Evidence-Based Business Process Management
Description
Talk on evidence-based business process management delivered by Marlon Dumas at the Leading Practice Conference, Charleston, SC, USA, 5 March 2014
Transcript
Trends in Business Process Management
The Era of Evidence-Based
Business Process Management
Marlon Dumas
University of Tartu, Estonia
In collaboration with Wil van der Aalst,
Marcello La Rosa and Fabrizio Maggi
Charleston, SC, USA
5-6 March 2014
LEAD the Way
Are you watching yourself?
And your business processes?
3 months later
Back to basics…
1.
Any process is better than no process
2.
A good process is better than a bad process
3.
Even a good process can be improved
4.
Any good process eventually becomes a bad process
–
…unless continuously cared for
Michael Hammer
Business Process Intelligence (BPI)
Business
Process
Intelligence
BAM
Process
Analytics
Reports &
Dashboards
Process
Mining
Process Analytics: Dashboards
Process Cycle
Time
of Order
Processing
Process
Frequency
of Order
Processing
Process Cycle Time
of Order Processing
split up to different
Plants
ARIS (Software AG)
Process Mining
Sta rt
Re gis te r or de r
Pre pa re
s hipme nt
Event log
(Re )s e nd bill
Organization model
Ship goods
Conta ct
cus t ome r
Re ce ive paym e nt
Archive orde r
End
Process model
Disco, ProM, QPR, Celonis,
Aris PPM, Perceptive Reflect
Social network
Performance dashboards
10
Slide by Ana Karla Alves de Medeiros
Automated Process Discovery
CID
Task
Time Stamp
…
13219 Enter Loan Application
-
13219 Retrieve Applicant Data
2007-11-09 T 11:22:15
-
13220 Enter Loan Application
2007-11-09 T 11:22:40
-
13219 Compute Installments
2007-11-09 T 11:22:45
-
13219 Notify Eligibility
2007-11-09 T 11:23:00
-
13219 Approve Simple Application
2007-11-09 T 11:24:30
-
13220 Compute Installements
…
2007-11-09 T 11:20:10
2007-11-09 T 11:24:35
-
…
…
…
Notify
Rejection
Retrieve
Applicant
Data
Enter Loan
Application
Approve
Simple
Application
Compute
Installments
Notify
Eligibility
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Approve
Complex
Application
Process Mining: Value Proposition
Understand your processes as they are
• Not as you imagine them
Back your hypotheses with evidence
• Not only with intuitions and beliefs
Quantify the impact of redesign options
• Before and after
Process Mining: Where is it used?
Insurance
–Suncorp Australia
Health
–AMC Hospital, The Netherlands
–São Sebastião Hospital, Portugal
–Chania Hospital, Greece
–EHR Workflow Inc., USA
Transport
–ANA Airports, Portugal
Electronics
–Phillips, The Netherlands
Government, banking, construction … You next?
How to?
Exploratory method
–Discover models
–Visualize performance over models
–Discover and compare variants
Question-driven method
–Identify a problem in a process
–Decompose into questions
–Measure and analyze questions
The L* Method
1. Plan & Frame the Problem
2. Collect the Data
3. Analyze: Look for Patterns
4. Interpret & Create Insights
Create Business Impact
Wil van der Aalst. “Process Mining”. Springer, 2012.
1. Plan and Frame Problem
Frame the problem, e.g. as a top-level question or phenomenon
–How and why does customer experience with our order-to-cash
processes diverge (geographically, product-wise, temporally)?
–Why does the process perform poorly (bottlenecks, slow handovers)?
–Why do we have frequent defects or performance deviance?
Refine problem into:
–Sub-questions
–Identify success criteria and metrics
Identify needed resources, get buy-in, plan remaining phases
Planning step – Suncorp Case
Oftentimes „simple‟ claims take an unexpectedly long time to complete
–
To what extent does the cycle time of the claims handling process diverge?
–
What distinguishes the processing of simple claims completed on-time, and
simple claims not completed on time?
–
What `early predictors‟ can be used to determine that a given `simple‟ claim
will not be completed on time?
Team of analysts, relevant managers, IT experts
Define what a “simple claim” is.
