© 2014 IBM Corporation
Towards Cognitive BPMas the Next Generation BPM Platform for Analytics-driven Business Processes
Hamid R. Motahari Nezhad, Rama Akkiraju
IBM Research, USA
Leveraging information and analytics for smarter process decisions
© 2013 IBM Corporation
Outline
Business Process Management
– Historical Perspective
– Business Process Analytics
Cognitive Systems
– IBM Watson
– Data Explosion
Cognitive BPM
– Vision
– Example Use Case
– Initial Work in Support of Cognitive BPM Vision
– Research Questions and Directions
Conclusions and Discussion
2
© 2013 IBM Corporation
BPM: Historical Perspective
3
Databases
Ba
ck
en
d
\S
ys
tem
sL
aye
r
Self-Generating Integration
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Bu
sin
es
sR
ule
sL
aye
r
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
ObjectsAnything
Business Level
Objects
Pro
ce
ss
La
ye
r
Any Process
General Workflow System and User InteractionsCalculation
Inte
rfa
ce
La
ye
r
Web
Service
Presentation Presentation
XML
API
Ba
ck
en
d
\S
ys
tem
sL
aye
r
Self-Generating Integration
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Bu
sin
es
sR
ule
sL
aye
r
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
ObjectsAnything
Business Level
Objects
Pro
ce
ss
La
ye
r
Any Process
General Workflow System and User InteractionsCalculation
Inte
rfa
ce
La
ye
r
Web
Service
PresentationPresentation PresentationPresentation
XML
API
XML
API
BPMS
TQM
General WorkflowBPR
BPM
time
ERP
WFM
EAI
‘85 ‘90 ‘95 ‘05‘00‘98
IT Innovations
Management Concepts
DatabasesDatabases
Ba
ck
en
d
\S
ys
tem
sL
aye
r
Self-Generating Integration
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Bu
sin
es
sR
ule
sL
aye
r
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
ObjectsAnything
Business Level
Objects
Pro
ce
ss
La
ye
r
Any Process
General Workflow System and User InteractionsCalculation
Inte
rfa
ce
La
ye
r
Web
Service
Presentation Presentation
XML
API
Ba
ck
en
d
\S
ys
tem
sL
aye
r
Self-Generating Integration
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Bu
sin
es
sR
ule
sL
aye
r
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
ObjectsAnything
Business Level
Objects
Pro
ce
ss
La
ye
r
Any Process
General Workflow System and User InteractionsCalculation
Inte
rfa
ce
La
ye
r
Web
Service
PresentationPresentation PresentationPresentation
XML
API
XML
API
BPMS
Ba
ck
en
d
\S
ys
tem
sL
aye
r
Self-Generating Integration
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Bu
sin
es
sR
ule
sL
aye
r
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
ObjectsAnything
Business Level
Objects
Pro
ce
ss
La
ye
r
Any Process
General Workflow System and User InteractionsCalculation
Inte
rfa
ce
La
ye
r
Web
Service
Presentation Presentation
XML
API
Ba
ck
en
d
\S
ys
tem
sL
aye
r
Self-Generating Integration
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Bu
sin
es
sR
ule
sL
aye
r
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
ObjectsAnything
Business Level
Objects
Pro
ce
ss
La
ye
r
Any Process
General Workflow System and User InteractionsCalculation
Inte
rfa
ce
La
ye
r
Web
Service
PresentationPresentation PresentationPresentation
XML
API
XML
API
BPMS
TQMTQM
General WorkflowBPR
General WorkflowBPR
BPMBPMBPM
time
ERPERP
WFMWFM
EAIEAI
‘85 ‘90 ‘95 ‘05‘00‘98
IT Innovations
Management Concepts
Ref: (partly) adapted from Ravesteyn, 2007
‘15
Social BPM
Business Process Analytics
© 2013 IBM Corporation
Business Process Analytics
Encompasses all activities that are performed on process data (logs, events, social network,
metadata, etc.) to deliver insight to process, monitor and optimize process and recommend
actions
Technically covering applying machine learning, data mining, optimization and automation
techniques on process(-related) data
4
Ref: Muehlen, 2009Ref: Forrester, 2010
© 2013 IBM Corporation
Five Types of Analytics
Existing BPA need to be designed, defined and programmed
Mostly reactive: not autonomous/learning, and proactive
5
Discovery
Analytics
Ref: Gartner
© 2012 International Business Machines Corporation7
Result of IBM Research “Grand Challenge”
On February 14, 2011, IBM Watson made history
© 2012 International Business Machines Corporation8
Businesses are “dying of thirst in an ocean of data”
1 in 2business leaders
don’t have access to data they need
83%of CIOs cited BI and analytics as part of their visionary plan
2.2Xmore likely that top
performers use business analytics
80%of the world’s data
today is unstructured
90% of the world’s data was created in the
last two years
1 Trillionconnected devices
generate 2.5 quintillion bytes
data / day
© 2012 International Business Machines Corporation9
Understands
natural language
and human
communication
Adapts and learns
from user
selections and
responses
Generates and
evaluates
evidence-based
hypothesis
Cognitive System
1
2
3 Cognitive Systems do actively
discover, learn and act
A Cognitive System offers computational capabilities typically based on Natural
Language Processing (NLP), Machine Learning (ML), and reasoning chains, on
large amount of data, which provides cognition powers that augment and scale
human expertise
Watson
