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Business Process Intelligence Keynote
The document discusses the importance of business process management (BPM) analytics, emphasizing how valuable historical data can drive continuous process improvement and business intelligence. It outlines the evolution of BPM, the significance of analytics in real-time monitoring and predictive optimization, and various frameworks for measuring process performance. Additionally, it highlights the role of predictive simulation and the need for understanding semantics and context within BPM analytics.
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Business Process Intelligence Keynote
- 1. Process Analytics andIntelligence Semantics and other Frontiers Michael zur Muehlen, Ph.D. Center for Business Process Innovation Howe School of Technology Management Stevens Institute of Technology Hoboken NJ Michael.zurMuehlen@stevens.edu 1
- 2. Why Care AboutBPM Analytics? When Workflow Management Systems first began to proliferate (1990s) there was little attention paid to the data generated by the running processes. Most thought of this as an audit trail, not a source of information for process improvement. We now understand that the historical record contains valuable information essential to a well orchestrated continuous process improvement program. Correctly designed analytics is the starting point for providing business process intelligence. The analytics drives both real-time monitoring and predictive optimization of the executing Business Process Management System. 2
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- 4. Industrial BPM Paper Trading Order Acct. Mgmt. Phone Management Process Fax Payments E-mail Complaints Production Management Input Transparency Job Channels Automation, but only if not Types too complex / rare other regulatory requirements no economies of scale 4
- 5. Industrial Back-Office Search processes using ‣technical and ‣business criteria Display shows ‣status ‣start time ‣end time ‣instance data 5
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- 7. Trends Don’t focuson what works - focus on exceptions Search is still manual - need suggestions (Amazon for BPM) Workflow isn’t dead - not even close 7
- 8. Analytics Capabilities driveMaturity Business Process Management Maturity Strategic Alignment Governance Method IT People Culture Process Improvement Process Roles and Process Design & Process Design & Process Skills & Process Values & Plan Responsibilities Modeling Modeling Expertise Beliefs Strategy & Process Decision Making Process Process Process Education & Process Attitudes & Capability Linkage Implementation & Implementation & Processes Executions Executions Learning Behaviors Process Metrics & Process Control & Process Control & Process Collaboration Responsiveness to Process Architecture Performance Linkage & Communication Measurement Measurement Process Change Process Output Process Management Process Improvement Process Improvement Leadership Attention Standards & Innovation & Innovation Process Knowledge Measurement to Process Process Customers & Process Management Process Project & Process Project & Process Management Process Social Stakeholders Controls Program Management Program Management Leaders Networks Source: Rosemann & DeBruin 2006
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- 10. Process Measures Framework Voice of the Customer 10 Davis (2006)
- 11. Process Measures Framework CustomerNeeds Customer Issues Voice of the SLGs Customer 10 Davis (2006)
- 12. Process Measures Framework CustomerNeeds Translates into Process Objectives Customer Issues Voice of the SLGs Customer 10 Davis (2006)
- 13. Process Measures Framework CustomerNeeds Translates into Process Objectives Customer Issues Voice of the SLGs Customer s ce en flu In In flu en ce s Product Strategy Operational Strategy Business Strategy Translates into Process Efficiency Targets 10 Davis (2006)
- 14. Process Measures Framework CustomerNeeds Translates into Process Objectives Customer Issues Voice of the SLGs Customer s ce en flu In In flu en ce s Product Strategy Voice of the Process Operational Strategy Business Strategy Translates into Process Efficiency Targets 10 Davis (2006)
- 15. Process Measures Framework CustomerNeeds Translates into Process Objectives Customer Issues Measures Voice of the SLGs Customer Key Goal Indicator (KGI) s ce en flu In In flu en ce s Product Strategy Voice of the Process Operational Strategy Business Strategy Translates into Process Efficiency Targets 10 Davis (2006)
- 16. Process Measures Framework CustomerNeeds Translates into Process Objectives Customer Issues Measures Voice of the SLGs Customer Key Goal Indicator (KGI) s ce en flu In In flu en Key Performance ce s Indicator (KPI) Product Strategy Voice of the Process Operational Strategy Measures Business Strategy Translates into Process Efficiency Targets 10 Davis (2006)
