Business Process Intelligence Keynote

Michael zur Muehlen, Ph.D.Center for Business Process InnovationHowe School of Technology ManagementStevens Institute of TechnologyHoboken NJMichael.zurMuehlen@stevens.edu

Process Analytics and Intelligence Semantics and other Frontiers

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Why Care About BPM Analytics?

When Workflow Management Systems first began to proliferate (1990s) there was little attention paid to the data generated by the running processes.

Most thought of this as an audit trail, not a source of information for process improvement.

We now understand that the historical record contains valuable information essential to a well orchestrated continuous process improvement program.

Correctly designed analytics is the starting point for providing business process intelligence.

The analytics drives both real-time monitoring and predictive optimization of the executing Business Process Management System.

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3

BPM 1.0:Managing Work

Paper

4

Industrial BPM

Input Channels

OrderManagement

Process

Job Types

Production ManagementTransparencyAutomation, but only if not

too complex / rareother regulatory requirementsno economies of scale

Phone

Fax

E-mail

Trading

Acct. Mgmt.

Payments

Complaints

Search processes using‣technical and‣business criteria

Display shows ‣status‣start time‣end time‣instance data

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Industrial Back-Office

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Task Management

TrendsDon’t focus on what works - focus on exceptions

Search is still manual - need suggestions (Amazon for BPM)

Workflow isn’t dead - not even close

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Analytics Capabilities drive Maturity

Governance Method IT People CultureStrategic Alignment

Process Roles and Responsibilities

Process Design & Modeling

Process Skills & Expertise

Process Values & Beliefs

Process Improvement Plan

Decision Making Processes

Process Implementation &

ExecutionsProcess Education &

LearningProcess Attitudes &

BehaviorsStrategy & Process Capability Linkage

Process Management Standards

Process Improvement & Innovation Process Knowledge Leadership Attention

to ProcessProcess Output Measurement

Process Metrics & Performance Linkage

Process Control & Measurement

Process Collaboration & Communication

Responsiveness to Process ChangeProcess Architecture

Process Management Controls

Process Project & Program Management

Process Management Leaders

Process Social Networks

Process Customers & Stakeholders

Business Process Management Maturity

Process Design & Modeling

Process Implementation &

Executions

Process Improvement & Innovation

Process Control & Measurement

Process Project & Program Management

Source: Rosemann & DeBruin 2006

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10

Voice of the Customer

Process Measures Framework

Davis (2006)

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Voice of the Customer

Customer Needs

Customer Issues

SLGs

Process Measures Framework

Davis (2006)

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Voice of the Customer

Customer Needs

Customer Issues

Process ObjectivesTranslates into

SLGs

Process Measures Framework

Davis (2006)

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Voice of the Customer

Customer Needs

Customer Issues

Process Objectives

Process Efficiency Targets

Translates into

Business Strategy

Operational Strategy

Influe

nces

Product Strategy

Influences

SLGs

Process Measures Framework

Davis (2006)

Translates into

10

Voice of the Customer

Customer Needs

Customer Issues

Process Objectives

Process Efficiency Targets

Translates into

Voice of the Process

Business Strategy

Operational Strategy

Influe

nces

Product Strategy

Influences

SLGs

Process Measures Framework

Davis (2006)

Translates into

10

Voice of the Customer

Customer Needs

Customer Issues

Process Objectives

Key Goal Indicator (KGI)

Process Efficiency Targets

Measures

Translates into

Voice of the Process

Business Strategy

Operational Strategy

Influe

nces

Product Strategy

Influences

SLGs

Process Measures Framework

Davis (2006)

Translates into

10

Voice of the Customer

Customer Needs

Customer Issues

Process Objectives

Key Performance Indicator (KPI)

Key Goal Indicator (KGI)

Process Efficiency Targets

Measures

Translates into

Voice of the Process

Business Strategy

Operational Strategy Measures

Influe

nces

Product Strategy

Influences

SLGs

Process Measures Framework

Davis (2006)

Translates into

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Analytics

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Process Analytics Architecture

Enterprise IT Infrastructure

ERP ECM BPM

Legacy

EAI

Custom

Business Process Analytics

Business Activity Monitoring

Dashboards

Process Intelligence

Simulation

Data Mining

Optimization

Event Detection & Correlation

Event Bus

Process Controlling

Historical Analytics Rule-based

Notification

External Event Sources

Processing of Context Events

Analytics Architecture

Rep

orts

Par

ticip

ants

,U

DFs

, XP

DL

Publish

AE Database (relational or triple store)

