系统动力学在企业工程中的应用--系统文献综述

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-05-14 DOI:10.1108/jm2-05-2023-0082
Huda Hussain, Marne De Vries
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引用次数: 0

摘要

本研究旨在调查系统动力学(SD)应用在企业工程(EE)研究和实践中的综合使用情况。SD 在企业工程中的应用正被广泛接受,成为支持决策过程和捕捉企业内部关系的工具。设计/方法/途径采用标准的系统文献综述(SLR)方法,对现有文献进行全面综述。结果SLR 显示,90% 的结果集由同行评审的学术会议和期刊论文组成。SLR 确定了一个由 83 位作者组成的高度分散的作者集。在这些作者中,Vinay Kulkarni 是一位活跃的作者,他在该研究领域与他人合作发表了多达四篇论文。分析进一步显示,将 SD 应用与电子工程结合使用是一个新兴的研究领域,其成熟度仍有待提高。虽然 EE 的各个阶段都受到了关注,但目前的研究工作更侧重于设计阶段。原创性/价值该研究阐明了结合 SD 和 EE 学科技术的跨学科研究的现状,提出了结合这些现有学科优势的未来研究课题。
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System dynamics applied in enterprise engineering – a systematic literature review

Purpose

This study aims to investigate the combined use of System Dynamics (SD) applications in Enterprise Engineering (EE) research and practice. SD application in EE is becoming widely accepted as a tool to support decision-making processes and for capturing relationships within enterprises.

Design/methodology/approach

A systematic literature review (SLR) is conducted using a standard SLR method to provide a comprehensive review of existing literature. The search was conducted on ten platforms identifying 30 publications which were analysed through the use and development of a codebook.

Findings

The SLR showed that 90% of the result set consisted of peer-reviewed academic conferences and journal papers. The SLR identified a highly dispersed author set of 83 authors. Amongst these authors, Vinay Kulkarni was an active author who has co-authored up to four publications in this research area. The analysis further revealed that the combined use of SD applications and EE is an emerging research area that still needs to develop in maturity. While all phases of EE have received attention, the current research work is more focused on the design phase. The important gap between model development and implementation is identified.

Originality/value

The study elucidates the existing status of interdisciplinary research combining techniques from the SD and EE disciplines, suggesting future research topics that combine the strengths of these existing disciplines.

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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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