背景(考古学)
生产(经济)
绩效管理
性能指标
绩效衡量
生产经理
公制(单位)
制造工程
计算机科学
制造业
设备总体有效性
工程类
工程管理
运营管理
业务
营销
古生物学
生物
经济
宏观经济学
作者
Nathaniel David Smith,Yuri Hovanski,Joe Tenny,Sebastian Bergner
出处
期刊:Machines
[Multidisciplinary Digital Publishing Institute]
日期:2024-08-14
卷期号:12 (8): 555-555
标识
DOI:10.3390/machines12080555
摘要
Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last 30 years, the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine’s utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating the opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company (New York City, NY, USA), and software tools have been developed by PTC Inc. (Boston, MA, USA) to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo (Esslingen, Germany) Cyber-Physical Lab (FCPL), an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring.
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