整合 AHP 和模糊推理系统,增强组织转型历程的能力:评估中小企业实施工业 4.0 的情况

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Intelligence Pub Date : 2024-09-13 DOI:10.1007/s10489-024-05816-0
Isabel Fernández, Javier Puente, Borja Ponte, Alberto Gómez
{"title":"整合 AHP 和模糊推理系统,增强组织转型历程的能力:评估中小企业实施工业 4.0 的情况","authors":"Isabel Fernández,&nbsp;Javier Puente,&nbsp;Borja Ponte,&nbsp;Alberto Gómez","doi":"10.1007/s10489-024-05816-0","DOIUrl":null,"url":null,"abstract":"<div><p>The combined use of the Analytical Hierarchy Process (AHP) and Fuzzy Inference Systems (FISs) can significantly enhance the effectiveness of transformative projects in organizations by better managing their complexities and uncertainties. This work develops a novel multicriteria model that integrates both methodologies to assist organizations in these projects. To demonstrate the value of the proposed approach, we present an illustrative example focused on the implementation of Industry 4.0 in SMEs. First, through a review of relevant literature, we identify the key barriers to improving SMEs' capability to implement Industry 4.0 effectively. Subsequently, the AHP, enhanced through Dong and Saaty’s methodology, establishes a consensus-based assessment of the importance of these barriers, using the judgments of five experts. Next, a FIS is utilized, with rule bases automatically derived from the preceding weights, eliminating the need for another round of expert input. This paper shows and discusses how SMEs can use this model to self-assess their adaptability to the Industry 4.0 landscape and formulate improvement strategies to achieve deeper alignment with this transformative paradigm.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 23","pages":"12357 - 12377"},"PeriodicalIF":3.4000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-05816-0.pdf","citationCount":"0","resultStr":"{\"title\":\"Integration of AHP and fuzzy inference systems for empowering transformative journeys in organizations: Assessing the implementation of Industry 4.0 in SMEs\",\"authors\":\"Isabel Fernández,&nbsp;Javier Puente,&nbsp;Borja Ponte,&nbsp;Alberto Gómez\",\"doi\":\"10.1007/s10489-024-05816-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The combined use of the Analytical Hierarchy Process (AHP) and Fuzzy Inference Systems (FISs) can significantly enhance the effectiveness of transformative projects in organizations by better managing their complexities and uncertainties. This work develops a novel multicriteria model that integrates both methodologies to assist organizations in these projects. To demonstrate the value of the proposed approach, we present an illustrative example focused on the implementation of Industry 4.0 in SMEs. First, through a review of relevant literature, we identify the key barriers to improving SMEs' capability to implement Industry 4.0 effectively. Subsequently, the AHP, enhanced through Dong and Saaty’s methodology, establishes a consensus-based assessment of the importance of these barriers, using the judgments of five experts. Next, a FIS is utilized, with rule bases automatically derived from the preceding weights, eliminating the need for another round of expert input. This paper shows and discusses how SMEs can use this model to self-assess their adaptability to the Industry 4.0 landscape and formulate improvement strategies to achieve deeper alignment with this transformative paradigm.</p></div>\",\"PeriodicalId\":8041,\"journal\":{\"name\":\"Applied Intelligence\",\"volume\":\"54 23\",\"pages\":\"12357 - 12377\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s10489-024-05816-0.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10489-024-05816-0\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Intelligence","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10489-024-05816-0","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

结合使用层次分析法(AHP)和模糊推理系统(FIS)可以更好地管理项目的复杂性和不确定性,从而显著提高组织转型项目的成效。这项工作开发了一种新颖的多标准模型,将这两种方法整合在一起,以协助组织开展这些项目。为了证明所提方法的价值,我们以中小企业实施工业 4.0 为例进行了说明。首先,通过回顾相关文献,我们确定了提高中小企业有效实施工业 4.0 能力的关键障碍。随后,通过 Dong 和 Saaty 的方法改进的 AHP,利用五位专家的判断,对这些障碍的重要性进行了基于共识的评估。接着,利用 FIS,从前面的权重中自动得出规则基础,从而消除了另一轮专家输入的需要。本文展示并讨论了中小型企业如何利用这一模型来自我评估其对工业 4.0 环境的适应性,并制定改进战略,以更深入地与这一变革范式保持一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Integration of AHP and fuzzy inference systems for empowering transformative journeys in organizations: Assessing the implementation of Industry 4.0 in SMEs

The combined use of the Analytical Hierarchy Process (AHP) and Fuzzy Inference Systems (FISs) can significantly enhance the effectiveness of transformative projects in organizations by better managing their complexities and uncertainties. This work develops a novel multicriteria model that integrates both methodologies to assist organizations in these projects. To demonstrate the value of the proposed approach, we present an illustrative example focused on the implementation of Industry 4.0 in SMEs. First, through a review of relevant literature, we identify the key barriers to improving SMEs' capability to implement Industry 4.0 effectively. Subsequently, the AHP, enhanced through Dong and Saaty’s methodology, establishes a consensus-based assessment of the importance of these barriers, using the judgments of five experts. Next, a FIS is utilized, with rule bases automatically derived from the preceding weights, eliminating the need for another round of expert input. This paper shows and discusses how SMEs can use this model to self-assess their adaptability to the Industry 4.0 landscape and formulate improvement strategies to achieve deeper alignment with this transformative paradigm.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
期刊最新文献
ZPDSN: spatio-temporal meteorological forecasting with topological data analysis DTR4Rec: direct transition relationship for sequential recommendation Unsupervised anomaly detection and imputation in noisy time series data for enhancing load forecasting A prototype evolution network for relation extraction Highway spillage detection using an improved STPM anomaly detection network from a surveillance perspective
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1