Emerging BPM Technologies

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]

Copyright Kemsley Design Ltd., 2012 2

How Social Changes

Everything

Technology: Social BPM


Consumer Tools Set Expectations

l Consumption

l Participation

l Creation

l User experience

l Access anywhere

4 Copyright Kemsley Design Ltd., 2012

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)


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


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


The Changing Nature of Work

Technology: Dynamic/Adaptive Case Management


The Extremes Of Work

Routine Work

Knowledge Work


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


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


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


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


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


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


Discovering Hidden Process

Gems

Technology: Process Mining


Process Mining – Sources


BPMS Event Log Format


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



Combining All Event Logs


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 Check PO 08:38:10 08:38:15 System

8287 Create order 08:38:18 08:38:31 System

Generating A Process Model


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


More On Process Mining

Process Mining:

BPM Upside-Down

Thursday, 11:30am, Diplomat 5

Anne Rozinat

Fluxicon


Charting A Course In Uncertain

Conditions

Technology: Process Simulation


Model-Simulate-Analyze-Optimize


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


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)


Smarter Processes for

Smarter Outcomes

Technology: Predictive Analytics/Process Intelligence


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


Process + Analytics + Decisions =

Intelligent Processes

Business Process

Business Intelligence

Business Rules


Process Analytics in a BPMS


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


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


Sandy Kemsley

Kemsley Design Ltd.

email: [email protected]

blog: www.column2.com

twitter: @skemsley


Slides at www.slideshare.net/skemsley

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