Process Mining Tutorial

Transcription

1 Anne Rozinat Christian W. Günther 26. April 2010 Web: Phone: +31(0) Copyright 2010 Fluxicon

2 Problem IT-supported business processes are complex Lack of insight into what really happens Manual process analysis: Time-consuming Incomplete Subjective 2

3 Solution Use IT log data to support process discovery Quickly visible results Objective insight Process mining is a new form of data mining: Discovers process patterns IT Analyzes complete history of processed cases to reverse-engineer the process 3

4 Fluxicon Software engineers and process mining experts Former PhD students of Prof. Wil van der Aalst at TU/e Product Development: Christian W. Günther Previous lead developer of process mining software ProM Customer Understanding: Anne Rozinat Process mining projects with ASML, Philips, and others 4

5 Goals of this tutorial Understand phases of process mining analysis Be able to get started and play around with your own data 5

6 Agenda 1. Introduction 2. Hands-on Tutorial Example Scenario Roadmap Process Mining Session 3. Conclusion 6

7 Example Scenario Customer service process Call center 1 CRM 2 Customers Front Line Back Line 7

8 Example Scenario Start Our problem: Increased costs: - More activities - Lower first call resolution rate Decreased customer satisfaction: - Net promotor score (NPS) Handle Case Call Outbound Inbound Call End Inbound Handle Outbound Expected Process 8

9 Example Scenario Process Mining: You can t control what you can t measure. Questions: 1) Is the expected process the actual process? 2) Can we find points of improvement to save cost or increase quality? 9

10 Roadmap Questions Data Extraction Event Log Construction Data Analysis Presentation 10

11 Roadmap Questions Data Extraction Event Log Construction Data Analysis Presentation 11

12 Data Extraction IT Admin of call center performs SQL Query on the CRM system CRM All cases started last month For two problematic product categories CSV Data CSV file is starting point for our Session 12

13 Roadmap Questions Data Extraction Event Log Construction Data Analysis Presentation 13

14 Event Log Construction Mapping input data onto event sequences This usually involves writing code We use Fluxicon s tool Nitro for speed-up CSV Data Event Log 14

15 Roadmap Questions Data Extraction Event Log Construction Data Analysis Presentation 15

16 Data Analysis Event log can be loaded in open source software ProM Event Log Download from Academic toolset that is great to start experimenting with process mining 16

17 Roadmap Questions Data Extraction Event Log Construction Data Analysis Presentation Focus of today s session 17

18 Hands-on Session Let s get started! NITRO ProM 18

19 Step 0 - Inspect Data Open ExampleLog.csv file in Excel and inspect its contents You can see information about Service instances Service operations Start and end times Additional data.. 19

20 Step 1- Construct Log Start Nitro and load ExampleLog.csv Assign columns as follows: Service ID Case ID Operation Activity Start Date Set column ignored End Date Timestamp (completion of activity)... Other Agent Resource Press Start conversion and Export MXML 20

21 Step 2 - Inspect Log Start ProM and open ExampleLog.mxml.gz Look at Dashboard in resulting window to see overview information about event log Select Inspector tab in same window to inspect individual service instances 21

22 Step 3 - Discover Process Choose Mining Raw ExampleLog.mxml.gz (unfiltered) Heuristics miner from menu Press start mining Look at the resulting process model Numbers in rectangles are activity frequencies Lower number at arcs is frequency of connection 22

23 Step 4 - Add Start and End Go back to log window and select Filter tab Select Advanced filter tab Select Add Artificial Start Task Log Filter from list press add selected filter press add new filter Select Add Artificial End Task Log Filter... 23

24 Step 5 - Discover Process Choose Mining Filtered ExampleLog.mxml.gz (Advanced filter) Heuristics miner from menu Press start mining 24

25 Step 6 - Compare Process Answer question No. 1: Start Is the expected process the actual process? Observations: 1. Actual process is much more complex! Handle Case Call Outbound Inbound Call Inbound Handle Outbound 2. Does not always start with calls or s (quality problem) End 25

26 Step 7 - Construct New Log Goal: We want to see whether quality problem is in front line (FL) or back line (BL) Go back to Nitro and change Agent Position field from Other to Activity Press Start conversion Select use attribute --combine all-- as activity and press Export MXML 26

27 Step 8 - Inspect New Log Open new log in ProM Select Filter tab and see how activities are distinguished between BL and FL Observation: In Start Events we can see that new cases are started in the back line (should not happen) 27

28 Step 9 - Drill Down Select Inbound Call-BL in Start events filter to focus on cases that start with this activity Go to Summary tab in log window and scroll to bottom to look at Originators Actionable result for question No. 2: Give targeted training: Agents can be asked to reuse existing service instances 28

29 Step 10 - Discover Process Go to Filter tab in log window again, choose Advanced filter tab Select + add Add Artificial Start Task Log Filter Select + add Add Artificial End Task Log Filter Choose Mining Filtered ExampleLog.mxml.gz (Advanced filter) Fuzzy miner from menu Press start mining 29

30 Step 11 - Tune Level of Detail Move the slider in the Node filter tab on the right ( significance cutoff ) up and down Observe how the process can be simplified and detailed dynamically Pull the slider down to the bottom Last step: We will now visualize how individual cases flow through process 30

31 Step 12 - Animate Process Go to Animation tab and pull Lookahead slider to the far left Press view animation Press button to start animation Observe how one service instance after another moves through the process Drag needle to end of time line and observe how most used paths get thicker and thicker 31

32 That s it! We learned how to discover a process model and found opportunities to improve service quality by targeted training Close the loop: Take action and verify results Data Extraction Event Log Construction Data Analysis Action 32

33 Further Steps ProM allows for much more: Perform quantitative analysis Explicitly check conformance of initial model Perform social network analysis... We could also include additional data sources (NPS results and Servicer data) 33

34 Quantitative Analysis Determine No. of process variants (354 unique paths) Analyze frequency and timing of activities 34

35 Conformance Initial Model Start 67% of the cases fit Inbound Call Inbound Handle Case Handle Call Outbound Outbound End 35

36 Social Network Analysis Shows case transfers between agents 36

37 Take-away Points Real processes are often more complex than you would expect There is no one right model You can take multiple views on the same data Process mining is an explorative, interactive activity 37

38 Benefits Leveraging IT log data to understand the actual process allows to: Promote good and avoid bad practices Start process discussions not from an empty sheet Provide evidence to support your point of view Reduce IT complexity by identifying the actually used process variants 38

39 Applicability Administration Health Care Banking, Insurance Product Testing Application Usage... 39

40 Contact Today s session will be made available online Watch our weblog Follow us on Twitter We d love to hear from you! anne@fluxicon.com Phone: +31(0)