Causal modeling alternatives in operations research: Overview and application

Ronald D. Anderson, G. Vastag

Research output: Contribution to journalArticle

51 Citations (Scopus)

Abstract

This paper uses the relationships between three basic, fundamental and proven concepts in manufacturing (resource commitment to improvement programs, flexibility to changes in operations, and customer delivery performance) as the empirical context for reviewing and comparing two casual modeling approaches (structural equation modeling and Bayesian networks). Specifically, investments in total quality management (TQM), process analysis, and employee participation programs are considered as resource commitments. The paper begins with the central issue of the requirements for a model of associations to be considered causal. This philosophical issue is addressed in reference to probabilistic causation theory. Then, each method is reviewed in the context of a unified causal modeling framework consistent with probabilistic causation theory and applied to a common dataset. The comparisons include concept representation, distribution and functional assumptions, sample size and model complexity considerations, measurement issues, specification search, model adequacy, theory testing and inference capabilities. The paper concludes with a summary of relative advantages and disadvantages of the methods and highlights the findings relevant to the literature on TQM and on-time deliveries.

Original languageEnglish
Pages (from-to)92-109
Number of pages18
JournalEuropean Journal of Operational Research
Volume156
Issue number1
DOIs
Publication statusPublished - Jul 1 2004

Fingerprint

Causation
Operations research
operations research
Quality Management
Operations Research
quality assurance
Total quality management
commitment
Resources
Structural Equation Modeling
process analysis
Model Complexity
model theory
Alternatives
Model Theory
Bayesian Networks
Modeling
resources
manufacturing
Sample Size

Keywords

  • Bayesian networks
  • Causal modeling
  • Delivery performance
  • Manufacturing
  • Structural equation modeling
  • TQM

ASJC Scopus subject areas

  • Information Systems and Management
  • Management Science and Operations Research
  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Modelling and Simulation
  • Transportation

Cite this

Causal modeling alternatives in operations research : Overview and application. / Anderson, Ronald D.; Vastag, G.

In: European Journal of Operational Research, Vol. 156, No. 1, 01.07.2004, p. 92-109.

Research output: Contribution to journalArticle

@article{0880fc6cfbb34d8aae21e09f81659552,
title = "Causal modeling alternatives in operations research: Overview and application",
abstract = "This paper uses the relationships between three basic, fundamental and proven concepts in manufacturing (resource commitment to improvement programs, flexibility to changes in operations, and customer delivery performance) as the empirical context for reviewing and comparing two casual modeling approaches (structural equation modeling and Bayesian networks). Specifically, investments in total quality management (TQM), process analysis, and employee participation programs are considered as resource commitments. The paper begins with the central issue of the requirements for a model of associations to be considered causal. This philosophical issue is addressed in reference to probabilistic causation theory. Then, each method is reviewed in the context of a unified causal modeling framework consistent with probabilistic causation theory and applied to a common dataset. The comparisons include concept representation, distribution and functional assumptions, sample size and model complexity considerations, measurement issues, specification search, model adequacy, theory testing and inference capabilities. The paper concludes with a summary of relative advantages and disadvantages of the methods and highlights the findings relevant to the literature on TQM and on-time deliveries.",
keywords = "Bayesian networks, Causal modeling, Delivery performance, Manufacturing, Structural equation modeling, TQM",
author = "Anderson, {Ronald D.} and G. Vastag",
year = "2004",
month = "7",
day = "1",
doi = "10.1016/S0377-2217(02)00904-9",
language = "English",
volume = "156",
pages = "92--109",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier",
number = "1",

}

TY - JOUR

T1 - Causal modeling alternatives in operations research

T2 - Overview and application

AU - Anderson, Ronald D.

AU - Vastag, G.

PY - 2004/7/1

Y1 - 2004/7/1

N2 - This paper uses the relationships between three basic, fundamental and proven concepts in manufacturing (resource commitment to improvement programs, flexibility to changes in operations, and customer delivery performance) as the empirical context for reviewing and comparing two casual modeling approaches (structural equation modeling and Bayesian networks). Specifically, investments in total quality management (TQM), process analysis, and employee participation programs are considered as resource commitments. The paper begins with the central issue of the requirements for a model of associations to be considered causal. This philosophical issue is addressed in reference to probabilistic causation theory. Then, each method is reviewed in the context of a unified causal modeling framework consistent with probabilistic causation theory and applied to a common dataset. The comparisons include concept representation, distribution and functional assumptions, sample size and model complexity considerations, measurement issues, specification search, model adequacy, theory testing and inference capabilities. The paper concludes with a summary of relative advantages and disadvantages of the methods and highlights the findings relevant to the literature on TQM and on-time deliveries.

AB - This paper uses the relationships between three basic, fundamental and proven concepts in manufacturing (resource commitment to improvement programs, flexibility to changes in operations, and customer delivery performance) as the empirical context for reviewing and comparing two casual modeling approaches (structural equation modeling and Bayesian networks). Specifically, investments in total quality management (TQM), process analysis, and employee participation programs are considered as resource commitments. The paper begins with the central issue of the requirements for a model of associations to be considered causal. This philosophical issue is addressed in reference to probabilistic causation theory. Then, each method is reviewed in the context of a unified causal modeling framework consistent with probabilistic causation theory and applied to a common dataset. The comparisons include concept representation, distribution and functional assumptions, sample size and model complexity considerations, measurement issues, specification search, model adequacy, theory testing and inference capabilities. The paper concludes with a summary of relative advantages and disadvantages of the methods and highlights the findings relevant to the literature on TQM and on-time deliveries.

KW - Bayesian networks

KW - Causal modeling

KW - Delivery performance

KW - Manufacturing

KW - Structural equation modeling

KW - TQM

UR - http://www.scopus.com/inward/record.url?scp=1242321013&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1242321013&partnerID=8YFLogxK

U2 - 10.1016/S0377-2217(02)00904-9

DO - 10.1016/S0377-2217(02)00904-9

M3 - Article

AN - SCOPUS:1242321013

VL - 156

SP - 92

EP - 109

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 1

ER -