Keynote delivered at the 6th International Workshop on Business Process Intelligence (BPI'10), September 13, 2010, in conjunction with the BPM 2010 conference, Hoboken, NJ

1. Process Analytics and Intelligence
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
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2. Why Care About BPM 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

3. BPM 1.0:
Managing Work 3

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

6. Task Management
6

7. Trends
Don’t focus on what works - focus on exceptions
Search is still manual - need suggestions (Amazon for BPM)
Workflow isn’t dead - not even close
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8. Analytics Capabilities drive Maturity
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

9. 9

10. Process Measures Framework
Voice of the
Customer
10
Davis (2006)

11. Process Measures Framework
Customer Needs
Customer Issues
Voice of the
SLGs Customer
10
Davis (2006)

12. Process Measures Framework
Customer Needs Translates into Process Objectives
Customer Issues
Voice of the
SLGs Customer
10
Davis (2006)

13. Process Measures Framework
Customer Needs 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
Customer Needs 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
Customer Needs 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
Customer Needs 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
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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
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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
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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
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23. Real Time
Real Time Management
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 Time Management
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 Time Management
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
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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

28. Predictive
Simulation
Data Mining
Optimization
19

29. Predictive
Simulation
Data Mining
Optimization
Holy Grail?
20

30. Semantics & Context
21

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

32. Semantics? OW(L)! 23

33. Cf.: Rosemann (2008) 24

34. Open Issues
Predicting Workflow 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
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35. Thank You
Michael zur Muehlen, 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
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