ArticleJournal of manufacturing science and engineering2019
Where do we start? Guidance for technology implementation in maintenance management for manufacturing.
Article in Journal of manufacturing science and engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Enhancing facility maintenance using knowledge-based BIM systems.Scientific reports · 2025Article
- Assessment of a Novel Position Verification Sensor to Identify and Isolate Robot Workcell Health Degradation.Journal of manufacturing science and engineering · 2021Article
- A Data-Driven Framework for Team Formation for Maintenance Tasks.International journal of prognostics and health management · 2021Article
- Measurement and Evaluation for Prognostics and Health Management (PHM) for Manufacturing Operations - Summary of an Interactive Workshop Highlighting PHM Trends.International journal of prognostics and health management · 2021Article
- Rethinking Maintenance Terminology for an Industry 4.0 Future.International journal of prognostics and health management · 2021Article
- Organizing Tagged Knowledge: Similarity Measures and Semantic Fluency in Structure Mining.Journal of mechanical design (New York, N.Y. : 1990) · 2020Article
- Foundations of information governance for smart manufacturing.Smart and sustainable manufacturing systems · 2020Article
- Nestor: A Tool for Natural Language Annotation of Short Texts.Journal of research of the National Institute of Standards and Technology · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Recent efforts in Smart Manufacturing (SM) have proven quite effective at elucidating system behavior using sensing systems, communications and computational platforms, along with statistical methods to collect and analyze real-time performance data. However, how do you effectively select where and when to implement these technology solutions within manufacturing operations? Furthermore, how do you account for the human-driven activities in manufacturing when inserting new technologies? Due to a reliance on human problem solving skills, today's maintenance operations are largely manual processes without wide-spread automation. The current state-of-the-art maintenance management systems and out-of-the-box solutions do not directly provide necessary synergy between human and technology, and many paradigms ultimately keep the human and digital knowledge systems separate. Decision makers are using one or the other on a case-by-case basis, causing both human and machine to cannibalize each other's function, leaving both disadvantaged despite ultimately having common goals. A new paradigm can be achieved through a hybridized systems approach - where human intelligence is effectively augmented with sensing technology and decision support tools, including analytics, diagnostics, or prognostic tools. While these tools promise more efficient, cost-effective maintenance decisions, and improved system productivity, their use is hindered when it is unclear what core organizational or cultural problems they are being implemented to solve. To explicitly frame our discussion about implementation of new technologies in maintenance management around these problems, we adopt well established error mitigation frameworks from human factors experts - who have promoted human-systems integration for decades - to maintenance in manufacturing. Our resulting tiered mitigation strategy guides where and how to insert SM technologies into a human-dominated maintenance management process.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.