LEAD the Way

The Era of Evidence-BasedBusiness Process Management

LEAD the Way

Trends in Business Process Management

Marlon Dumas

University of Tartu, Estonia

In collaboration with Wil van der Aalst, Marcello La Rosa and Fabrizio Maggi

Charleston, SC, USA5-6 March 2014

Are you watching yourself?

And your business processes?

3 months later

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

Back to basics…

Business Process

Intelligence

BAM Process Analytics

Reports & Dashboards

Process Mining

Business Process Intelligence (BPI)

Process Frequencyof Order

Processing

Process Cycle Time

of Order Processing

Process Cycle Timeof Order Processingsplit up to different

Plants

ARIS (Software AG)

Process Analytics: Dashboards

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Start

Register order

Prepareshipment

Ship goods

(Re)send bill

Receive paymentContact

customer

Archive order

End

Performance dashboards

Process model

Organization model

Social network

Event log

Slide by Ana Karla Alves de Medeiros

Disco, ProM, QPR, Celonis,Aris PPM, Perceptive Reflect

Process Mining

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CID Task Time Stamp …

13219 Enter Loan Application 2007-11-09 T 11:20:10 -

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 11:24:35 -

… … … …

Automated Process Discovery

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: Value Proposition

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?

Process Mining: Where is it used?

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

How to?

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.

The L* Method

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

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

2. Collect the data

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

OK

OK Good

Not Ideal Expected Performance

Line

Suncorp Case

Main resultNailed down key activities/patterns associated with slower performance!

Simple “timely” claims Simple “slow” claims

Discriminative Model Discovery

WHAT’S THE CATCH?

There you are!

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

Process Mining: Mastering Complexity

Trace clustering

G. Greco et al., Discovering Expressive Process Models by Clustering Log Traces

Zoom-out: ProM’s Fuzzy Miner

Bose, Veerbeck & van det Aalst: Discovering Hierarchical Process Models using ProM

Extract SubprocessesProM’s two-phase miner

Pavlos Delias et al. Clustering Healthcare Processes with a Robust Approach

Chania Hospital Use Case

Pavlos Delias et al. Clustering Healthcare Processes with a Robust Approach

Chania Hospital Use CaseMost frequent paths

Pavlos Delias et al. Clustering Healthcare Processes with a Robust Approach

Chania Hospital Use CaseTrace clustering

Trace Clustering – General Principle

www.interactiveinsightsgroup.com

Do we really want models…Or do we want understanding?

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?

CID Amount Installm Salary Age Len Task13210 20000 2000 2000 25 1 NR13220 25000 1200 3500 35 2 NE13221 9000 450 2500 27 2 NE13219 8500 750 2000 25 1 ASA13220 25000 1200 3500 35 2 ACA13221 9000 450 2500 27 2 ASA

… … … … … … …

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Decision Miner

installment > salaryor ….

installment ≤ salaryor …

amount ≤ 10000 or …

amount ≥ 10000or …

Discovering Decision Rules

Discovering Descriptive RulesProM’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”

Maggi et al. Discovering Data-Aware Declarative Process Models from Event Logs

Now it’s better…

Bose and van der Aalst: Discovering signature patterns from event logs.

Discriminative Rule Mining

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 Contents1. Introduction2. Process Identification3. Process Modeling4. Advanced Process Modeling5. Process Discovery6. Qualitative Process Analysis7. Quantitative Process Analysis8. Process Redesign9. Process Automation10. Process Intelligence

http://fundamentals-of-bpm.org

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/

Want to know more?

Marlon DumasUniversity of Tartu

E-Mail: marlon.dumas@ut.ee

For more information:www.fundamentals-of-bpm.org

Questions?

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