Artificial Intelligence Competencies in Logistics Management: An Empirical Insight from Bahrain

IF 0.9 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE Journal of Information & Knowledge Management Pub Date : 2023-11-07 DOI:10.1142/s0219649223500594
Ahmad Saleh Shatat, Abdallah Saleh Shatat
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Abstract

This research seeks to examine the artificial intelligence (AI) competencies in logistics management by reviewing its capabilities, challenges and benefits. To increase the use of AI in logistics management, this study addresses the issues of the current technology in AI adoption in logistics management. This goal was accomplished using a systematic methodology. First, a detailed review was conducted to look at the advantages, challenges and current AI competencies. Using a survey instrument and a simple random sampling technique, the required data was collected from 44 businesses which effectively use AI in their logistical operations. The collected data gave insightful information on how AI is currently being used in logistics management. The outcome of this study shows that AI significantly affects logistics management. The study reveals notable competencies, significant challenges and major advantages of AI in managing logistics activities through the systematic analysis and synthesis of the obtained data. These findings demonstrate how AI has the potential to improve operational effectiveness, resource allocation, decision-making processes and supply chain operations in logistics management. A potential recommendation is to establish strategies and guidelines for efficient implementation and integration of AI technologies in logistics management based on the observed technology gap and the research’s findings. This will minimise the current gap and optimise the advantages of the industry’s use of AI, resulting in higher performance, cost savings and increased competitiveness for logistics business organisations.
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物流管理中的人工智能能力:来自巴林的经验洞察
本研究旨在通过回顾人工智能(AI)在物流管理中的能力、挑战和好处,来检验人工智能(AI)的能力。为了增加人工智能在物流管理中的应用,本研究解决了人工智能在物流管理中应用的当前技术问题。这一目标是通过系统的方法实现的。首先,进行了详细的审查,以查看优势,挑战和当前的人工智能能力。通过使用调查工具和简单的随机抽样技术,从44家在物流运营中有效使用人工智能的企业收集了所需的数据。收集的数据提供了有关人工智能目前如何用于物流管理的深刻信息。这项研究的结果表明,人工智能显著影响物流管理。该研究通过对获得的数据进行系统分析和综合,揭示了人工智能在管理物流活动方面的显著能力、重大挑战和主要优势。这些发现表明,人工智能有可能提高物流管理中的运营效率、资源分配、决策过程和供应链运营。一项潜在的建议是,根据观察到的技术差距和研究结果,制定战略和指导方针,以便在物流管理中有效地实施和整合人工智能技术。这将最大限度地缩小目前的差距,并优化行业使用人工智能的优势,从而提高物流业务组织的性能,节省成本并提高竞争力。
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来源期刊
Journal of Information & Knowledge Management
Journal of Information & Knowledge Management INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
2.40
自引率
25.00%
发文量
95
期刊介绍: JIKM is a refereed journal published quarterly by World Scientific and dedicated to the exchange of the latest research and practical information in the field of information processing and knowledge management. The journal publishes original research and case studies by academic, business and government contributors on all aspects of information processing, information management, knowledge management, tools, techniques and technologies, knowledge creation and sharing, best practices, policies and guidelines. JIKM is an international journal aimed at providing quality information to subscribers around the world. Managed by an international editorial board, JIKM positions itself as one of the leading scholarly journals in the field of information processing and knowledge management. It is a good reference for both information and knowledge management professionals. The journal covers key areas in the field of information and knowledge management. Research papers, practical applications, working papers, and case studies are invited in the following areas: -Business intelligence and competitive intelligence -Communication and organizational culture -e-Learning and life long learning -Electronic records and document management -Information processing and information management -Information organization, taxonomies and ontology -Intellectual capital -Knowledge creation, retention, sharing and transfer -Knowledge discovery, data and text mining -Knowledge management and innovations -Knowledge management education -Knowledge management tools and technologies -Knowledge management measurements -Knowledge professionals and leadership -Learning organization and organizational learning -Practical implementations of knowledge management
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