Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

Bozorgi, Zahra Dasht; Teinemaa, Irene; Dumas, Marlon; Rosa, Marcello La; Polyvyanyy, Artem

doi:10.1109/icpm53251.2021.9576853

Cited by 20 publications

(20 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though not explicitly addressing fairness, several proposals for applying causal machine learning techniques in the context of process mining have been made. For example, Bozorgi et al [17,18] looked at discovering causal rules from event logs as well as taking some form of cost into account when making suggestions for intervention in running cases as part of a prescriptive process mining approach. By making the causalities explicit its may be feasible to include fairness constraints into decisions.…”

Section: Algorithmic Fairnessmentioning

confidence: 99%

Responsible Process Mining

Mannhardt

2022

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

The prospect of data misuse negatively affecting our life has lead to the concept of responsible data science. It advocates for responsibility to be built, by design, into data management, data analysis, and algorithmic decision making techniques such that it is made difficult or even impossible to intentionally or unintentionally cause harm. Process mining techniques are no exception to this and may be misused and lead to harm. Decisions based on process mining may lead to unfair decisions causing harm to people by amplifying the biases encoded in the data by disregarding infrequently observed or minority cases. Insights obtained may lead to inaccurate conclusions due to failing to considering the quality of the input event data. Confidential or personal information on process stakeholders may be leaked as the precise work behavior of an employee can be revealed. Process mining models are usually white-box but may still be difficult to interpret correctly without expert knowledge hampering the transparency of the analysis. This chapter structures the topic of responsible process mining based on the FACT criteria: Fairness, Accuracy, Confidentiality, and Transparency. For each criteria challenges specific to process mining are provided and the current state of the art is briefly summarized.

show abstract

Section: Algorithmic Fairnessmentioning

confidence: 99%

Responsible Process Mining

Mannhardt

2022

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

show abstract

“…Metzger et al [13] use an RL algorithm to automatically learn when to trigger process adaptations based on the predictions. Recent work proposed a framework that recommends when to apply an intervention (treatment) to an ongoing case to decrease its cycle time by building causal models [2]. Orthogonal random forests trained on historical traces is used to estimate the effect of a treatment (or intervention) on the reduction in cycle time, given the current state of a case.…”

Section: Related Workmentioning

confidence: 99%

Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning

Agarwal¹,

Gupta²,

Sindhgatta³

et al. 2022

Preprint

View full text Add to dashboard Cite

Recommending a sequence of activities for an ongoing case requires that the recommendations conform to the underlying business process and meet the performance goal of either completion time or process outcome. Existing work on next activity prediction can predict the future activity but cannot provide guarantees of the prediction being conformant or meeting the goal. Hence, we propose a goal-oriented next best activity recommendation. Our proposed framework uses a deep learning model to predict the next best activity and an estimated value of a goal given the activity. A reinforcement learning method explores the sequence of activities based on the estimates likely to meet one or more goals. We further address a real-world problem of multiple goals by introducing an additional reward function to balance the outcome of a recommended activity and satisfy the goal. We demonstrate the effectiveness of the proposed method on four real-world datasets with different characteristics. The results show that the recommendations from our proposed approach outperform in goal satisfaction and conformance compared to the existing state-of-the-art next best activity recommendation techniques.

show abstract

“…Recently, various PrPM methods have been proposed [2,6,14,18]. These methods, however, implement intervention policies based on predictions of negative outcomes without considering the uncertainty of these predictions.…”

Section: Supported By the European Research Council (Pix Project)mentioning

confidence: 99%

When to Intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints

Shoush

Dumas

2022

Lecture Notes in Business Information Processing

Self Cite

View full text Add to dashboard Cite

Prescriptive process monitoring approaches leverage historical data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process’s performance. A centerpiece of a prescriptive process monitoring method is its intervention policy: a decision function determining if and when to trigger an intervention on an ongoing case. Previous proposals in this field rely on intervention policies that consider only the current state of a given case. These approaches do not consider the tradeoff between triggering an intervention in the current state, given the level of uncertainty of the underlying predictive models, versus delaying the intervention to a later state. Moreover, they assume that a resource is always available to perform an intervention (infinite capacity). This paper addresses these gaps by introducing a prescriptive process monitoring method that filters and ranks ongoing cases based on prediction scores, prediction uncertainty, and causal effect of the intervention, and triggers interventions to maximize a gain function, considering the available resources. The proposal is evaluated using a real-life event log. The results show that the proposed method outperforms existing baselines regarding total gain.

show abstract

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

Cited by 20 publications

References 18 publications

Responsible Process Mining

Responsible Process Mining

Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning

When to Intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints

Contact Info

Product

Resources

About