工业 4.0 与精益技术在制造业中的融合:系统文献综述

Bhanu Prakash Sah, Nadia Islam Tanha, Md Arafat Sikder, S. M. Habibullah
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摘要

本系统性文献综述探讨了工业 4.0 与精益技术在制造业中的融合,随着各行业寻求提高效率和竞争力,这一主题的重要性与日俱增。通过分析 2010 年至 2023 年间发表的 156 篇同行评审期刊论文、会议论文和行业报告,本综述确定了文献中的重要主题、优势、挑战和差距。以物联网、大数据分析、人工智能(AI)和机器学习(ML)为特征的工业 4.0 为改进实时数据收集、流程自动化和高级分析提供了巨大潜力。这些技术与注重减少浪费和持续改进的精益生产原则相结合,可以提高运营效率、改善质量控制和加快响应速度。不过,审查也强调了一些挑战,包括初始成本高、需要技术熟练的劳动力,以及将新技术与现有系统集成的复杂性。尽管存在这些挑战,大量案例研究和最佳实践证明了这些集成方法的成功实施,为未来研究和实际应用提供了宝贵的启示。本综述最后提出了一些建议,以弥补已发现的差距,并利用工业 4.0 与精益技术之间的协同作用实现制造业的卓越运营。
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THE INTEGRATION OF INDUSTRY 4.0 AND LEAN TECHNOLOGIES IN MANUFACTURING INDUSTRIES: A SYSTEMATIC LITERATURE REVIEW
This systematic literature review examines the integration of Industry 4.0 and Lean technologies in manufacturing, a topic of growing importance as industries seek to enhance efficiency and competitiveness. By analyzing 156 peer-reviewed journal articles, conference papers, and industry reports published between 2010 and 2023, this review identifies vital themes, benefits, challenges, and gaps in the literature. Industry 4.0, characterized by IoT, big data analytics, artificial intelligence (AI), and machine learning (ML), offers significant potential for improving real-time data collection, process automation, and advanced analytics. When integrated with Lean manufacturing principles, which focus on waste reduction and continuous improvement, these technologies can lead to more efficient operations, better quality control, and faster response times. However, the review also highlights several challenges, including high initial costs, the need for a skilled workforce, and the complexity of integrating new technologies with existing systems. Despite these challenges, numerous case studies and best practices demonstrate the successful implementation of these integrated approaches, providing valuable insights for future research and practical applications. This review concludes with recommendations for addressing the identified gaps and leveraging the synergies between Industry 4.0 and Lean technologies to achieve operational excellence in manufacturing.
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