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Smart Trucking - Status of Digital Transformation of the Trucking Industry: A Bibliometric Analysis

机译：智能货车 - 卡车运输业的数字变换现状：一对伯格计量分析

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Industry 4.0 and the Smart Manufacturing paradigm are rapidly transforming the way business is conducted in the manufacturing industry. The underlying core technologies of the emerging digital transformation include the Internet of Things (IoT) and data analytics. While the shop floor is the main focus of these paradigms in the narrow sense, it is becoming more prevalent for other companies or business units along the digital supply network to investigate the potential to improve their processes by leveraging these technologies as well. One area that is traditionally reliant on human operators and optimization is the trucking industry. In recent years, this industry has started to explore how the above-mentioned technologies can be utilized to improve their operations, and new policies are further amplifying this process, e.g., by mandating Electronic Logging Device (ELD) to be used by commercial drivers in the USA. This development forces companies to collect digital, high-resolution data - and the question emerges how to utilize this data in the most value-adding way. In this paper, we analyze the current state of the art of digital transformation in the trucking industry by conducting a bibliometric study of recently published papers to identify the main research areas and themes in this field, the leading authors, countries, and journals in this domain. This work will provide the basis for further in-depth studies on optimization techniques and available case studies to provide theoretical input for practitioners and scholars.
机译：行业4.0和智能制造范式正在迅速改变业务在制造业的业务。新兴数字转换的潜在核心技术包括物联网（物联网）和数据分析。虽然商店地板是这些范式的主要重点在狭义上，但对于数字供应网络沿着数字供应网络的其他公司或商业单位而言，越来越普遍，以调查通过利用这些技术来改善流程的可能性。传统上依赖人类运营商和优化的一个领域是货运行业。近年来，该行业已开始探讨上述技术如何用于改善其运营，新政策进一步放大该过程，例如通过授权被商业驱动程序使用电子测井设备（ELD）美国。该开发部队公司收集数字，高分辨率数据 - 问题如何以最值加入方式利用此数据。在本文中，我们通过对最近公布的论文进行了一项生物毛管测量研究，分析了卡车运输行业的数字转型艺术状态，以确定本领域的主要研究领域和主题，领先的作者，国家和期刊领域。这项工作将为进一步深入研究优化技术和可用案例研究提供基础，为从业者和学者提供理论投入。

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