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Analysis of manual data collection in maintenance context

机译:维护上下文中手动数据收集分析

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The purpose of this paper is to identify details of technological, organizational and people (TOP) factors affecting maintenance technicians' use of computerized maintenance management systems (CMMS) in manual collection of asset data. Design/methodology/approach - In addition to TOP factor details, results from six case studies in Finland, India and the Caribbean are presented. Interviews and observations clarify the role of TOP factors in CMMS use in industrial maintenance. Findings - In total, 17 detailed TOP factors are identified and criteria for analyzing CMMS contexts with them are defined. Analyzing the cases with these factors reveals that technicians who collect good quality data have received good training and instructions for the CMMS, are competent,and understand how manually collected data benefits them in their own work. However, even these sites struggle with the usability of the CMMS. Research limitations/implications-The 17 TOP factors and the criteria for CMMS evaluation extend understanding on context and usability in manual data collection. Case study method does not imply the relative importance of the TOP factors, which calls for future research using quantitative methods. Practical implications - Management can use the criteria to analyze the context of manual data collection for improvements, e.g., in CMMS usability. Originality/value - Insights from industrial environments and a new way of studying contextual factors of CMMS use are presented. The results extend a data quality research framework with details to manual data collection and define the TOP factors in CMMS context.
机译:本文的目的是识别影响维护技术人员在手动收集资产数据中使用计算机化技术人员使用计算机维护管理系统(CMMS)的技术,组织和人员(顶级)因素的细节。设计/方法/方法 - 除了顶部因素细节外,还提出了芬兰,印度和加勒比的六个案例研究。访谈和观察阐明了工业维护中CMMS在CMMS中的主要因素的作用。调查结果 - 确定了17个详细的顶部因素,并定义了分析与它们的CMMS上下文的标准。分析这些因素的案例揭示了收集良好质量数据的技术人员已经获得了良好的培训和CMMS的指示,是有能力的,并了解手动收集的数据如何在自己的工作中受益。但是,即使这些网站也争取了CMMS的可用性。研究限制/含义 - 17个顶部因素和CMMS评估的标准在手动数据收集中延伸了对上下文和可用性的理解。案例研究方法并不意味着使用定量方法来呼吁未来研究的最高因素的相对重要性。实际意义 - 管理可以使用标准分析手动数据收集的背景以进行改进,例如CMMS可用性。提出了来自工业环境的原创性/价值和研究CMMS使用的上下文因素的新方法。结果将数据质量研究框架扩展到详细信息,以便手动数据收集,并在CMMS上下文中定义顶部因素。

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