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Machine criticality assessment for productivity improvement Smart maintenance decision support

机译：生产力提高智能维护决策支持机器关键性评估

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摘要

Purpose - The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity. Design/methodology/approach - An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety. Findings - The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization. Originality/value - Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.
机译：目的 - 本文的目的是通过智能维护规划提高生产率，包括生产力作为维护组织的目标之一。因此，该论文的目标是调查现有的机器关键性评估，并确定关键性评估工具的组成部分，以提高生产率。设计/方法/方法 - 本文采用了嵌入式多案研究设计。六种不同的案件选自三个多国制造公司运营的六种不同的生产站点。数据收集以访谈，焦点小组和档案记录的形式进行。在每个案例中都收集了多个数据来源。这些案例包括不同的生产布局，如加工，装配和铸造，确保数据变化。调查结果 - 本文的主要发现是对制造公司如何评估机器关键性和计划维护活动的更深入了解。实证结果表明，关于现有的关键性评估工具缺乏信任。因此，确定了维护组织内的必要变化，以提高生产率，这些是技术进步，即动态和数据驱动方法和组织变更，即在执行维护优先级时使用系统的透视方法。原创/值 - 机器关键性评估研究很少见，尤其是实证研究。本文的原创性在于在智能维护规划中进行的实证研究，以实现生产力改进。此外，识别机器关键性评估的组件对于高效规划维护活动的研究和行业同样重要。

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  • 来源
    《Work study》 |2019年第5期|858-878|共21页
  • 作者

    Maheshwaran Gopalakrishnan; Anders Skoogh; Antti Salonen; Martin Asp;

  • 作者单位

    Department of Industrial and Materials Science Chalmers University of Technology Gothenburg Sweden;

    Department of Industrial and Materials Science Chalmers University of Technology Gothenburg Sweden;

    School of Innovation Design and Engineering Malardakns University Eskilstuna Sweden;

    Volvo Group Truck Operations Skovde Sweden;

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

    Productivity; Bottleneck;

    机译：生产率;瓶颈;

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