Evidence map›Paper›PMID 39439469›Full record

ArticleJournal of manufacturing science and engineering2019

Where do we start? Guidance for technology implementation in maintenance management for manufacturing.

Michael P Brundage, Thurston Sexton, Melinda Hodkiewicz, K C Morris, Jorge Arinez, Farhad Ameri, Jun Ni, Guoxian Xiao

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. A Data-Driven Framework for Team Formation for Maintenance Tasks.International journal of prognostics and health management · 2021
    Article
  4. Article
  5. Rethinking Maintenance Terminology for an Industry 4.0 Future.International journal of prognostics and health management · 2021
    Article
  6. Organizing Tagged Knowledge: Similarity Measures and Semantic Fluency in Structure Mining.Journal of mechanical design (New York, N.Y. : 1990) · 2020
    Article
  7. Foundations of information governance for smart manufacturing.Smart and sustainable manufacturing systems · 2020
    Article
  8. Nestor: A Tool for Natural Language Annotation of Short Texts.Journal of research of the National Institute of Standards and Technology · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Michael P BrundageNational Institute of Standards and Technology Gaithersburg, MD 20814, USA.
Thurston SextonNational Institute of Standards and Technology Gaithersburg, MD 20814, USA.
Melinda HodkiewiczUniversity of Western Australia Crawley WA 6009, AUS.
K C MorrisNational Institute of Standards and Technology Gaithersburg, MD 20814, USA.
Jorge ArinezGM Research and Development Center Warren, MI 48090, USA.
Farhad AmeriTexas State University San Marcos, TX 78666, USA.
Jun NiUniversity of Michigan Ann Arbor, MI 48109, USA.
Guoxian XiaoGM Research and Development Center Warren, MI 48090, USA.

Funding

Intramural NIST DOC 9999-NIST
6 · The paper itself

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

PMID39439469
PMCPMC11494710

What OpenQuestion holds

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Registered trials

None linked

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.