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 language | English |
|---|---|
| Pages (from-to) | 714-723 |
| Number of pages | 10 |
| Journal | Procedia Computer Science |
| Volume | 138 |
| DOIs | |
| Publication status | Published - Oct 2018 |
Fingerprint
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
Saab, N., Helms, R. W., & Zoet, M. (2018). Predictive quality performance control in BPM: proposing a framework for predicting quality anomalies. Procedia Computer Science, 138, 714-723. https://doi.org/10.1016/j.procs.2018.10.094