Process Mining and Data-Driven Process Simulation
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Guest lecture delivered at the - Institut Teknologi Sepuluh on 8 December 2022. This lecture gives an overview of process mining and simulation techniques, and how the two can be used together in process improvement projects.
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Process Mining and Data-Driven Process Simulation
- 1. Professor @ University of Tartu Co-Founder @ Apromore December 8, 2022
- 2. Meet Sven - Operations Excellence Manager @ a government administration - Sven and colleagues care about having predictable processes for asset management, citizen service delivery, procurement, etc. - Every week, Sven and colleagues have different questions: - Why are there deviations with respect to the normative procurement procedures? - Why is the number of complaints related to asset maintenance increasing? - How to reduce response time for citizen requests? - should we invest in automation? - should we increase resource capacity? (where?)
- 4. Process Map Automated Process Discovery Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility 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 Installments 2007-11-09 T 11:24:35 - … … … … BPMN process model
- 8. Conformance Checking Non-compliant pathways This task should not happen here This task was skipped, it should have been executed
- 11. Business Process Simulation • Versatile quantitative analysis method for • As-is analysis • What-if analysis • In a nutshell: • Run a large number of process instances • Gather performance data (cost, time, resource usage) • Analyze the collected data via dashboards, animation and other visualizations 12
- 12. Process Simulation Model the process Define a simulation scenario Run the simulation Analyze the simulation outputs Repeat for alternative scenarios 13
- 13. Example 14
- 14. Elements of a simulation scenario 1. Processing times of activities • Fixed value • Probability distribution 15
- 15. Simulation Example 17 Exp(20m) Normal(20m, 4m) Normal(10m, 2m) Normal(10m, 2m) Normal(10m, 2m) 0m
- 16. Elements of a simulation model 1. Processing times of activities • Fixed value • Probability distribution 2. Conditional branching probabilities 3. Arrival rate of process instances and probability distribution • Typically exponential distribution with a given mean inter-arrival time • Arrival calendar, e.g. Monday-Friday, 9am-5pm, or 24/7 18
- 17. Branching probability and arrival rate 19 9:00 10:00 11:00 12:00 13:00 13:00 Arrival rate = 2 applications per hour Inter-arrival time = 0.5 hour Negative exponential distribution From Monday-Friday, 9am-5pm 0.3 0.7 0.3 35m 55m
- 18. Elements of a simulation model 1. Processing times of activities • Fixed value • Probability distribution 2. Conditional branching probabilities 3. Arrival rate of process instances • Typically exponential distribution with a given mean inter-arrival time • Arrival calendar, e.g. Monday-Friday, 9am-5pm, or 24/7 4. Resource pools 20
- 19. Resource pools • Name • Size of the resource pool • Cost per time unit of a resource in the pool • Availability of the pool (working calendar) • Examples: Clerk Credit Officer € 25 per hour € 35 per hour Mon-Fri, 9am-5pm Mon-Fri, 9am-4pm
- 20. Elements of a simulation model 1. Processing times of activities • Fixed value • Probability distribution 2. Conditional branching probabilities 3. Arrival rate of process instances and probability distribution • Typically exponential distribution with a given mean inter-arrival time • Arrival calendar, e.g. Monday-Friday, 9am-5pm, or 24/7 4. Resource pools 5. Assignment of tasks to resource pools 22
- 22. Process Simulation Model the process Define a simulation scenario Run the simulation Analyze the simulation outputs Repeat for alternative scenarios 24 ✔ ✔ ✔
- 23. Analyzing the results • The simulated logs can be analyzed using a dedicated dashboard, to focus on statistics such as case duration, waiting time, cost • Different scenarios (e.g. as-is and what-if) can be compared using multi-log dashboard or multi-log animation D
- 24. Simulation in Apromore • Process simulation in Apromore can be used to define both as-is & what-if scenarios on top of BPMN models that have been automatically discovered from a log • The simulation scenarios are then simulated to generate a simulation log per scenario. • We can then use the “Variants Analysis” template to compare as-is vs “what-of” or to compare multiple “what-if” scenarios Note: the BPMN model to be simulated may also be created from scratch or uploaded into Apromore
- 25. Pitfalls of simulation • Stochasticity • Simplifying assumptions • Data quality 27
