitability.

UR - http://www.scopus.com/inward/record.url?scp=33646581663&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33646581663&partnerID=8YFLogxK

U2 - 10.1061/(ASCE)0742-597X(2006)22:1(27)

DO - 10.1061/(ASCE)0742-597X(2006)22:1(27)

M3 - Article

AN - SCOPUS:33646581663

VL - 22

SP - 27

EP - 34

JO - Journal of Management in Engineering - ASCE

JF - Journal of Management in Engineering - ASCE

SN - 0742-597X

IS - 1

ER -