towards cognitive bpm as the next generation bpm platform ... · health identification and outcome...

© 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


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


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


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


Inv oices

Business Lev el

Obj ects

AFE’s

Business Level

ObjectsAnything

Business Level

Objects

Pro

ce

ss

La

ye

r

Any Process


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


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


Inv oices

Business Lev el

Obj ects

AFE’s

Business Level

ObjectsAnything

Business Level

Objects

Pro

ce

ss

La

ye

r

Any Process


Inte

rfa

ce

La

ye

r

Web

Service


XML

API

Ba

ck

en

d

\S

ys

tem

sL

aye

r


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


Inv oices

Business Lev el

Obj ects

AFE’s

Business Level

ObjectsAnything

Business Level

Objects

Pro

ce

ss

La

ye

r

Any Process


Inte

rfa

ce

La

ye

r

Web

Service


XML

API

XML

API

BPMS

Ba

ck

en

d

\S

ys

tem

sL

aye

r


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


Inv oices

Business Lev el

Obj ects

AFE’s

Business Level

ObjectsAnything

Business Level

Objects

Pro

ce

ss

La

ye

r

Any Process


Inte

rfa

ce

La

ye

r

Web

Service


XML

API

Ba

ck

en

d

\S

ys

tem

sL

aye

r


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


Inv oices

Business Lev el

Obj ects

AFE’s

Business Level

ObjectsAnything

Business Level

Objects

Pro

ce

ss

La

ye

r

Any Process


Inte

rfa

ce

La

ye

r

Web

Service


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


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


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


COGNITIVE SYSTEMS

6

© 2012 International Business Machines Corporation7

Result of IBM Research “Grand Challenge”

On February 14, 2011, IBM Watson made history


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


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


COGNITIVE BPM

10


Spectrum of work: from structured to unstructured

11

Ref: Motahar-Nezhad, Swanson, 2013

Adaptive

BPM

Unstructured Information

Cognitive

BPM


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


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


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


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.


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


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


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


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


THANK YOU!

QUESTIONS?

20

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