business process performance prediction on a tracked simulation model
DESCRIPTION
Business Process Performance Prediction on a Tracked Simulation Model. Andrei Solomon , Marin Litoiu – York University. Agenda. Motivation Proposed Architecture State Prediction Results Conclusions. Motivation. Business processes need to adapt to satisfy service level agreements - PowerPoint PPT PresentationTRANSCRIPT
Business Process Performance Prediction
on a TrackedSimulation Model
Andrei Solomon , Marin Litoiu– York University
Motivation
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› Business processes› need to adapt to
satisfy service level agreements› monitor› determine
changes› Execute
Motivation
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› analyzing the data› quantitative evaluation of different change decisions
› process optimization › needs forecasted key performance indicators› to asses the effect of changes
› limitations of current approach:› forecasts based on simple interpolation inaccurate predictions and wrong decisions
Benefits feedback based evolutionarchitecture that+ business agility+ more accurate simulation+ more accurate predictions+ more accurate decisions
States and KPI States: • Raw monitoring metrics
▫ Individual task durations▫ Message length and frequency▫ Number of users, etc..
KPIs:• Example: Average Process
Duration KPI• KPI definition - specifies the
method of calculation, given: ▫ current instances ▫ aggregated metrics▫ predefined set of aggregation
functions (i.e. average)▫ time period for data collection
(example: rolling 30 days = 30 days sliding window)
▫ specifies a desired target • Are defined in Modeling phase
Predictive Feedback Loop› Goal
› maintain KPIs close to the reference target
› predict short term change› to enable more effective
planning and strategic decisions
› using estimated states
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Case Study (Credit Approval)
Approved ?
N o
Y e s
R eject P rocess C lone
3 3 .3%
1 0 %
9 0 %
5 0 %
D isburse C lone
P rocess C lone 2
3 3 .3 %
3 3 .3 %
5 0 %
D isburse
P rocess
Approved ?
N o
Y e s
R eject P rocess C lone
5 0 %
5 0 %
1 0 %
9 0 %
5 0 %
D isburse C lone
5 0 %
D isburse
P rocess
Approved ?
N o
Y e s
R eject 1 0 %
9 0 %
P rocess C lone
3 3 .3 %
P rocess C lone 2
3 3 .3 %
3 3 .3 %
D isburs
P rocess
P rocess
Approve d?
N o
Y e s D isburse
R eject
• Estimation, prediction and integration: our contribution
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IBM WebSphere Integration Developer (WID)
IBM WebSphere Business Modeller
IBM WebSphere Process Server + Monitor
Conclusions & Future workConclusions:› feedback based evolution architecture› automated live monitoring › a KPI prediction module
› Forecasts the states (linear regression and ARIMA)› Uses a simulator to correlates the states
Further work and future challenges include:› validation - other estimators› modeling human resources› implement an optimization algorithm
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