From the BPM Emerging Technology summit keynote that I gave at Building Business Capability 2012 in Fort Lauderdale. Provides an introduction to social BPM, dynamic case management, process simulation, predictive process analytics, and process mining.

1. Emerging Technologies
in BPM
Keynote: Emerging BPM
Techniques & Technology Summit
Building Business Capability 2012
Sandy Kemsley l www.column2.com l @skemsley

2. Emerging BPM Techniques &
Technologies Summit
l The “Hurricane Sandy” edition
l Thinking on the Job: Adaptive Case
Management in Practice [cancelled]
l Modeling and Analytics for Process
Excellence [speaker replaced]
l Process Mining: BPM Upside-Down
[speaker arriving from Europe 9pm tonight]
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3. Technology: Social BPM
How Social Changes
Everything
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4. Consumer Tools Set Expectations
l Consumption
l Participation
l Creation
l User experience
l Access anywhere
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5. Social BPM Business Benefits
l Weak ties/tacit knowledge exploitation
l Knowledge sharing
l Social feedback
l Transparency
l Participation
l Activity and decision distribution (crowd-
sourcing)
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Source: Brambilla et al, “A Notation for Social BPM”

6. Collaborative Process Modeling
l Multiple people participate in process
discovery, modeling and documentation
l Internal and external participants
l Technical and non-technical participants
l Preserves institutional memory
l Facilitates cross-silo collaboration and
innovation
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7. Process Event Streams
l Timeline of activity for social monitoring
l Process models during creation
l Process instances during execution
l Publish/subscribe model to “watch” certain
processes or event types
l Direct link to underlying process model or
instance for unsolicited participation
l Usually mobile-enabled
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8. Technology: Dynamic/Adaptive Case Management
The Changing Nature of Work
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9. The Extremes Of Work
Routine Knowledge
Work Work
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10. Goals Of Work Types
Routine Work Knowledge Work
l Efficiency l Flexibility
l Accuracy l Assist human knowledge
l Process improvement work
l Automation l Collect artifacts
l “Classic” BPM l Adaptive Case
Management (ACM) /
Production CM /
Dynamic CM
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11. Characterizing The Extremes
Routine Work Knowledge Work
l A priori process model l No a priori model
l Controlled participation l Collaboration on demand
l Automatable, especially l Little automation, but
with service integration, guided by rules and
rules and events events
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12. The Structured/Unstructured
Debate
If you can’t model
Exceptions are the
it up front, you just
new normal: every
don’t understand
process is different
the process
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13. But It’s Not That Simple
Structured Work Unstructured Work
l Some process are that l Some processes have
repeatable, especially sufficient variability that
automated processes modelling is inefficient
l Ad hoc process l Instrumentation of
exceptions already exist, unstructured processes
they’re just off the grid provides value
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14. Structure Spectrum
Structured Structured with Unstructured with Unstructured
• e.g., automated ad hoc pre-defined • e.g., investigations
regulatory process exceptions fragments
• e.g., financial back- • e.g., insurance
office transactions claims
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15. Dynamic Process Runtime
l User can add participants from own
network or recommended expert
l Non-participant can opt-in to process
l Audit trail captured within BPMS
l Eliminates uncontrolled email
processes
l Captures patterns for
process improvement
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16. Technology: Process Mining
Discovering Hidden Process
Gems
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17. Process Mining – Sources
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18. BPMS Event Log Format
Trans. ID Activity Start Time End Time Resource
8287 Enter customer 08:34:15 08:37:44 User jsmith
data
8287 Check credit 08:37:52 08:38:05 Equifax service call
1399 Enter customer 08:37:59 08:44:40 User sjones
data
8287 Enter order 08:38:09 08:38:39 ERP system call
1399 Check credit 08:44:58 08:45:06 Equifax service call
4283 Enter order 08:45:01 08:45:35 ERP system call
1399 Enter order 08:45:18 08:45:38 ERP system call
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19. Combining All Event Logs
Trans. Activity Start End Resource
ID Time Time
8287 Enter customer 08:34:15 08:37:44 User jsmith
data
8287 Create 08:34:25 08:35:55 User jsmith
customer
record
8287 Create address 08:36:12 08:37:39 User jsmith
record
8287 Check credit 08:37:52 08:38:05 Equifax service
call
8287 Enter order 08:38:09 08:38:39 ERP system call
8287 Check PO 08:38:10 08:38:15 System
8287 Create order 08:38:18 08:38:31 System
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20. Generating A Process Model
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21. Generated Model Data
Source: Fluxicon 21

22. Working With Process Mining
Results
l Actual flows, not idealized models
l Frequency and duration of each path
l Optimization:
l Detect main flows and common variations
l Detect loopbacks and other inefficiencies
l Detect wait times
l Analyze variations over time
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23. More On Process Mining
Process Mining:
BPM Upside-Down
Thursday, 11:30am, Diplomat 5
Anne Rozinat
Fluxicon
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24. Technology: Process Simulation
Charting A Course In Uncertain
Conditions
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25. Model-Simulate-Analyze-Optimize
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26. Simulation Goals
l Test and validate process models
l Establish path patterns
l Estimate end-to-end times
l Optimize resource utilization and SLA
performance across peak/slack periods
l During runtime, predict performance based
on realtime analytics
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27. Simulation in the BPM Lifecycle
Source: Lanner 27

28. More On Analytics And Simulation
Modeling and Analytics
for Process Excellence
Thursday, 10:10am, Diplomat 5
Denis Gagné
Workflow Management Coalition
(replacing Robert Shapiro)
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29. Technology: Predictive Analytics/Process Intelligence
Smarter Processes for
Smarter Outcomes
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30. Why Predictive Processes?
“Predictive analytics is not just about
forecasting what’s coming down the pike.
It’s also about keeping the bad alternative
futures from happening.”
James Kobielus, Forrester
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31. Process + Analytics + Decisions =
Intelligent Processes
Business
Process
Business Business
Rules Intelligence
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32. Process Analytics in a BPMS
l Executing
process
l Realtime
process
dashboard
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33. What You Can Do With
Process Analytics
l Information to support manual decisions
l E.g., display queue sizes to help manager to
reallocate work
l Data to trigger automated actions
l E.g., spawn fraud detection process when
series of events occur for same customer
l Predict missed SLAs
l E.g., compare history of activity timeline to
estimate overall time to completion
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34. Focus On The Goal, Not The Task
l Compare:
l Current to baseline model
l Current to historical
l Analyze:
l Process dependencies and critical path
l Simulate to identify future problems
l Act:
l Self-adjust through feedback to decisioning
l In-process user guidance
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35. Slides at www.slideshare.net/skemsley
Sandy Kemsley
Kemsley Design Ltd.
email: sandy@kemsleydesign.com
blog: www.column2.com
twitter: @skemsley
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