期刊
PRODUCTION PLANNING & CONTROL
卷 33, 期 1, 页码 1-19出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/09537287.2020.1817601
关键词
Productivity; criticality assessment; bottleneck; maintenance prioritization; data-driven decision-making
资金
- VINNOVA, the Swedish Energy Agency
- Swedish Transport Administration [2015-06887]
Data-driven decision support is crucial for the efficient operation of modern digitalized production systems.
Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in real-time. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldom updated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据