=Paper=
{{Paper
|id=Vol-3407/paper6
|storemode=property
|title=Prescriptive Monitoring of Business Processes Under Uncertainty and Resource Constraints
|pdfUrl=https://ceur-ws.org/Vol-3407/paper6.pdf
|volume=Vol-3407
|authors=Mahmoud Shoush
|dblpUrl=https://dblp.org/rec/conf/caise/Shoush23
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==Prescriptive Monitoring of Business Processes Under Uncertainty and Resource Constraints==
https://ceur-ws.org/Vol-3407/paper6.pdf
Prescriptive Monitoring of Business Processes Under
Uncertainty and Resource Constraints
Mahmoud Shoush1
1
University of Tartu, Narva mnt 18, 51009 Tartu, Estonia
Abstract
Prescriptive process monitoring is a set of techniques that optimize business processes’ performance by
constructing intervention policies. Such policy monitors processes to identify undesired outcomes (e.g.,
customer dissatisfaction) and prescribe actions or interventions (e.g., proposing a customer discount) to
prevent their costly occurrence. However, existing techniques predominantly rely on pure predictive
models to predict undesired outcomes without considering the level of uncertainty associated with these
predictions. Consequently, interventions are triggered based on uncertain predictions, assuming they
are effective and can be immediately executed for all cases. The underpinning assumption is that a
process worker always exists to execute the intervention, e.g., calling a customer to propose a discount.
If executed, it reduces the cost of undesired outcomes. However, in practice, the resources available to
organizations, such as personnel, may be limited, and we cannot observe the effectiveness of utilizing
the intervention beforehand. To address the mentioned gaps, in this doctoral project, we aim to design
and develop an intervention policy that optimally learns with confidence, during the execution time of
the process, how to select and prioritize business cases to allocate resources to execute interventions
and thus improve a given process objective, e.g., gain. To achieve this, we integrate machine learning
methodologies. Initially, we use a predictive model with a confidence estimation method to identify
cases with a higher likelihood of undesired outcomes. Then, we use a causal inference model to estimate
the expected impact of an intervention on a case’s outcome. Finally, we develop a resource allocator that
monitors and assigns resources to selected cases when necessary.
Keywords
Prescriptive Process Monitoring, Machine Learning, Causal Inference
1. Introduction
Prescriptive process monitoring(PrPM) is a process mining subfield that uses business process
execution records (aka event logs) and machine learning techniques to monitor and improve
business processes during runtime. Hence, it is precious for businesses seeking to improve
operational efficiency, reduce costs, and enhance the quality of their products or services. By
monitoring and analyzing process data during runtime, PrPM can identify areas for improvement
and provide actionable recommendations for optimizing the process. For example, a customer
churn process where customers stop doing business with a company can lead to lost revenue
CAiSE ’23: Doctoral Consortium co-located with the 35th International Conference on Advanced Information Systems
Engineering, CAiSE ’23, June 12-16, 2023, Zaragoza, Spain
Envelope-Open mahmoud.shoush@ut.ee (M. Shoush)
GLOBE https://github.com/mshoush (M. Shoush)
Orcid 0000-0002-7423-9909 (M. Shoush)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
45
and reduced profitability. PrPM can identify churn customers (or cases) and provide prescriptive
recommendations for reducing churn, such as offering targeted promotions.
Recently, several PrPM techniques have been proposed [1, 2, 3, 4]. These techniques consist of
two components. The first is a predictive model that estimates the probability that a given case
will end up with an undesired outcome. The second is an intervention policy, which determines
if an intervention should be triggered for a given case to optimize a gain function. However,
these techniques lack two main things. Firstly, they trigger interventions when cases are likely
to end undesirably, assuming that predictions will be continuously generated, even when wholly
uncertain and regardless of the consequences of lower-quality predictions. Secondly, these
techniques assume that triggered interventions are effective and that it is possible to trigger
any number of interventions at any time. However, business processes are dynamic in reality,
and uncertainty levels may remain high. Furthermore, interventions may come with costs and
require resources, such as an employee’s time, which are limited and expensive.
In this Ph.D. project, we aim to address the abovementioned gap. We consider a problem
that includes monitoring business cases to provide timely intervention during runtime to avoid
the costly occurrence of undesired outcomes when cases unfold. We assume that resources
are bounded, and process interventions with associated costs are predefined based on domain
expertise. Specifically, we seek to answer the following research question:
Whom? When? Which?
How can we confidently identify and prioritize business cases (whom?) and provide timely (when?)
interventions (which?) considering resources are bounded to optimize a business value, e.g., gain?
In order to address the research question, this study utilizes a comprehensive approach
involving integrating three distinct machine learning algorithms: predictive, conformal, and
causal. The predictive algorithm estimates the likelihood of future outcomes for individual cases,
while also providing a measure of uncertainty associated with these predictions. By doing so, it
quantifies how certain the predictive model is with its predictions. In comparison, the conformal
algorithm is applied to convert the predictive model’s output into predictions with a guaranteed
confidence level. The causal model is then implemented to estimate the potential effectiveness
of interventions in mitigating negative outcomes for cases identified as requiring intervention
based on the preceding predictive and conformal analyses. Moreover, a resources allocator is
developed and utilized to monitor the availability of resources, allocate, and release them. The
resources allocator is used to learn which cases should be treated during runtime and when
given the available resources.
2. Related Work
A number of PrPM techniques have been proposed in the past decade. These techniques can
be organized into three groups based on how the intervention policy prescribes interventions
to improve a business value [5]. The first focuses on control flow to recommend the next best
action or activity to improve a pre-defined KPI [6, 7, 3]. The second focuses on a resource view to
guide resource allocation decisions [8, 9]. Finally, the third focuses on triggering interventions
to avoid or mitigate the effect of undesired outcomes and considers both control flow and
46
resources perspectives [1, 4, 2]. Our proposal fits in the latter; It seeks to learn policies for
triggering interventions to avoid undesired outcomes under uncertainty and finite resources.