- 26. Stochasticity • Problem • Simulation results may differ from one run to another • Solutions 1. Make the simulation timeframe long enough to cover weekly and seasonal variability, where applicable 2. Use multiple simulation runs, average results of multiple runs, compute confidence intervals 28 Multiple simulation runs Average results of multiple runs Compute confidence intervals
- 27. Simplifying assumptions of process simulation • The process model is always followed to the letter • No deviations • No workarounds • A resource only works on one task instance at a time • No multitasking • If a resource becomes available and a work item (task) is enabled, the resource will start it right away • No batching • Resources work constantly (no interruptions) • Every day is the same! • No tiredness effects • No distractions beyond “stochastic” ones 29
- 28. Data quality • Problem • Simulation results are only as trustworthy as the input data • Solutions: 1. Rely as little as possible on “guesstimates”. Use input analysis where possible: • Derive simulation scenario parameters from numbers in the scenario • Use statistical tools to check fit the probability distributions • Simulate the “as is” scenario and cross-check results against actual observations 2. Or discover the simulation model automatically from event logs using data-driven simulation 30
- 29. Data-Driven Construction of Digital Process Twins Simulation Model Discoverer Process Constraints or Process Model Enterprise System Process Change Specification Simulation Engine Predicted Performance Simulation Model
Editor's Notes
- Other distributions allowed by BIMP: Uniform, Triangular, Log-normal, Gamma… Continuous uniform: the value is uniformely distributed in the interval between a and b. A value can be any between a and b Discrete uniform distribution: a finite number of values are equally likely to be observed Triangular: In probability theory and statistics, the triangular distribution is a continuous probability distribution with lower limit a, upper limit b and mode c, where a < b and a ≤ c ≤ b. Log-normal: In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable X {\displaystyle X} is log-normally distributed, then Y = ln ( X ) {\displaystyle Y=\ln(X)} has a normal distribution. Likewise, if Y {\displaystyle Y} has a normal distribution, then X = exp ( Y ) {\displaystyle X=\exp(Y)} has a log-normal distribution. Gamma: In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions. The common exponential distribution and chi-squared distribution are special cases of the gamma distribution.
- Resource pools in this context is not to be confused with pools in BPMN. A resource pool here is simply a set of resources who can perform a given activity. A resource pool for example can be a role or a group. In a way resource pool in the context of simulation is closer to the notion of “lane” in BPMN and typically lanes in BPMN will become resource pools when we simulate the process.
- To specify a resource pool we need of course to give it a name. We also have to specify the size of the pool, meaning how many resources belong to it. Finally, we --- A resource pool in the context of simulation is specified by means of its name; the size of the resource pool, meaning the number of instances of that resource type; and the cost per time unit, say per hour or per day or per month of a given resource of that pool; and finally the availability of the pool, meaning during what calendar are the resources in the pool available. An example of a resource pool could be for example a clerk, which corresponds to a role within the organisation. That clerk has a cost of 25 Euros an hour and works according to a calendar, Monday to Friday, nine to five. Another example could be a credit officer with a cost of each credit officer of 25 per hour and a calendar of Monday to Friday, nine to five. In some simulation tools it is possible to define the cost and the calendar at the level of each individual resource rather than define it at the level of the resource pool. However in subsequent examples we’ll concentrate on the case where all the resources in a given pool have the same cost and abide to the same calendar.
- In our case for example we might say that check credit history is performed by a clerk, same for check income sources, whereas assess application, make a credit offer, and notify rejection are performed by a credit officer, while receive customer feedback is performed by the system.
- https://www.if4it.com/core-domain-knowledge-critical-foundation-successful-design-thinking/ https://towardsdatascience.com/minimum-viable-domain-knowledge-in-data-science-5be7bc99eca9