ProcessOLAP andDataMining Databases

ProcessEngine

Administration

Con

trols

Ana

lysi

s E

ngin

e

Exp

oses

UD

Fs

Trig

gers

Cub

e P

roce

ssin

g

Mon

itors

DB

s

Queries

Web Service

Con

text

Dat

a

Client

Business Operations

Process Controlling

Historical Analytics

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Analysis Engine

Staging and Event Queue

Fact and Dimension

Tables

BAM Dashboards

Status indicators

Queue Counts

Counters

Goal/KPI status and trends

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Real Time

Dashboards

Alerts & Actions

Actions & Alerts

ProcessMetrics

GoalsThresholds

Risk Mitigation

KPI Evaluation

Action Schedule

Web Service Callor

Execute Script

Actions

Email and Cellphone notification

Process Event

Triggers

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Real Time

Dashboards

Alerts & Actions

Actions & Alerts

ProcessMetrics

GoalsThresholds

Risk Mitigation

KPI Evaluation

Action Schedule

Web Service Callor

Execute Script

Actions

Rules Engine

Email and Cellphone notification

Process Event

Triggers

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Real Time

Dashboards

Alerts & Actions

Real Time Management

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Source: compare Hackathorn, 2002

BusinessValue

TimeDataLatency

AnalysisLatency

DecisionLatency

Reaction Time

Infrastructure Latency

Business-­relevant Event occurs

Event data stored Analysis

information delivered Action taken

Value lost through latency

Real Time

Dashboards

Alerts & Actions

Real Time Management

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Source: compare Hackathorn, 2002

BusinessValue

TimeDataLatency

AnalysisLatency

DecisionLatency

Reaction Time

Infrastructure Latency

Business-­relevant Event occurs

Event data stored Analysis

information delivered Action taken

Acceleration through real-­time Monitoring

Value lost through latency

Real Time

Dashboards

Alerts & Actions

Real Time Management

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Source: compare Hackathorn, 2002

BusinessValue

TimeDataLatency

AnalysisLatency

DecisionLatency

Reaction Time

Infrastructure Latency

Business-­relevant Event occurs

Event data stored Analysis

information delivered Action taken

Acceleration through real-­time Monitoring

Value lost through latency

Value proposition of

real-­time Monitoring

Real Time

Dashboards

Alerts & Actions

SimulationWhy would you want to build simulation models?

A simulation model lets you do what-ifs

What if I changed my staff schedules

What if I bought a faster check sorter

What if the number of applications increased dramatically because of a marketing campaign

The simulation results predict the effect on critical KPIs such as end-to-end cycle time and cost per processed application.

Simulation plays an important role in continuous process improvement

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Predictive

Simulation

Data Mining

Optimization

Simulation Technology

Simulation is useful to make the business case for new processes

Simulation models for existing processes are great for tweaking

But Businesses don’t operate one process at a time

Resource dependencies across many processes

Questions such as staff training/assignment can’t be answered by single simulation

Even experienced modelers can use some suggestions

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Predictive

Simulation

Data Mining

Optimization

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Predictive

Simulation

Data Mining

Optimization

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Predictive

Simulation

Data Mining

Optimization

Holy Grail?

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Semantics & Context

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Design Time

Run Time

Process Semantics (e.g., activity labels)

BPMN Semantics (e.g., objects +

connectors) Payload Semantics (e.g., data objects +

messages)

Payload Instance Semantics (e.g.,

case data + messages)

Processing Behavior (e.g., audit trail)

Layout (placement of objects)

Metadata (e.g. author, version, validity)

Semantics? OW(L)! 23

24Cf.: Rosemann (2008)

Open IssuesPredicting Workflow Performance Based on Case Data

Scheduling

Dealing with Events outside of Workflow Scope

Non-Workflow Systems

Modeling Complex Event Processing

Reactive/Adaptive Systems

Linking Technical Metrics to (Business) Goals/Metrics

Traceability

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Michael zur Muehlen, Ph.D.Center for Business Process InnovationHowe School of Technology ManagementStevens Institute of TechnologyCastle Point on the HudsonHoboken, NJ 07030Phone: +1 (201) 216-8293Fax: +1 (201) 216-5385E-mail: mzurmuehlen@stevens.eduWeb: http://www.stevens.edu/bpmslides: www.slideshare.net/mzurmuehlen

Thank You

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