Data-driven decision making in advanced manufacturing Systems: modeling and analysis of critical success factors

IF 2.8 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Vimlesh Kumar Ojha, Sanjeev Goyal, Mahesh Chand
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Abstract

ABSTRACTData-driven decision making (DDDM) in advanced manufacturing systems (AMS) is the use of data to make smart decisions that improve manufacturing operations. Companies can make themselves more competitive, cut costs, and improve their production by using data analytics. The investigation of critical success factors aids companies in identifying vital areas that demand attention for the implementation of DDDM in AMS. This comprehension enables companies to devise effective strategies for the successful adoption of DDDM within AMS. In this research, twelve critical success factors that affect the use of DDDM in AMS were discovered and statistically analysed using an integrated methodology of ISM, MICMAC, and DEMATEL to create a hierarchical model. This research paper suggests that companies should focus on developing a skilled workforce and creating a data-driven culture to successfully adopt DDDM in AMS. Additionally, the findings highlight the importance of top management support and government initiatives in promoting the adoption of DDDM in manufacturing.KEYWORDS: Advanced manufacturing Systemscritical success factors (CSFs)DDDMadoptionbig data (BD)ISM-DEMATEL Article highlight Produces a roadmap for the implementation of DDDM in AMS.Exploring the key drivers that enable the effective implementation of DDDM in AMS through the identification of critical success factors (CSFs).Analysing the CSFs and modelling them on the basis of their prominence using an integrated ISM-MICMAC-DEMATEL methodology.Abbreviations DDDM=Data-driven decision makingAMS=Advanced Manufacturing SystemsCSFs=Critical Success FactorsBDA=Big data analyticsDT=Digital transformationLR=Literature reviewIoT=Internet of ThingsCPS=Cyber-physical systemsSME=Small & medium-sized4IR=Fourth industrial revolution or Industry 4.0SM=Smart ManufacturingISM=Interpretive structural modellingAcknowledgmentsIndustry professionals from India’s manufacturing sector were a huge help to the authors in identifying and comparing factors and validating findings, and the authors are grateful for their assistance.Disclosure statementIt should be noted that the research discussed in this publication was not influenced by any financial or personal conflicts of interest of the authors.Data availability statementAll data generated or analysed during this research are included in this article.
先进制造系统中的数据驱动决策:关键成功因素的建模和分析
【摘要】先进制造系统(AMS)中的数据驱动决策(DDDM)是利用数据做出改进制造操作的明智决策。通过使用数据分析,公司可以使自己更具竞争力,降低成本,并提高产量。对关键成功因素的调查有助于公司确定在AMS中实施DDDM需要注意的关键领域。这种理解使公司能够制定有效的战略,在AMS中成功采用DDDM。在这项研究中,我们发现了影响DDDM在AMS中使用的12个关键成功因素,并使用ISM、MICMAC和DEMATEL的综合方法进行了统计分析,以创建一个层次模型。本研究报告建议,企业应专注于培养一支熟练的员工队伍,并创造一种数据驱动的文化,以成功地在AMS中采用DDDM。此外,研究结果强调了高层管理支持和政府举措在促进制造业采用DDDM方面的重要性。关键词:先进制造系统关键成功因素(CSFs)DDDM采用大数据(BD)ISM-DEMATEL文章重点为在AMS中实施DDDM提供了路线图。通过确定关键成功因素(CSFs),探索在AMS中有效实施DDDM的关键驱动因素。利用综合的ISM-MICMAC-DEMATEL方法对气候变化框架进行分析,并根据其突出程度对其进行建模。缩写DDDM=数据驱动决策ams =先进制造系统scsf =关键成功因素sbda =大数据分析sdt =数字化转型lr =文献综述ot =物联网scps =网络物理系统ssme =中小型4ir =第四次工业革命或工业4.0SM=智能制造ism =解释性结构建模致谢来自印度制造业的行业专业人士在识别和比较因素和方面为作者提供了巨大的帮助验证发现,作者对他们的帮助表示感谢。应该指出的是,本出版物中讨论的研究不受作者任何财务或个人利益冲突的影响。数据可用性声明本研究过程中生成或分析的所有数据都包含在本文中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Decision Systems
Journal of Decision Systems OPERATIONS RESEARCH & MANAGEMENT SCIENCE-
CiteScore
6.30
自引率
23.50%
发文量
55
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