Predictive quality performance control in BPM: proposing a framework for predicting quality anomalies

Naef Saab, Remko W. Helms, Martijn Zoet

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Business process management (BPM) literature suggests that more than 60% of quality improvement projects fail due to factors associated with the lack of predictive quality performance control and the failure of continuously searching for quality anomalies in quality performance over time. Quality anomalies are indications of extreme performance deviation from quality expectations and requirements. The findings suggest that quality performance control in BPM is the scientific method for producing quality anomaly knowledge and signalling opportunities for informed, systematic, and continuous performance improvement. A predictive framework is proposed based on the findings.
Original languageEnglish
Pages (from-to)714-723
Number of pages10
JournalProcedia Computer Science
Volume138
DOIs
Publication statusPublished - Oct 2018

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Keywords

  • bpm,bpr,business process intelligence,design science research,deviation mining,outlier,predictive analytics,process mining,qfd,quality control,quality improvement,quality performance measurement,temporal data mining,time series analysis,tqm

Cite this

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title = "Predictive quality performance control in BPM: proposing a framework for predicting quality anomalies",
abstract = "Business process management (BPM) literature suggests that more than 60{\%} of quality improvement projects fail due to factors associated with the lack of predictive quality performance control and the failure of continuously searching for quality anomalies in quality performance over time. Quality anomalies are indications of extreme performance deviation from quality expectations and requirements. The findings suggest that quality performance control in BPM is the scientific method for producing quality anomaly knowledge and signalling opportunities for informed, systematic, and continuous performance improvement. A predictive framework is proposed based on the findings.",
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Predictive quality performance control in BPM : proposing a framework for predicting quality anomalies. / Saab, Naef; Helms, Remko W.; Zoet, Martijn.

In: Procedia Computer Science, Vol. 138, 10.2018, p. 714-723.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Predictive quality performance control in BPM

T2 - proposing a framework for predicting quality anomalies

AU - Saab, Naef

AU - Helms, Remko W.

AU - Zoet, Martijn

PY - 2018/10

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N2 - Business process management (BPM) literature suggests that more than 60% of quality improvement projects fail due to factors associated with the lack of predictive quality performance control and the failure of continuously searching for quality anomalies in quality performance over time. Quality anomalies are indications of extreme performance deviation from quality expectations and requirements. The findings suggest that quality performance control in BPM is the scientific method for producing quality anomaly knowledge and signalling opportunities for informed, systematic, and continuous performance improvement. A predictive framework is proposed based on the findings.

AB - Business process management (BPM) literature suggests that more than 60% of quality improvement projects fail due to factors associated with the lack of predictive quality performance control and the failure of continuously searching for quality anomalies in quality performance over time. Quality anomalies are indications of extreme performance deviation from quality expectations and requirements. The findings suggest that quality performance control in BPM is the scientific method for producing quality anomaly knowledge and signalling opportunities for informed, systematic, and continuous performance improvement. A predictive framework is proposed based on the findings.

KW - bpm,bpr,business process intelligence,design science research,deviation mining,outlier,predictive analytics,process mining,qfd,quality control,quality improvement,quality performance measurement,temporal data mining,time series analysis,tqm

U2 - 10.1016/j.procs.2018.10.094

DO - 10.1016/j.procs.2018.10.094

M3 - Article

VL - 138

SP - 714

EP - 723

JO - Procedia Computer Science

JF - Procedia Computer Science

SN - 1877-0509

ER -