1. Business process improvement based on event logs Pnina Soffer

2. My research: business processes beyond control flow Abstracting from BP modeling languages using a state-based goal-oriented process view. Addressing various process-related problems in a coherent manner – Goal reachability assessment – Effect of data deficiencies on business processes – “How to think” when creating a process model – Flexibility and impact of process changes – Learning from experience – Compliance checking and diagnosis

3. Process mining Mines event logs of an information system, to: – Understand and represent the actual process – Check for compliance with required standards and procedures – Identify performance bottlenecks – Identify improvement opportunities

4. Example 1: Identifying process workarounds and related problems Workarounds are intentional deviations from procedures Identifying and analyzing workarounds can guide process improvement Exploratory study in 5 organizations revealed 6 workaround types and problems related to them Mining event logs of the relevant IS enabled quantifying 4 of these types

5. Workaround diagnosis: example results Organizat ion Process Number of instances % instances with workarounds by type Bypassing Post-hoc updates Role violation Post-hoc reporting 1 PR approval 36885.1%1.3%2.7%5.9% 2 PR approval 692053.2%8.8%22.3%12.0% Purchase ordering 42116.8%7.2%24.4%12.6% 3 PR approval 2128975.3%25.0%3.5%68.1% Purchase ordering 521711.9%4.8%9.0%4.1% Average in all processes30.5%9.4%12.4%20.5%

6. Example 2: Business process learning Learning: the ability to induce from past experience in order to improve present and future performance. – Avoid repeating past mistakes – Know how to replicate past successful executions Resource used: log of past process executions Main idea: to learn based on – What has been done (process path) – In what situations (context) – What has been achieved (outcome, performance)

7. What should be considered as “context”? How to identify and recommend the best process path for a given context? Two main questions:

8. Aims to maximize the chances for a successful sale Has a lot of information about each customer: – Approximate age, clothing style, height, figure, eye color, hair color, state of mind… Needs to use this information for an effective classification to determine how to approach each customer Context identification: The sales person’s problem

9. An effective classification of process instances supports the selection of an appropriate path to achieve better outcomes Facing a large amount of contextual information, how to identify the relevant factors to establish an effective classification? Solution approach: feature selection and decision tree growing, identifying context variables and conditions that help predicting action-result combinations The general problem

10. Identifying best process paths Classifying process instances by their context. Mining each context-based group separately, to identify decisions that have the highest effect on the outcomes achieved. Automated generation of decision rules accordingly, recommending options to select at given situations.

11. Current status Performance% instances% improvement BeforeAfter Excellent20.423.314.4 Acceptable75.876.4 Unacceptable4.61.076 Total delivered instances92.892.7 Undelivered instances6.46.5 Rejected instances0.8 Current solution evaluated by simulation with promising results. Still ongoing research

12. Ongoing research directions Learning in medical processes: – High dimensional context – Compliance with clinical constraints – Processes without a clear completion point Addressing exceptions and low-volume variations Providing real time decision support Main difficulty: a need for high quality logs to experiment with.