Analysis of manual data collection in maintenance context

Katrine Mahlamäki*, Marko Nieminen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)


Purpose: 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.

Original languageEnglish
JournalJournal of Quality in Maintenance Engineering
Publication statusPublished - 1 Jan 2019
MoE publication typeA1 Journal article-refereed


  • Data quality
  • Human factors
  • Industrial maintenance


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