1. Service Analysis and Simulation in Process Mining Doctoral Consortium, BPM14’ Arik Senderovich Advisers: Avigdor Gal and Avishai Mandelbaum 7.9.2014

2. Contents  Introduction: o Services o Operational process mining  Research Goal  Research Outline  Preliminary Work: o Queue Mining: Predicting Delays in Services  Future Work 2

3. Services economic interactions between customers and service providers that create added value in return for customer’s time, money and effort. Service management – operations, strategy, and information technology (Fitzsimmons and Fitzsimmons, 2006) What are services? 3

4. Services in Call Centers 4

5. Services in Emergency Departments 5

6. Services in Transportation 6

7. Characteristics of Services  Services require participation (of both sides), perish if not handled online and cannot be stored (lost business)  Scarce resources and uncertainty in demand formulate Queues in front of service activities: 7 Service

8. The Essence of Process Mining Process-Aware Information-System Process Modeling and Analysis Data Mining Process Mining Extract non-trivial information on business processes from event data Process Mining: Discovery, Conformance and Enhancement of Business Processes (van der Aalst, 2011) 8

9. PM: Compliance vs. Performance Illustration by Wil van der AalstWil van der Aalst 9

10. Types of Performance Analysis  We aim at data-driven: o Capacity analysis (e.g. utilization of resources, bottleneck identification) o Time analysis (e.g. predicting delays, sojourn times) o Sensitivity analysis (directions for process improvement) o Optimization with respect to some goal 10

11. Operational (Data-Driven) Analysis Event Log Model Specification Discovery Business Process Validation Events Operational Goals Data-driven Model Valid Model Operational Support Selection Improve/Predict/Recommend Model, e.g. QNet, PNet Up-to-date Event Log Event log 11

12. Mind the Gap  Queues are not treated separately from service activities in analytic and simulation performance models (e.g. for time prediction) 12 Sojourn time = Activity Time + Delay 1.Control-flow perspective 2.Resource perspective 3.Time perspective 4.Queueing perspective Agent Mean=30 seconds

13. Research Goal  Integrating service analysis techniques (e.g. Queueing Theory) into process mining by discovering analytic and simulation models from event data Service Analysis and Simulation in Process Mining 13

14. Research Outline  Queue mining: discovery and analysis of service processes via analytical or approximated queueing models  Discovery of simulation models of service processes from event data  Development of a single modeling framework for discovery, conformance and performance analysis of business processes with queues that combines: o Current process mining perspectives (e.g. control-flow, time, resources) – at the instance level o with the queueing perspective – at an aggregate level o Simulation being the common denominator 14

15. Queue Mining “Queue Mining – Predicting delays in service processes” (CAiSE14’) 15

16. Data Mining Queue Mining Service Modeling and Analysis via the queueing perspective 16 Queue mining – predicting delays in service processes (Senderovich, Weidlich, Gal, Mandelbaum, 2014)

17. Goals of the Preliminary Work  Introducing the queueing perspective to process mining  Showing that queue mining techniques improve prediction accuracy in service processes  Validating well-established results in Queueing Theory against real-world data 17

18. Steps towards our goal 1. Target a relevant operational problem: online delay prediction 2. Explore Queueing Data (Q-Log) that comes from a real-world service process 3. Consider delay prediction methods 4. Empirical evaluation of the methods 18

19. Online Delay Prediction  A target-customer arrives into the queue: Problem: Predict the waiting time of the target-customer o Important in service processes o Simple, but not too simple (can be generalized) 1 s 19

20. Steps towards our goal 1. Target an operational problem 2. Explore Queueing Data (Q-Log) that comes from a real-world service process 20

21. Queueing Data (Q-Log)  ILDUBank (Israeli Daily Updated Bank) data, coming from the Technion SEELab  Focus on a single customer type o “General Banking” (70% of bank’s customers)  Training log of 250488 delays; test log of 117709 delays (January-March, 2011) 21

22. Q-Log: Example 1 s Queue Service Abandonments 22

23. Steps towards our goal 1. Target a relevant operational problem (e.g. the online delay prediction problem) 2. Explore a real-life Q-Log 3. Predict delays via several methods: o Extensions for existing Process Mining techniques o “Classical” queueing models o Heavy-traffic approximations of queueing models 4. Empirical evaluation of the methods 23

24. Method 1: Transition System Based on van der Aalst et al., 2011 24 Delays ={45,4,56,78,…} Predictor is the average over past delays; suitable for systems in steady-state

25. Method 1 vs. Real Data 25

26. Method 2: Extending the Transition System 26 QL&Delays ={(10,45), (12,4),…} Prediction based on K-Means clustering of queue-lengths

27. Method 1 vs. Method 2 (RASE) 27

28. M3: Queue-Length Predictor Based on the G/M/s+M model (Whitt, 1999) 1 s Queue Service Abandonments 28

29. Statistical vs. Queueing Model (RASE) QLM is accurate for Moderate Load (model assumptions) 29

30. Approximations of queueing models (Heavy-Traffic)  “Classical” queueing models suffer from oversimplifying assumptions: o Exponential service times/patience o Poisson arrivals  Realistic queueing models are rarely tractable mathematically; however these models can be approximated  The idea: analyzing the queueing model under limits of its parameters 30

31. M4: Last-to-Enter-Service (Armony et al., 2009; Ibrahim and Whitt, 2009)  A target-customer arrives into the queue: The last customer to enter service waited w in queue Prediction: the target-customer will wait w 31

32. Results (Root Average Squared Error) 32  Queueing models and their approximations are valuable when mining service event logs  Current process mining techniques can be extended with queueing features  Model assumptions are of essence and must be validated before using the model

33. Operational (Data-Driven) Analysis Event Log Model Specification Discovery Business Process Validation Events Operational Goals Data-driven Model Valid Model Operational Support Selection Improve/Predict/Recommend Model, e.g. QNet, PNet Up-to-date Event Log Event log 33

34. More on Queue Mining  Work-in-progress: o Delay prediction in queueing networks (buses) / multi- class services (call centers) o Mining RTLS hospital data  Future work: Automatic model selection from a possible set of analytical queueing models o Search for analytically tractable queueing networks (somewhat analogous to searching for sound PNets) o vs. search for simple models that aggregate complex realities yet work well in practice 34

35. Towards Data-Driven Simulation Models of Services (and Business Processes with Queues) 35

36. The need to Simulate Service Processes 36  Complex service environments often result in analytically intractable queueing models  Solutions: o Approximations (e.g. snapshot principle) with (sometimes) unrealistic assumptions o Simulation

37. Discovering Simulation Models of Services 37 1. Service network structure (control-flow from both a customer and a resource perspective) 2. Building blocks (arrival rates, service times, routing probabilities) 3. Scheduling protocols (rules by which customers and resources are matched with each other) o “Mining Resource-Scheduling Protocols”, BPM14’

38. Long-Term Future Work: Business Process Simulation with Queues Business process simulation (Rozinat et al., 2009) Simulation mining of services Data-driven simulation models of business processes with queues 38

39. Business Process Simulation with Queues 39  Defining a unified modeling framework (combination of instance level and aggregate level models)  Discovery of queueing information from event logs without explicit queueing events  Discovery, conformance checking and performance analysis methods for all process mining perspectives (control-flow, resources, time, queues,…) Main challenges:

40. Thank you! sariks@tx.technion.ac.il 40