- 17. Analytics 11
- 18. Business Process Analytics Process Business Process Controlling Activity Monitoring Intelligence Processing of Event Detection & Dashboards Simulation Context Events Correlation Historical Analytics Rule-based Data Mining Notification Optimization Event Bus EAI External Event ERP ECM BPM Sources Legacy Custom Enterprise IT Infrastructure Process Analytics Architecture 12
- 19. Analytics Architecture Process Controlling Historical Analytics Analysis Engine AE Database (relational or triple store) Client Process Staging and Fact and OLAP and Event Publish Dimension Process Queries Engine Tables DataMining Queue Databases UDFs, XPDL Cube Processing Reports Participants, Analysis Engine Context Data Exposes Triggers Controls Monitors UDFs DBs Web Service Administration Business Operations 13
- 20. Real Time BAM Dashboards Dashboards Alerts & Actions Status indicators Queue Counts Counters Goal/KPI status and trends 14
- 21. Real Time Actions &Alerts Dashboards Alerts & Actions KPI Evaluation Process Goals Metrics Thresholds Risk Mitigation Email and Cellphone Actions notification Web Service Call or Execute Script Process Event Action Schedule Triggers 15
- 22. Real Time Actions &Alerts Dashboards Alerts & Rules Actions Engine KPI Evaluation Process Goals Metrics Thresholds Risk Mitigation Email and Cellphone Actions notification Web Service Call or Execute Script Process Event Action Schedule Triggers 15
- 23. Real Time Real TimeManagement Dashboards Alerts & Actions Business Business-relevant Value Event occurs Value lost through Event data latency stored Analysis information delivered Action taken Data Analysis Decision Time Latency Latency Latency Infrastructure Latency Reaction Time Source: compare Hackathorn, 2002 16
- 24. Real Time Real TimeManagement Dashboards Alerts & Actions Business Business-relevant Value Event occurs Value lost through Event data latency stored Analysis information delivered Action taken Data Analysis Decision Time Latency Latency Latency Infrastructure Latency Reaction Time Acceleration through real- time Monitoring Source: compare Hackathorn, 2002 16
- 25. Real Time Real TimeManagement Dashboards Alerts & Actions Business Business-relevant Value Event occurs Value proposition of Value lost real-time through Event data Monitoring latency stored Analysis information delivered Action taken Data Analysis Decision Time Latency Latency Latency Infrastructure Latency Reaction Time Acceleration through real- time Monitoring Source: compare Hackathorn, 2002 16
- 26. Predictive Simulation Simulation Data Mining Optimization Why would you want to build simulation models? A simulation model lets you do what-ifs What if I changed my staff schedules What if I bought a faster check sorter What if the number of applications increased dramatically because of a marketing campaign The simulation results predict the effect on critical KPIs such as end-to- end cycle time and cost per processed application. Simulation plays an important role in continuous process improvement 17
- 27. Predictive Simulation Technology Simulation Data Mining Optimization Simulation is useful to make the business case for new processes Simulation models for existing processes are great for tweaking But Businesses don’t operate one process at a time Resource dependencies across many processes Questions such as staff training/assignment can’t be answered by single simulation Even experienced modelers can use some suggestions 18
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- 29. Predictive Simulation Data Mining Optimization Holy Grail? 20
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- 31. Design Process BPMN Semantics Time Semantics (e.g., (e.g., objects + Payload Semantics activity labels) connectors) (e.g., data objects + messages) Metadata (e.g. author, Layout (placement version, validity) of objects) Payload Instance Processing Behavior Semantics (e.g., (e.g., audit trail) case data + Run messages) Time 2
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- 34. Open Issues PredictingWorkflow Performance Based on Case Data Scheduling Dealing with Events outside of Workflow Scope Non-Workflow Systems Modeling Complex Event Processing Reactive/Adaptive Systems Linking Technical Metrics to (Business) Goals/Metrics Traceability 25
- 35. Thank You Michael zurMuehlen, Ph.D. Center for Business Process Innovation Howe School of Technology Management Stevens Institute of Technology Castle Point on the Hudson Hoboken, NJ 07030 Phone: +1 (201) 216-8293 Fax: +1 (201) 216-5385 E-mail: mzurmuehlen@stevens.edu Web: http://www.stevens.edu/bpm slides: www.slideshare.net/mzurmuehlen 26