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
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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。
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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).
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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.
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