Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 714-723 |
| Number of pages | 10 |
| Journal | Procedia Computer Science |
| Volume | 138 |
| DOIs | |
| Publication status | Published - 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
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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 journal › Article › Academic › peer-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
Y1 - 2018/10
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 -