emerging bpm technologies
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Sandy Kemsley l www.column2.com l @skemsley
Emerging Technologies
in BPM
Keynote: Emerging BPM
Techniques & Technology Summit
Building Business Capability 2012
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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How Social Changes
Everything
Technology: Social BPM
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Consumer Tools Set Expectations
l Consumption
l Participation
l Creation
l User experience
l Access anywhere
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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”
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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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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The Changing Nature of Work
Technology: Dynamic/Adaptive Case Management
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The Extremes Of Work
Routine Work
Knowledge Work
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Goals Of Work Types
Routine Work
l Efficiency
l Accuracy
l Process improvement
l Automation
l “Classic” BPM
Knowledge Work
l Flexibility
l Assist human knowledge
work
l Collect artifacts
l Adaptive Case
Management (ACM) /
Production CM /
Dynamic CM
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Characterizing The Extremes
Routine Work
l A priori process model
l Controlled participation
l Automatable, especially
with service integration,
rules and events
Knowledge Work
l No a priori model
l Collaboration on demand
l Little automation, but
guided by rules and
events
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The Structured/Unstructured
Debate
If you can’t model it up front, you just don’t understand
the process
Exceptions are the new normal: every process is different
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But It’s Not That Simple
Structured Work
l Some process are that
repeatable, especially
automated processes
l Ad hoc process
exceptions already exist,
they’re just off the grid
Unstructured Work
l Some processes have
sufficient variability that
modelling is inefficient
l Instrumentation of
unstructured processes
provides value
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Structure Spectrum
Structured
• e.g., automated regulatory process
Structured with ad hoc exceptions
• e.g., financial back-office transactions
Unstructured with pre-defined fragments
• e.g., insurance claims
Unstructured
• e.g., investigations
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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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Discovering Hidden Process
Gems
Technology: Process Mining
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Process Mining – Sources
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BPMS Event Log Format
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Trans. ID Activity Start Time End Time Resource
8287 Enter customer
data
08:34:15 08:37:44 User jsmith
8287 Check credit 08:37:52 08:38:05 Equifax service call
1399 Enter customer
data
08:37:59 08:44:40 User sjones
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
Combining All Event Logs
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Trans.
ID
Activity Start
Time
End
Time
Resource
8287 Enter customer
data
08:34:15 08:37:44 User jsmith
8287 Create
customer
record
08:34:25 08:35:55 User jsmith
8287 Create address
record
08:36:12 08:37:39 User jsmith
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
Generating A Process Model
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Generated Model Data
Source: Fluxicon 21
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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More On Process Mining
Process Mining:
BPM Upside-Down
Thursday, 11:30am, Diplomat 5
Anne Rozinat
Fluxicon
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Charting A Course In Uncertain
Conditions
Technology: Process Simulation
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Model-Simulate-Analyze-Optimize
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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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Simulation in the BPM Lifecycle
Source: Lanner 27
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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Smarter Processes for
Smarter Outcomes
Technology: Predictive Analytics/Process Intelligence
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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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Process + Analytics + Decisions =
Intelligent Processes
Business Process
Business Intelligence
Business Rules
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Process Analytics in a BPMS
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l Executing
process
l Realtime
process
dashboard
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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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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Sandy Kemsley
Kemsley Design Ltd.
email: sandy@kemsleydesign.com
blog: www.column2.com
twitter: @skemsley
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Slides at www.slideshare.net/skemsley
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