大数据分析在可持续制造供应链中的应用前景

IF 4.5 Q1 MANAGEMENT Benchmarking-An International Journal Pub Date : 2023-09-15 DOI:10.1108/bij-11-2022-0690
Rohit Raj, Vimal Kumar, Bhavin Shah
{"title":"大数据分析在可持续制造供应链中的应用前景","authors":"Rohit Raj, Vimal Kumar, Bhavin Shah","doi":"10.1108/bij-11-2022-0690","DOIUrl":null,"url":null,"abstract":"Purpose Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework. Design/methodology/approach Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique. Findings To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario. Research limitations/implications The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential. Practical implications In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability. Originality/value The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).","PeriodicalId":48029,"journal":{"name":"Benchmarking-An International Journal","volume":"133 1","pages":"0"},"PeriodicalIF":4.5000,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Big data analytics adaptive prospects in sustainable manufacturing supply chain\",\"authors\":\"Rohit Raj, Vimal Kumar, Bhavin Shah\",\"doi\":\"10.1108/bij-11-2022-0690\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework. Design/methodology/approach Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique. Findings To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario. Research limitations/implications The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential. Practical implications In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability. Originality/value The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).\",\"PeriodicalId\":48029,\"journal\":{\"name\":\"Benchmarking-An International Journal\",\"volume\":\"133 1\",\"pages\":\"0\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2023-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Benchmarking-An International Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/bij-11-2022-0690\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Benchmarking-An International Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/bij-11-2022-0690","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 2

摘要

尽管目前在实现大数据分析如何大大增强可持续制造供应链(SMSC)方面取得了进展,但在管理SMSC的几个操作之间的信息和信任方面,大数据操作的相关因素的故事情节存在重大差距。本研究试图通过研究从物联网(IoT)设备、群体行为参数、社交网络和生态系统框架中获得的SMSC运营中使用大数据的关键促成因素来填补这一空白。适应性前景(提高供应链绩效、打击假冒、生产力、透明度、安全和安全、资产管理和通信)是本研究首先概念化、定义的结构,然后在考虑最佳最差方法(BWM)技术的情况下,研究基于大数据分析的SMSC操作。首先,本文探讨了两种情况,一种是使用大数据分析,另一种是使用实证研究。其次,分析了大数据在解决MSC障碍中的部署以及在实现SMSC目标中的协同作用。该研究发现,在SMSC方案中,大数据分析遇到的障碍较少,收益更高。研究的局限性/启示研究结果显示,为了有效地处理业务,对供应商、分销商和物流供应商的信息和信任进行360度的观察是必不可少的。在后covid情景中,供应链从业者可以使用供应链合作伙伴的数据来开发弹性并实现可持续性。独创性/价值本研究为研究增加的独特价值在于,它将制造供应链(MSC)的数据、信任和可持续性方面联系起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Big data analytics adaptive prospects in sustainable manufacturing supply chain
Purpose Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework. Design/methodology/approach Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique. Findings To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario. Research limitations/implications The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential. Practical implications In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability. Originality/value The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).
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
CiteScore
10.40
自引率
16.10%
发文量
154
期刊介绍: Benchmarking is big news for companies committed to total quality programmes. Its enthusiastic reception by many prominent business figures has created high levels of interest in a technique which promises big rewards for co-operating partners. Yet, like total quality itself, it must be understood in its proper context, and implemented single mindedly if it is to be effective - this journal helps companies to decide if benchmarking is right for them, and shows them how to go about it successfully.
期刊最新文献
E-waste supply chain risk management: a framework considering omnichannel and circular economy The importance of warehouses in logistics outsourcing: benchmarking the perspectives of 3PL providers and shippers Finding the sweet spot in Industry 4.0 transformation: an exploration of the drivers, challenges and readiness of the Thai sugar industry The moderating effect of environmental performance on the relationship between sustainability assurance quality and firm value: a simultaneous equations approach Examining the relationships between big data analytics capability, entrepreneurial orientation and sustainable supply chain performance: moderating role of trust
×
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