© 2013 IBM Corporation
Spectrum of work: from structured to unstructured
11
Ref: Motahar-Nezhad, Swanson, 2013
Adaptive
BPM
Unstructured Information
Cognitive
BPM
© 2013 IBM Corporation
Cognitive BPM: Characteristics
A Cognitive BPM system offers the computational capability of a cognitive
system to provide analytical support for processes over structured and
unstructured information sources, and continuously discover, learn and
act to improve the process outcome
– Two pressing needs: supporting complex process decisions, and
processing large amount of data
–Analytics-driven and integrated process model definition, composition
and adaptation
• Process definition is not assumed apriori, light design, learning,
configuration/customization and improvement from experience
• The notions of lean, agile and adaptive processes supported by
insight
–Analytics supporting the adaptation of process enactment
• When, and What to adapt (cognitive BPM)
• How (Existing process adaptation techniques)
–Revisiting the main abstractions of process execution lifecycle
• Task status: notion of task completion12
© 2013 IBM Corporation
Process Definition/
Composition
Process Enactment
Process/ Environment
Sensing
Process Analytics
Proactive/ Reactive Process Changes
Cognitive BPM Lifecycle
13
Environment
Sensing
Data
sources
Data Processing/
Analytics
Process
Composition /
Enactment Update
Process
Monitoring/Analytics
IoT
© 2013 IBM Corporation
Use Case 1: Knowledge-intensive Enterprise Processes
Human-Centric, knowledge-instensive Processes in IT Services Provider
Environments (Sales Management Processes)
– Reference, descriptive processes are available, no (WFM) system supporting
the process
– The need for a business-aware automation solution for human-centric
processes
– Multiple data sources feeding process decisions
– Inbox used a work management system, in addition to phone, chat and records
in databases
– Changes to process guidelines and templates are commonplace and
communicated through email
Cognitive BPM
– Providing automation support, and analytics over process
– Ability to process and link process information from unstructured sources over
multiple channels
– Putting the business first (outcome), not the process
• Process should support more sales, through employing all analytics type:
diagnostic, predictive, prescriptive
14
© 2013 IBM Corporation
Research Supporting Cognitive BPM in Enterprise Processes
15
Health Identification and Outcome Prediction for Outsourcing Services Based on Textual Comments
Hamid R. Motahari Nezhad, Daniel B Green ia, Taiga Nakamura, and Rama Akkiraju, IEEE SCC 2014
A Win Prediction Model for IT Outsourcing Bids
Daniel Greenia, Rama Akkiraju, and Mu Qiao, IEEE SRII Global Conference 2014.
© 2013 IBM Corporation
Use Case 2: Work Assistant Example
Assume an executive admin is managing an event organization process for their
department
– Step 1: sending invite to an event to employees in their department, through
email and requests for RSVP
• Cognitive BPM (1): Q&A ability for the admin: How many have confirmed,
how many pending, how many not answered
• Cognitive BPM (2): Predictive analytics: how many will eventually RSVP?
• Cognitive BPM (3): Diagnostic analytics: why some not accepted
(customers)?
– Step 2: Ordering place, food, transportation, etc
• Cognitive BPM (1): tracking of the process steps, which vendor have
replied, which ones pending, have questions, etc.
• Cognitive BPM (2): keeping track of synchronization and consistency
(dates, amounts, numbers, etc.) among different steps
– Step 3: Pre-event steps
• Reminding people who have RSVPed
• Compiling and sending logistic information (from different steps)
16
© 2013 IBM Corporation
Research in Support of Cognitive BPM in Work Assistant Space
Task, commitment and process extraction from workers interactions over
email and chat
17
Anup K. Kalia, Hamid R. Motahari Nezhad, Claudio Bartolini, Munindar P. Singh: Monitoring
Commitments in People-Driven Service Engagements. IEEE SCC 2013: 160-167
© 2013 IBM Corporation
Research Directions
Abstractions and models for Cognitive Processes
Cognitive Process Management System
–Analytics on unstructured information to support process
understanding
–Analytics to support process adaptation, customization and
configuration
–Proactive process adaptation
Adaptive Case management, and Cognitive BPM
–Cognitive augmentation of human experts on process decisions
Teaching processes to cognitive agents
– Interactive learning where cognitive agents ask process questions
–Gradual learning through experience, and process improvement
18
© 2013 IBM Corporation
Conclusions and Discussions
We are in the beginning of a profound transformation of BPM
field due to advances in AI and Cognitive Computing
Major advances in business process analytics work so far,
however, systems need prescriptions by humans
The vision of Cognitive BPM supports a self-learning,
adaptive and analytics BPM systems that focuses on process
outcome
–Analytics-driven, learning, and proactive adaptation
–Enabling systems and human work together to achieve
better results
19