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Presentation from a half-day seminar at Building Business Capability in Las Vegas, 12 November 2013.
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Sandy Kemsley l www.column2.com l @skemsley
The Evolving
Business Process
Technology Landscape
Building Business Capability
Las Vegas 2013
Agenda
l Technology overview and case studies
l Social BPM
l Dynamic/adaptive case management
l Process mining
l Process simulation
l Predictive process analytics
l Preparing for emerging BPM technologies
l New ways of working
l Smarter processes
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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”
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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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Collaborative Process Modeling
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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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Process Event Streams
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Bank Of Tennessee
Mortgage Processing
Case Study: Social BPM
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Case Study: Bank Of Tennessee
l Service-focused regional bank
l Mortgage process before BPM:
l Manual, paper-based
l Long process with bottlenecks and errors
l Many exceptions, constantly changing
l Limited visibility and audit trail
l Search for social collaboration and BPM
platforms merged
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Source: Will Barrett, Bank of Tennessee, “Worksocial Pays Dividends”
A Social Mortgage Process
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Mobile Mortgages
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Bank Of Tennessee: Benefits
l ROI based on social and BPM
l 30% faster to complete process
l Reduced errors
l Process activity stream user interface
l Unified communication channel
l Faster, more efficient actions in place
l Increased adoption/decreased training
l Improved visibility and audit trail
l Critical SLAs visible for action
l Entire process within BPM Copyright Kemsley Design Ltd., 2013 15
The Changing Nature of Work
Technology: Dynamic/Adaptive Case Management
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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/Production/
Dynamic Case
Management (ACM/PCM/
DCM)
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Routine Work
Knowledge Work
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
BUT
l Ad hoc process
exceptions already exist,
they’re just off the grid
Unstructured Work
l Highly variable
processes make
modelling inefficient
BUT
l Instrumentation of
unstructured processes
provides value
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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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Dynamic Process Runtime
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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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Norwegian Food Safety Authority
Food Safety Inspections
Case Study: Adaptive Case Management
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Case Study: NFSA
l Ensuring food safety, animal/plant welfare:
l Scheduled inspections and events
l Emergency response
l Maintain food safety history
l Apply complex regulations
l Case folder with dynamic worklist
l Case = person/establishment, e.g., farm
l Tasks created dynamically as required,
manually or triggered by events
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Source: Computas
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NFSA: Dynamic Task Selection
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NFSA: Benefits
l Entire food safety history for each
establishment
l Two dynamic case management modes
l Control activity module for regular activities with
full domain data
l Emergency response module with alerts and
follow-up tasks
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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
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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
33
Generating A Process Model
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Generated Model Data
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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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AkzoNobel Decorative Paints
Procure-To-Pay Process
Case Study: Process Mining
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Case Study: AkzoNobel
l Procure-to-pay implemented in SAP
l Process variants based on country-specific
practices
l Need to improve efficiency:
l Remove undesirable variants
l Incorporate local best practices into global
standardized process
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Source: Cap Gemini, “Process Mining for Value Extraction”
Extract/Analyze SAP Log Data
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AkzoNobel Benefits
l Insight into exception processes
l Comparison between countries and
identification of best practices
l Direct insight for process improvements
l Compliance control compared to corporate
guidelines
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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
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William Grant & Sons
Distillery Vat Scheduling
Case Study: Process Simulation
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Case Study: William Grant & Sons
l Increased demand and product range
exceeded capacity
l 83 product types
l 22 process paths
l Planned $1.2M investment in additional
distilling vats
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Source: SIMUL8
Simulation Scenarios With
Variable Vats, Paths, Rules
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William Grant & Sons: Benefits
l Identified low utilization rates for production
vats
l Avoided $1.2 investment through
production rescheduling
l Planning for simulation-driven scheduling
to automate high utilization
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Smarter Processes for
Smarter Outcomes
Technology: Predictive Process Analytics
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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 52
Critical Path Perspective
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Review order
and notify
customer
Select product
Prepare
invoice
Pack product
Ship product
and invoice
ID Task Name Start Finish DurationJun 3 2012
4 5 6 7 8
1 1d04/06/201204/06/2012Review order and notify customer
3 2d06/06/201205/06/2012Select product
4 3h07/06/201207/06/2012Pack product
5 1d 3h06/06/201205/06/2012Prepare invoice
7 4h08/06/201207/06/2012Ship product and invoice
2 0d05/06/201205/06/2012Order confirmed
6 0d07/06/201207/06/2012Ready for shipping
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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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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Preparing For The Big Shift:
New Ways Of Working
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The Future Of Business
l Processes need to be:
l More engaging
l More adaptable
l More transparent
l Smarter
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Participatory Culture
l Time and resources explicitly allocated
l For collaboration
l For co-creation
l All stakeholders expected to participate
l Appropriate tools provided
l Input considered regardless of level and
technical skills of participant
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Transparency And Openness
l Allow internal users to see all information
l Set open as default, override for specific
exceptions
l Allow access to external stakeholders
l Customers, business partners should see their
own information
l Enables easier knowledge dissemination
l Provides context for problem-solving and
collaboration
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Management Style Of Trust
l Allow workers to deviate from pre-defined
workflow when appropriate
l Management must allow sufficient autonomy
l Workers must feel comfortable
creating/modifying processes
l Allow workers to collaborate with resources
of their choice
l Assign work or ask assistance
l Internal and external
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Rewards And Incentives
l Set expectations for participation
l Reward for collaboration and process
improvement
l Reward for customer service over
efficiency
l Reward teamwork over individual effort
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Preparing For The Big Shift:
Smarter Processes
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Making Processes Smarter
l Add instrumentation to processes and
harvest big data
l Apply statistical analysis
l Understand cause and effect
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Summary
The Future Of Business
l Processes need to be:
l More engaging
l More adaptable
l More transparent
l Smarter
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Sandy Kemsley
Kemsley Design Ltd.
email: [email protected]
blog: www.column2.com
twitter: @skemsley
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