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.
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Emerging BPM Technologies
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]
Copyright Kemsley Design Ltd., 2012 2
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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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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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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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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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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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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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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