The works in [4] and [1] utilize an outcome-oriented predictive model that focuses solely
on generating predictions related to the outcome. However, these models do not consider the
quality of the predictions or provide any measure of confidence in the predictions. In [4], they
trigger an intervention when the probability of an undesired outcome exceeds a threshold
determined manually or empirically. While in [1], a reinforcement learning agent learns when
to trigger an intervention according to parameters such as prediction scores and reliability
estimates. The work in [2] introduces a causal inference model to estimate the expected effect
of triggering an intervention to reduce the cycle time of a process, which is another problem
compared to us. However, the works in [1, 4, 2] assume that interventions with a positive
impact occur immediately even when the level of uncertainty is high and do not examine the
finite capacity of resources.
3. Proposed Solutions
To address the research problems outlined, we introduce a prescriptive process monitoring
approach, illustrated in Fig. 1. Our proposed method breaks down the research problems into
three key questions, which we will answer in the subsequent sections.
• Whom to intervene? Exploring how to identify and prioritize candidate cases for inter-
ventions with confidence.
• When to intervene? Discussing the tradeoff between triggering an intervention now
versus later.
• Which intervention? Studying which intervention is suitable for which case.
3.1. Whom to intervene?
The present study aims to develop a method for identifying cases that require intervention
by utilizing predictive, conformal, and causal models, as outlined in previous studies [10, 11].
During the training phase, the aforementioned models are trained to filter ongoing cases and
identify suitable candidates for intervention. Subsequently, the predictive model is utilized
during the testing phase to calculate the probability of an undesirable outcome for a given
ongoing case. To provide a level of confidence in the predicted outcome, a conformal algorithm
is employed to transform the predictive model outputs into a prediction set. This algorithm
returns a prediction set containing a single outcome with a confidence of 1 − 𝛼, based on a
user-defined significance level (𝛼) and a predictive model. If the predicted outcome is uncertain,
the algorithm will return a prediction set containing both desired and undesired outcomes
or an empty set. Finally, the causal model is utilized to estimate the potential impact of an
intervention on an ongoing case, by assessing the increase in the probability of a positive
outcome, commonly known as the Conditional Average Treatment Effect (𝐶𝐴𝑇 𝐸).
47
Testing phase
process event
Incomplete prefix collator Filtering
trace events prefixes
Candidate
cases
Is No
Resource
Available?
Yes
Rank and
retrieve
predictive best case Wait for
model resources
best
Complete Event log
Conformal case
trace enriching
prediction
Causal
model Allocate
Training phase Calibration phase
Resource
Figure 1: An overview of the proposed solutions.
3.2. When to intervene?
The current study involves prioritizing candidate cases from a preceding step and constructing
a resource allocator to manage and allocate resources to the most profitable cases, thereby
determining the appropriate time to trigger interventions.
We propose utilizing two measures, namely gain [10] and adjusted gain [12], to prioritize
candidate cases and determine the appropriate timing of interventions. The gain represents
the benefits acquired at the current state of an ongoing case, while the adjusted gain considers
the current and future states of ongoing cases. Both measures incorporate the costs of the
intervention and the possibility of undesired outcomes. To operationalize these measures, we
developed a resource allocator [10], which assesses the availability of resources and assigns
them to the most profitable cases. Upon selecting the most profitable case, and once a free
resource is available, we assign it to the selected case and block it for a specific duration, i.e.,
the treatment duration. The number of available resources and the time required to perform the
intervention can be determined based on domain knowledge.
48
3.3. Which intervention?
Our previous proposal assumed that each ongoing case would require a single intervention.
However, in reality, cases may require one or more interventions, each with their own costs and
benefits. Furthermore, a case may need to be intervened upon multiple times at various points
during its execution. This process of allocating resources to ongoing cases multiple times with
different interventions can be costly, making the study challenging. Additionally, identifying a
set of interventions that would positively impact outcomes in advance presents yet another
challenge. To address these challenges, we plan to extend the definition of gain and adjusted
gain from our previous works to account for multiple interventions.
4. Methodology
The research methodology employed for this Ph.D. project will be the Design Science approach,
as outlined in [13]. This approach ensures accuracy through the use of extensive literature
review and the creation of a complete evaluation benchmark with clear selection and assessment
measures. To verify the relevance of the proposed solutions, the developed techniques will
undergo a comprehensive evaluation using both real-life and simulated logs. Furthermore,
where possible, the project will include case studies with relevant organizations to ensure the
practicality and effectiveness of the proposed solutions.
5. Challenges and Future Work
Prescriptive process monitoring approaches are often confronted with difficulties in identifying
interventions that could lead to improved outcomes. Discovering or defining interventions from
execution data can be challenging, as it necessitates the identification of a causal relationship
between a specific variable and the outcome. For example, identifying a correlation between
loan approval and monthly interest rates.
Moreover, our previous proposal encounters the challenge of optimizing intervention triggers.
While our rule-based model assigns resources to ongoing cases, these resources may only be
well-utilized if the potential gain is high. Therefore, to make informed decisions on when to
allocate and when not to, we plan to develop a more intelligent resource allocator based on the
anticipated gain at each point.
To advance our approach, we aim to move away from the rule-based model and adopt a
learning-based approach in the future. To accomplish this, we plan to leverage reinforcement
learning to learn an optimal policy at runtime for deciding when to trigger interventions and
how to efficiently allocate resources.
Acknowledgement
This project is supported by the European Research Council (PIX Project). The project is under
the supervision of Prof. Marlon Dumas, who is affiliated with the University of Tartu in Estonia.
49
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