business process intelligence keynote
DESCRIPTION
Keynote delivered at the 6th International Workshop on Business Process Intelligence (BPI'10), September 13, 2010, in conjunction with the BPM 2010 conference, Hoboken, NJTRANSCRIPT
Michael zur Muehlen, Ph.D.Center for Business Process InnovationHowe School of Technology ManagementStevens Institute of TechnologyHoboken [email protected]
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
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
6
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)
10
Voice of the Customer
Customer Needs
Customer Issues
SLGs
Process Measures Framework
Davis (2006)
10
Voice of the Customer
Customer Needs
Customer Issues
Process ObjectivesTranslates into
SLGs
Process Measures Framework
Davis (2006)
10
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
2
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: [email protected]: http://www.stevens.edu/bpmslides: www.slideshare.net/mzurmuehlen
Thank You
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