Discovering rules that describe not what happens but why it happens
Evidence-Based Business Process Management
Trends in Business Process Management
The Era of Evidence-Based
Business Process Management
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?
Back to basics…
Any process is better than no process
A good process is better than a bad process
Even a good process can be improved
Any good process eventually becomes a bad process
…unless continuously cared for
Business Process Intelligence (BPI)
Process Analytics: Dashboards
Process Cycle Time
of Order Processing
split up to different
ARIS (Software AG)
Re gis te r or de r
Pre pa re
s hipme nt
(Re )s e nd bill
cus t ome r
Re ce ive paym e nt
Archive orde r
Disco, ProM, QPR, Celonis,
Aris PPM, Perceptive Reflect
Slide by Ana Karla Alves de Medeiros
Automated Process Discovery
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
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?
–AMC Hospital, The Netherlands
–São Sebastião Hospital, Portugal
–Chania Hospital, Greece
–EHR Workflow Inc., USA
–ANA Airports, Portugal
–Phillips, The Netherlands
Government, banking, construction … You next?
–Visualize performance over models
–Discover and compare variants
–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:
–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
–Collect records associated to process entities (perhaps from multiple sources)
–Group records by process identifier to produce “traces”
–Export traces into standard format (XES)
–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
Discriminative Model Discovery
Simple “timely” claims
Simple “slow” claims
Nailed down key activities/patterns associated with slower
Process Mining: Mastering Complexity
–Filter out events (tasks)
–Filter out traces
Divide by variants (trace clustering)
–Many process models rather than one
–Focus on most frequent tasks or paths
–Identify subprocesses and collapse then down
Discover rules rather than models
G. Greco et al., Discovering Expressive Process Models by Clustering Log Traces
Do we really want models…
Or do we want understanding?
Discovering Business Rules
• Why does something happen at a given point in time?
Descriptive (temporal) rules
• When and why does something happen?
• When and why does something wrong happen?
What went wrong?
Not all rules are interesting
What is “interesting”?
–Generally not what is frequent (expected)
–But what deviates from the expected
–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.
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
–More sophisticated tool support, e.g. automated deviance identification
–Predictive monitoring: detect deviance at runtime
Table of Contents
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
Want to know more?
Task force on process mining (case studies, events, etc.)
Process mining portal and ProM toolset
Process Mining LinkedIn group
BPM‟2014 Conference, Israel, 8-11 Sept. 2014
University of Tartu
For more information: