Business Process Performance Prediction

on a TrackedSimulation Model

Andrei Solomon , Marin Litoiu– York University

Agenda

› Motivation› Proposed Architecture› State Prediction› Results› Conclusions

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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)

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• Estimation, prediction and integration: our contribution

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IBM WebSphere Integration Developer (WID)

IBM WebSphere Business Modeller

IBM WebSphere Process Server + Monitor

State Prediction – Linear Regression

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State Prediction - ARIMA

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Without Simulation

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KPI Prediction - Results

› (a) Err = 23%

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› (b) Err = 21% › (c) Err = 7%

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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•Thank you.•Questions?

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