WfMC Forum Poland 2007
Tutoriel WfMC France 2007
BPM in Practice:
WfMC Vice Chair, Sr. Product Manager TIBCO
WfMC External Relations Committee
Michael zur Muehlen
Professor, Stevens Institute of Technology
WfMC Executive Director
Technical Committee Chair, WfMC,
VP R&D, Fujitsu Computer Systems
Copyright 2007, Workflow Management Coalition
WfMC Executive Director
Dr. Michael zur Muehlen
Director, Center of Excellence in
Business Process Innovation
Content Developed by
VP of Global 360 and
WfMC XPDL Working Group Chair
Copyright 2007, Workflow Management Coalition
Why Care About Analytics and Data Mining?
–When Workflow Management Systems first began
to proliferate (1990s) there was little attention paid
to the data generated by the running processes.
Most thought 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.
Business Operations Control
Historical Real Time
Event Detection Actions
ERP BPM ECM
AE Relational Database Client
Process OLAP and
Web Service Administration Business Operations
Processing by Region Over Time
– Fast analysis of process,
activity & SLA statistics, West
quality and labor information Other
– Drill down / slice and dice – 10
Explore data from different
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
– Business process –You have to know where
intelligence to look in the hypercube.
– Identify process improvement
– End to end process visibility
– Status indicators
– Queue Counts
– Goal/KPI status
Actions & Alerts
Metrics Risk Mitigation
Email and Cellphone
Web Service Call
•Why would you want to build simulation
–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
–Hence simulation plays an important role in
continuous process improvement.
•Currently in use by major banks.
Review of Analytics, BAM and Simulation
– A stream of events produced by a variety of business
process engines (ERP, Supply Chain Management, BPMS
enactment) is fed to an Analytics engine which transforms
the event data into usable information.
– A Business Activity Monitoring module updates in real time
a set of KPI indicators and using a Rules Engine applied to
these indicators, generates Alerts and Actions which inform
managers of critical situations and alter the behavior of the
– A simulation tool, using the historical data, provides What-If
analysis in support of continuous process improvement.
Integrated with a Work Force Management system it
enables optimization of staff schedules.
• But designing the what-if scenarios can be a challenging and
labor-intensive task for a specialist.
–Automatic Optimization uses Analytics and
Simulation to generate and evaluate proposals for
achieving a set of goals.
–Analysis of Process structure in conjunction
with historical data about processing delays
and resource availability permits the intelligent
exploration of improvement strategies.
–Coupled with WorkForce Management
technology, this approach helps optimize staff
Cross-train most idle,
Wait Time Reduction by feasible person
Load Balancing Alternatively
hire new one
Analyze current situation
Throughput: Unresolved Work Objects
Before Load Balancing After Load Balancing
• Number of unresolved work
• Work objects are piling up as long
objects is limited
as workitems arrive
• (Upper bound for cycle times)
• (Cycle times go up continually)
• One region in particular is
Judging the Effect: Throughput Analysis
After role addition (e.g. cross training):
After resource addition:
Review of Automated Optimization
– Optimization, using goals formulated as KPI’s, can analyze
historical information and propose what changes are likely
to help attain these goals. It can systematically evaluate the
proposed changes, using the simulation tool as a
– This can be performed in a totally automated manner, with
termination upon satisfying the goal or recognizing that no
proposed change results in further improvement.
– Staff optimization, focusing on end-to-end cycle time and
processing cost as the KPI’s, is one example of the
application of this technology.
Data Apply Predict
There are three stages:
Process versus Content Data Mining
• We focus on the data generated by typical
computer-based business processes, using
Process Intelligence as the lens through
which to view the data.
• This process view is critical in developing
a mining structure and mining models that
expose correlations between Key
Performance Indicators and other factors
such as work item attributes, resource
schedules, arrival patterns and other
external business factors.
Predicting Cycle Time
We can tell customers the expected wait time for a loan.
Can Data Mining Technology Help Us?
• A marketing campaign is expected to increase the
number of low end loan applications next month.
• Simulation-based forecasting could be used to
optimize work force management, but the
simulation model must have accurate information
about how long each step in the process takes and
using average duration values based on history
will not do.
• How can data mining provide better estimates for
durations based on line-of-business attributes of
Discovering Duration Rule
Making Predictions using Simulation
and Data Mining
• Simulation and Data Mining can both be used to make
predictions. Are they competing or complementary
• We have already discussed the role of Data Mining in the
preparation of information required for accurate simulations.
• Apart from this, there are major differences.
– The simulation model must be a sufficiently accurate
representation of the collection of processes being executed.
It can make predictions for situations not previously encountered
so long as the underlying processes have not changed.
– The Data Mining predictions are based on a statistical analysis
of what has already happened. A trained mining model assumes
the historical patterns are still valid.
• There are major differences in performance.
– Simulation is computationally intensive. It takes significant time
to obtain predictions.
– In Data Mining, the training is computationally intensive.
Once a model is trained predictions are extremely fast.
Periodic retraining may be required to keep the model accurate.
• BPMS generate event streams that provide the Analytics
Data needed for Business Activity Monitoring in real time
and Continuous Process Improvement.
• A customizable Optimizer, employing Data Mining and
Simulation tool kits, derives from the Analytics Data a
stream of recommendations for improving the business
– Redeployment of resources
– Process changes
– Optimization of business rules
• The Data Mining component supports an alternative approach
to prediction under changing business circumstances and
generates critical information for use by the Simulator. It also
provides Process Discovery capabilities useful in Process
• StatSoft, Inc. (2006). Electronic Statistics Textbook. Tulsa,
OK: StatSoft. WEB:
• Microsoft Inc. (2006) SQL Server 2005 Books Online
• Wiley, Inc. (2005) Data Mining with SQL Server 2005, Tang
• Sams Publishing (2006) Microsoft SQL Server 2005
Integration Services, Haseldon
• Wiley, Inc. (2004) Data Mining Techniques: For Marketing,
Sales and CRM, Berry
• Idea Group Publishing (2001) Data Mining and Business
Intelligence: A Guide to Productivity, Kudyba