Quantitative and qualitative decision-making methods in simulation modelling

doi:10.1108/00251740210413370

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Management Decision

ISSN: 0025-1747
Incorporates: Journal of Management History (Archive)

Online from: 1967

Subject Area: Management Science/Management Studies

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Quantitative and qualitative decision-making methods in simulation modelling

Document Information:
Title:	Quantitative and qualitative decision-making methods in simulation modelling
Author(s):	Tillal Eldabi, (Department of Information Systems and Computing, Brunel University, Uxbridge, UK), Zahir Irani, (Department of Information Systems and Computing, Brunel University, Uxbridge, UK), Ray J. Paul, (Department of Information Systems and Computing, Brunel University, Uxbridge, UK), Peter E.D. Love, (School of Management Information Systems, Edith Cowan University, Perth, Australia)
Citation:	Tillal Eldabi, Zahir Irani, Ray J. Paul, Peter E.D. Love, (2002) "Quantitative and qualitative decision-making methods in simulation modelling", Management Decision, Vol. 40 Iss: 1, pp.64 - 73
Keywords:	Modelling, Qualitative techniques, Quantitative techniques
Article type:	Conceptual Paper
DOI:	10.1108/00251740210413370 (Permanent URL)
Publisher:	MCB UP Ltd
Abstract:	Discrete event simulation (DES) is widely known to be a quantitative research tool. A simulation modelling process is mainly based on feeding quantitative data into a model to produce quantitative results in a structured sequential process. Qualitative approaches to research take a less structured approach with more of an inclination towards judgmental and expert knowledge rather than hard data. In this paper the authors suggest that DES can be employed as both a qualitative and quantitative research tool. The paper demonstrates how simulation may represent both stances either separately or combined. This is based on the fact that the basic objectives of simulation are either for understanding – which needs a qualitative perspective – or performance measurement – which a needs quantitative perspective. Traditional quantitative and qualitative methods are discussed showing how DES might cope with the weaknesses of both stances. A structure for using DES as a combined research methodology is proposed.