Developing Maintenance Key Performance Indicators From Maintenance Work Order Data

Brundage, Michael P.; Morris, Katherine C.; Sexton, Thurston; Moccozet, Sascha; Hoffman, Michael

doi:10.1115/msec2018-6492

Cited by 22 publications

(19 citation statements)

References 8 publications

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“…For a long time, there has been a lack of consensus about which methodologies to use for PIs selection (Kumar et al , 2013). Researchers have, therefore, suggested different approaches for maintenance PIs selection (see for example: Stefanovic et al , 2017; Fangucci et al , 2017; Brundage et al , 2018; Wijesinghe and Mallawarachchi, 2019). In spite of this, the most common approach in practice is to select PIs based on measures already available, which neglects its relevance (Simões et al , 2016).…”

Section: Theorymentioning

confidence: 99%

Performance indicators for measuring the effects of Smart Maintenance

Lundgren

Bokrantz

Skoogh

2020

IJPPM

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PurposeThe purpose of this study is to ensure productive, robust and sustainable production systems and realise digitalised manufacturing trough implementation of Smart Maintenance – “an organizational design for managing maintenance of manufacturing plants in environments with pervasive digital technologies”. This paper aims to support industry practitioners in selecting performance indicators (PIs) to measure the effects of Smart Maintenance, and thus facilitate its implementation.Design/methodology/approachIntercoder reliability and negotiated agreement were used to analyse 170 maintenance PIs. The PIs were structurally categorised according to the anticipated effects of Smart Maintenance.FindingsCompanies need to revise their set of PIs when changing manufacturing and/or maintenance strategy (e.g. reshape the maintenance organisation towards Smart Maintenance). This paper suggests 13 categories of PIs to facilitate the selection of PIs for Smart Maintenance. The categories are based on 170 PIs, which were analysed according to the anticipated effects of Smart Maintenance.Practical implicationsThe 13 suggested categories bring clarity to the measuring potential of the PIs and their relation to the Smart Maintenance concept. Thereby, this paper serves as a guide for industry practitioners to select PIs for measuring the effects of Smart Maintenance.Originality/valueThis is the first study evaluating how maintenance PIs measure the anticipated effects of maintenance in digitalised manufacturing. The methods intercoder reliability and negotiated agreement were used to ensure the trustworthiness of the categorisation of PIs. Such methods are rare in maintenance research.

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Section: Theorymentioning

confidence: 99%

Performance indicators for measuring the effects of Smart Maintenance

Lundgren

Bokrantz

Skoogh

2020

IJPPM

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“…One of the most exciting recent developments is in natural language processing to enable work order texts to be read and analyzed more efficiently by computers. Examples of recent work in this area include [57,58,59,60]. This work is complemented by developments in semantic knowledge representation technologies to capture data and contextual relationships between data.…”

Section: Applicable Research and Technologiesmentioning

confidence: 99%

Where Do We Start? Guidance for Technology Implementation in Maintenance Management for Manufacturing

Brundage

Sexton

Hodkiewicz

et al. 2019

Journal of Manufacturing Science and Engineering

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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 the 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 system 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–system 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.

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“…Once a bad actor was detected, the information in the work order description was necessary for understanding the nature of the identified chronic failures. Brundage, Sexton, Morris, Moccozet, and Hoffman (2018) proposed a set of maintenance key performance indicators (KPI's) based on the characterization of the data from the CMMS/EAM and the possibilities in the data characterizations after incorporating results from NLP on the unstructured fields. Lukens and Naik (2019) proposed a methodology for practitioners to consistently utilize the SMRP Best Practices metrics with respect to data quality considerations, leveraging NLP technologies where appropriate.…”

Section: Asset Performance Metrics and Benchmarkingmentioning

confidence: 99%

Best Practices Framework for Improving Maintenance Data Quality to Enable Asset Performance Analytics

Lukens¹,

Naik²,

Saetia³

et al. 2019

PHM_CONF

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Field maintenance data is often captured manually and is prone to have incomplete and inaccurate information in the structured fields. However, unstructured fields captured through work order planning, scheduling, and execution contains a wealth of historical information about asset performance, failure patterns, and maintenance strategies. The prevalent data quality issues in maintenance data need to be understood and processed in order to extract actionable intelligence. This paper describes a best practices framework for measuring and improving data quality, developed through years of research and working with 120+ process and manufacturing organizations. The framework enables evaluating and executing analytics by identifying strengths in the data. It determines where and how asset performance measures such as benchmarking metrics, reliability measures, and bad actor identification can be evaluated with confidence. Missing or inconsistent information can be extracted from the unstructured fields using natural language processing (NLP) techniques to bridge gaps in the analysis. While the NLP algorithms make historical data usable for some analytics, the best practices identify improvements in the work process of capturing data, thereby improving future quality. A feedback on data quality indicators completes the loop to sustain improvements.

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Developing Maintenance Key Performance Indicators From Maintenance Work Order Data

Cited by 22 publications

References 8 publications

Performance indicators for measuring the effects of Smart Maintenance

Performance indicators for measuring the effects of Smart Maintenance

Where Do We Start? Guidance for Technology Implementation in Maintenance Management for Manufacturing

Best Practices Framework for Improving Maintenance Data Quality to Enable Asset Performance Analytics

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