Create awareness of the extent of the problem
2. Collect the data
Find relevant data sources
–Information systems, SAP, Oracle (Celonis), BPM Systems
–Identify process-related entities and their identifiers and map entities to
relevant processes in the process architecture
Extract traces
–Collect records associated to process entities (perhaps from multiple sources)
–Group records by process identifier to produce “traces”
–Export traces into standard format (XES)
Clean
–Filter irrelevant events
–Combine equivalent events
–Filter out traces of infrequent variants if not relevant
3. Analyze – Find Patterns
Discover the real process from the logs
Calculate process metrics
–Cycle times, waiting times, error rates
Explore frequent paths
Identify and explore ``deviance‟‟
Discover “types of cases”
–Classify e.g. by performance
Suncorp Case
Not Ideal
Expected
Performance Line
OK
OK
Good
Discriminative Model Discovery
Simple “timely” claims
Simple “slow” claims
Main result
Nailed down key activities/patterns associated with slower
performance!
WHAT’S THE CATCH?
There you are!
Process Mining: Mastering Complexity
Filter
–Filter out events (tasks)
–Filter out traces
Divide by variants (trace clustering)
–Many process models rather than one
Abstract (zoom-out)
–Focus on most frequent tasks or paths
–Identify subprocesses and collapse then down
Discover rules rather than models
Trace clustering
G. Greco et al., Discovering Expressive Process Models by Clustering Log Traces
Zoom-out: ProM’s Fuzzy Miner
Extract Subprocesses
ProM’s two-phase miner
Bose, Veerbeck & van det Aalst: Discovering Hierarchical Process Models using ProM
Chania Hospital Use Case
Pavlos Delias et al. Clustering Healthcare Processes with a Robust Approach
Chania Hospital Use Case
Most frequent paths
Pavlos Delias et al. Clustering Healthcare Processes with a Robust Approach
Chania Hospital Use Case
Trace clustering
Pavlos Delias et al. Clustering Healthcare Processes with a Robust Approach
Trace Clustering – General Principle
Do we really want models…
Or do we want understanding?
www.interactiveinsightsgroup.com
Discovering Business Rules
Decision rules
• Why does something happen at a given point in time?
Descriptive (temporal) rules
• When and why does something happen?
Discriminative rules
• When and why does something wrong happen?
Discovering Decision Rules
CID Amount Installm Salary Age Len Task
13210 20000
2000
2000 25 1 NR
13220 25000
1200
3500 35 2 NE
13221
9000
450
2500 27 2 NE
13219
8500
750
2000 25 1 ASA
13220 25000
1200
3500 35 2 ACA
13221
9000
450
2500 27 2 ASA
…
…
…
…
… … …
Decision
Miner
installment > salary
or ….
Notify
Rejection
amount ≤ 10000 or
…
Approve
Simple
Application
installment ≤ salary
or …
Notify
Eligibility
Approve
Complex
Application
amount ≥ 10000
or …
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Discovering Descriptive Rules
ProM’s DeclareMiner
Oh no! Not again!
What went wrong?
Not all rules are interesting
What is “interesting”?
–Generally not what is frequent (expected)
–But what deviates from the expected
Example:
–Every patient who is diagnosed with condition X undergoes surgery Y
But not if the have previously been diagnosed with condition Z
Interesting Rules – Deviance Mining
Something should have “normally” happened but
did not happen, why?
Something should normally not have happened
but it happened, why?
Something happens only when things go “well”
Something happens only when things go “wrong”
Now it’s better…
Maggi et al. Discovering Data-Aware Declarative Process Models from Event Logs
Discriminative Rule Mining
Bose and van der Aalst: Discovering signature patterns from event logs.
Take-Home Messages
BPM is moving from intuitionistic to evidence-based
–Like marketing in the past two decades
Convergence of BPM & BI Business Process Intelligence
Increasing number of successful case studies
Maturing landscape of process mining tools and methods
Next steps:
–More sophisticated tool support, e.g. automated deviance identification
–Predictive monitoring: detect deviance at runtime
Table of Contents
1. Introduction
2. Process Identification
3. Process Modeling
4. Advanced Process Modeling
5. Process Discovery
6. Qualitative Process Analysis
7. Quantitative Process Analysis
8. Process Redesign
9. Process Automation
10. Process Intelligence
http://fundamentals-of-bpm.org
Want to know more?
Task force on process mining (case studies, events, etc.)
–http://www.win.tue.nl/ieeetfpm/
Process mining portal and ProM toolset
–http://processmining.org
Process Mining LinkedIn group
–http://www.linkedin.com/groups/Process-Mining-1915049
BPM‟2014 Conference, Israel, 8-11 Sept. 2014
–http://bpm2014.haifa.ac.il/
Questions?
Marlon Dumas
University of Tartu
E-Mail: marlon.dumas@ut.ee
For more information:
www.fundamentals-of-bpm.org
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