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首页> 外文期刊>The international journal of logistics management >Leveraging big data analytics capabilities in making reverse logistics decisions and improving remanufacturing performance
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Leveraging big data analytics capabilities in making reverse logistics decisions and improving remanufacturing performance

机译：利用大数据分析能力，使反向物流决策和提高再制造性能

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摘要

Purpose - The study investigated the effect of big data analytics capabilities (BDACs) on reverse logistics (strategic and tactical) decisions and finally on remanufacturing performance. Design/methodology/approach - The primary data were collected using a structured questionnaire and an online survey sent to South African manufacturing companies. The data were analysed using partial least squares based structural equation modelling (PLS-SEM) based WarpPLS 6.0 software. Findings - The results indicate that data generation capabilities (DGCs) have a strong association with strategic reverse logistics decisions (SRLDs). Data integration and management capabilities (DMCs) show a positive relationship with tactical reverse logistics decisions (TRLDs). Advanced analytics capabilities (AACs), data visualisation capabilities (DVCs) and data-driven culture (DDC) show a positive association with both SRLDs and TRLDs. SRLDs and TRLDs were found to have a positive link with remanufacturing performance. Practical implications - The theoretical guided results can help managers to understand the value of big data analytics (BDA) in making better quality judgement of reverse logistics and enhance remanufacturing processes for achieving sustainability. Originality/value - This research explored the relationship between BDA, reverse logistics decisions and remanufacturing performance. The study was practice oriented, and according to the authors' knowledge, it is the first study to be conducted in the South African context.
机译：目的 - 该研究调查了大数据分析能力（BDACS）对逆向物流（战略和战术）决策的影响，最后对再制造表现进行了影响。设计/方法/方法 - 使用结构化问卷收集主要数据，并将在线调查发送到南非制造公司。基于基于结构方程建模（PLS-SEM）的WARPPLS 6.0软件，使用基于部分最小二乘来分析数据。结果 - 结果表明，数据生成功能（DGCS）与战略逆向物流决策（SRLD）具有强大的关联。数据集成和管理功能（DMCS）显示与战术反向物流决策（TRLD）的正相关关系。高级分析功能（AAC），数据可视化功能（DVC）和数据驱动的文化（DDC）显示与SRLDS和TRLD的正相关。发现SRLDS和TRLD与再制造性能有正面的联系。实际意义 - 理论指导结果可以帮助管理者了解大数据分析（BDA）的价值，使逆向物流的更好质量判断，增强实现可持续性的再制造过程。原创性/价值 - 本研究探讨了BDA，逆向物流决策与再制造性能之间的关系。这项研究是以实践为导向，并根据作者的知识，它是第一项在南非背景下进行的研究。

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  • 作者

    Surajit Bag; Sunil Luthra; Sachin Kumar Mangla; Yigit Kazancoglu;

  • 作者单位

    Department of Transport and Supply Chain Management School of Management College of Business and Economics University of Johannesburg Johannesburg South Africa and Department of Marketing and International Business School of Business and Economics North South University Dhaka Bangladesh;

    Department of Mechanical Engineering Ch Ranbir Singh State Institute of Engineering and Technology Jhajjar India;

    Jindal Global Business School O.P. Jindal Global University Haryana India and Plymouth Business School University of Plymouth Plymouth United Kingdom;

    Department of International Logistics Management Yasar University Izmir Turkey;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Africa; Information technology; Structural equation modelling; Reverse logistics; Logistics competences;

    机译：非洲;信息技术;结构方程模型;逆向物流;物流能力;

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