1.
Prescriptive Process
Monitoring for Cost-Aware
Cycle Time Reduction
Zahra Dasht Bozorgi, Irene Teinemaa,
Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy
1
International Conference on Process Mining (ICPM 2021)
November 4, 2021

2.
The Problem
• Mary is owner of a loan origination process @ a credit union.
• Speed matters to Mary! Sometimes, cases are delayed.
• There are a few actions (treatments) that Mary and
colleagues can trigger to reduce the cycle time of a case:
– Give a phone call to the customer instead of sending an
email
– Prepare two or more loan offers at once to avoid back-
and-forth
• Each intervention has a cost.
• For which cases should an intervention be triggered and
when?

3.
Baseline Solution: Predictive Monitoring
1. Train a predictive model from
an event log
2. Find cases that are predicted
to have the longest cycle time.
3. Apply the treatment to the
cases with the longest
predicted cycle time.
3

4.
Example: loan application process
4
Time
Case start
Receive application
Request further documents
Review application
Create loan offer
Check fraud
Send Offer
Wait for customer reply
Treatment point
Remaining [execution] time
Waiting
time
Case end
Cause of long cycle time
If we treat those cases that are predicted to be most delayed, we will
treat cases that take a long time, regardless of whether or not the
treatment has any effect!

5.
Causal Inference to the Rescue!
5
Event Log
Event Log
Treatment/Intervention
Causal model
TreatmentPolicy

6.
Preliminaries
How do we measure causal effects?
Causal Effect = difference between potential outcomes
Example (loan origination process)
- Treatment/intervention: calling the customer
- Outcome: shortening the cycle time
6
World 1
E[Y1] E[Y0]
World 2
Average Treatment Effect:
ATE = E[Y1 – Y0]
Conditional Average Treatment Effect:
CATE = E[Y1 – Y0|X= x]

7.
Preliminaries
id T Y Y1 Y0 Y1 – Y0
1 0 0 ? 0 ?
2 1 1 1 ? ?
3 1 0 0 ? ?
4 0 0 ? 0 ?
5 0 1 ? 1 ?
6 1 1 1 ? ?
7
Fundamental Problem of Causal Inference:
Missing Data!
Solution: Randomised AB Experiment
• Expensive
• Time-consuming
• You can do it every so often, but not all the time
Next best solution:
Uplift Modelling based on observational data
Pre-requisite:
I need to have seen the treatment being applied in a random or “sufficiently diverse” set
of situations

8.
Approach
8

9.
Log Pre-processing
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• Data Cleaning
• Feature Engineering
• K-prefix extraction
• Prefix Encoding

10.
Causal Model Construction
10
Trainingset Orthogonal Random
Forest
Input:
• Outcome Y
• Treatment T
• Features X
• Confounders W

11.
Policy Selection
11
Test set Trainedmodel Optimal policy based on
organizational constraints
Net-valuecurve

12.
Net-value Curve
1. Take all cases in the test set who are ranked in the top n%
according to the estimated treatment effect.
2. Calculate a scale factor for each segment:
N_treated/N_control
3. Calculate Qini(n) = sum(control) × scaleFactor –
sum(treated)
4. Then, gain(n) = v × Qini(n) – c × N_treated
5. Plot gain(n) for each n% policy
Where v is the value of reducing one unit of duration and c
is the cost of treating one case.

13.
Online Phase
13
live data Trainedmodel
Optimal policy
Treatment
Effects
Decision to treat

14.
Now, the cycle time of my talk is going too long…
Should I apply a treatment?
14

15.
Datasets and Experimental setup
• Baselines: Lasso and Random Forest
• Chosen because They are examples of a linear
and a non-linear model, and they perform well
on cycle time prediction for these data sets.
15
BPI Challenge 2017:
• Loan Application Process
• A mix of case and event attributes
• Balanced treatment and control groups
• Selected treatment: Call customer after offer
BPI Challenge 2019:
• Purchase-to-pay Process
• Mostly contains case attributes
• Unbalanced treatment and control groups
• Selected Treatment: Allow price change for the
item in the middle of the process.

16.
Results
16
BPIC 2017
BPIC 2019

17.
Future Work
• Optimising the time of treatment
• Handling multiple treatments:
• Multiple types of treatments, e.g. call customer vs make a second loan offer
• Discovering candidate treatments from an event log
• Conducting complementary evaluations such as simulation studies or randomised
experiments to validate the findings
17

18.
Thank you
Any treatments to reduce the cycle
time of this talk?
Zahra Dasht Bozorgi
zdashtbozorg@student.unimelb.edu.au
School of Computing and Information Systems
University of Melbourne