Prescriptive Process Monitoring Under Uncertainty and Resource Constraints

When to intervene?
Prescriptive Process Monitoring Under
Uncertainty and Resource Constraints
Mahmoud Shoush, Marlon Dumas.
{mahmoud.shoush, marlon.dumas}@ut.ee
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Refresh your mind: Intervention slide
Source: shorturl.at/oPSTX

Motivation
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● Positive outcome:
○ Customer accepted the offer and
signed the contract.
● Negative outcome:
○ Customer declined the offer, or
launched a complaint
Triggering interventions:
• Call customer to make another
offer.

Problem Statement
For which cases in the process should we trigger an intervention and when in such a
way that the total gain of this intervention is maximized?
● Every intervention has a cost
and consumes resources, with
limited capacity.
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Existing approaches*:
● Trigger an intervention based on the probability
that a case will lead to a negative outcome.
● Fahrenkrog et al. “Fire now, fire later: alarm-based systems for prescriptive process monitoring.”
● Metzger et al . “Triggering proactive business process adaptations via online reinforcement learning.”
● Bozorgi et al. “Prescriptive process monitoring for cost-aware cycle time reduction.”
● Shoush et al. “Prescriptive process monitoring under resource constraint: A causal inference approach.”
Problem Statement
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Existing approaches limitations:
● Quantifying the quality of the prediction scores, i.e.,
Uncertainty.
Problem Statement
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Uncertainty.
Problem Statement
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● Now versus Later.

Uncertainty.
● Now versus Later.
● Infinite resource capacity.
Problem Statement
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Approach
● Main objective is to determine
when to intervene in a given
case during its execution time
to prevent or mitigate the
effect of negative outcomes
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Approach: Training phase
Event log pre-processing:
● Determine intervention T that positively impacts an
outcome Y.
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Ensemble predictive model:
● Probability of negative outcomes: avg_pred
● Malinin, A., Prokhorenkova, L., Ustimenko, A.: Uncertainty in gradient
boosting via ensembles. arXiv preprint arXiv:2006.10562(2020).
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Ensemble predictive model:
● Probability of negative outcomes: avg_pred
● Prediction uncertainty or the total uncertainty: total_uncer*.
○ Data uncertainty: outcome overlaps.
○ Knowledge uncertainty: lack of model knowledge.
● Malinin, A., Prokhorenkova, L., Ustimenko, A.: Uncertainty in gradient
boosting via ensembles. arXiv preprint arXiv:2006.10562(2020).
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Causal model:
● The effectiveness of an intervention T on an
outcome y, i.e., CATE or Uplift score.
● CATE: (Conditional average treatment effect): The
expected causal effect of the intervention:
Causal effect
(CATE)
P(-veOut | intervene=1) - P(-veOut | intervene = 0)
=
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Approach: Testing phase
● Filtering.
● Ranking.
● Resource allocation.
Main components:
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Filtering:
● avg_pred > 𝛕, e.g., 0.5.
● CATE > 0.
● Minimum total uncertainty.
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Ranking:
● Now versus Later:
○ c_avg_pred.
○ c_CATE.
○ c_total_uncer.
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Ranking:
● Now versus Later:
○ c_avg_pred, f_avg_pred.
○ c_CATE, f_CATE.
○ c_total_uncer, f_total_uncer.
● Gain: is the benefits we attain at one state
only, either current or future.
○ c_gain, f_gain
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Gain* = costWithNoIntervention - costWithIntervention

Ranking:
● How to select the best case among candidates
considering current and future state for
ongoing cases ?
○ Gain: is the benefits we attain at one state
only, either current or future.
■ c_gain
■ f_gain
○ Opportunity cost: what we lose when we
intervene now versus later.
■ opp_cost = f_gain - c_gain
● Adjusted gain: is the benefits we attain,
considering current and future states.
○ adj_gain = c_gain - opp_cost
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Evaluation
● Data-set:
○ BPI challenge 2017.
● Predictive and causal models:
○ CatBoost.
○ Orthogonal Random Forest (ORF)
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Summary
● Adding temporal constraints on when
interventions can be triggered on a case.
What we did:
What is next :
● Handle multiple types of interventions.
● Experimenting with more event logs.
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