How to Discover Knowledge for Improving Availability in the Manufacturing Domain?

Utz, Fabian; Neumann, Christian; Omid, Tafreschi

doi:10.24251/hicss.2018.552

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…In line with the objective of the current paper, other scientific contributions in the literature emphasize the role of “ML for OEE improvement”, mostly based on improving availability as an OEE factor (Utz et al ., 2018) (Brodny and Tutak, 2018). In addition, in order to revolutionize the way of obtaining answers to asset performance questions (Black, 2014), the need for a more detailed and in-depth analysis of OEE loss factors is growing (see Table 1).…”

Section: State Of the Artmentioning

confidence: 61%

“…2.2 DM models for OEE improvement 14 scientific contributions were found in the Scopus database by selecting (("machine learning" OR "machine learning" OR "ML") AND "OEE") in article titles, abstracts and keywords. In line with the objective of the current paper, other scientific contributions in the literature emphasize the role of "ML for OEE improvement", mostly based on improving availability as an OEE factor (Utz et al, 2018) (Brodny and Tutak, 2018). In addition, in order to revolutionize the way of obtaining answers to asset performance questions (Black, 2014), the need for a more detailed and in-depth analysis of OEE loss factors is growing (see Table 1).…”

Section: Techniques For Kpi Improvementmentioning

confidence: 72%

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A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

Lucantoni,

Antomarioni,

Ciarapica

et al. 2023

IJQRM

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PurposeThe Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.Design/methodology/approachAssociation Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.FindingsThe method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.Originality/valueThe originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

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Section: State Of the Artmentioning

confidence: 61%

Section: Techniques For Kpi Improvementmentioning

confidence: 72%

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

Lucantoni,

Antomarioni,

Ciarapica

et al. 2023

IJQRM

View full text Add to dashboard Cite

show abstract

“…These databases include the potential largest set of peer-reviewed and English literature on the topic of interest, assisting in the tasks of identifying relevant research articles in this area as much as possible. As DQ for BDA in the SF context is an essential issue in interdisciplinary research areas such as IS and IM fields [30][31][32] where contributions are published in a wide variety of outlets (see Figure 2), we also used these outlets as sources to conduct a manual search in this SLR. Additionally, we included a list of references provided in related studies on DQ in SF [8,9,23,28], to identify relevant articles that might be missing in our search.…”

Section: Selecting Databases and Outletsmentioning

confidence: 99%

Data Quality Affecting Big Data Analytics in Smart Factories: Research Themes, Issues and Methods

et al. 2021

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Recent years have seen a growing call for use of big data analytics techniques to support the realisation of symmetries and simulations in digital twins and smart factories, in which data quality plays an important role in determining the quality of big data analytics products. Although data quality affecting big data analytics has received attention in the smart factory research field, to date a systematic review of the topic of interest for understanding the present state of the art is not available, which could help reveal the trends and gaps in this area. This paper therefore presents a systematic literature review of research articles about data quality affecting big data analytics in smart factories that have been published up to 2020. We examined 31 empirical studies from our selection of papers to identify the research themes in this field. The analysis of these studies links data quality issues toward big data analytics with data quality dimensions and methods used to address these issues in the smart factory context. The findings of this systematic review also provide implications for practitioners in addressing data quality issues to better use big data analytics products to support digital symmetry in the context of smart factory.

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How to Discover Knowledge for Improving Availability in the Manufacturing Domain?

Cited by 2 publications

References 20 publications

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

Data Quality Affecting Big Data Analytics in Smart Factories: Research Themes, Issues and Methods

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