Prescriptive Business Process Monitoring for Recommending Next Best Actions

Weinzierl, Sven; Dunzer, Sebastian; Zilker, Sandra; Matzner, Martin

doi:10.1007/978-3-030-58638-6_12

Cited by 41 publications

(30 citation statements)

References 21 publications

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“…Improving business processes performance is possibly be accomplished by monitoring and managing the KPIs of these processes [31]. Different studies are focused on business process improvement based on KPIs during the BPM [32,33].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Modern Digital Systems and Approaches to Business Process Management

et al. 2022

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In order to adapt to ever-changing customer needs and satisfy them, good Business Process Management (BPM) in Small and Medium-sized Enterprises (SMEs) is crucial. The target group of this research is production SMEs whose BPM can be monitored respecting the values of key performance indicators (KPIs). This paper shows how improving the performance of the observed business processes can improve the level of customer satisfaction. This improvement should lead to the sustainability of SMEs in the market. In this paper, evaluation of business processes performance is defined as a multi-criteria decision problem. The relative importance of considered KPIs and their imprecise values are described by linguistic expressions, which are then modeled by triangular intuitionistic fuzzy numbers (TIFNs). Calculation of KPI weights is done by using the fuzzy analytic hierarchy process (FAHP). Evaluation of BPM success is conducted respecting the obtained KPI weights and KPI values. An optimal solution for BPM success improvement, respecting customer satisfaction indicators, is calculated using the Artificial Neural Network (ANN) and Genetic Algorithm (GA) approaches. By applying the proposed model, managers of production SMEs can determine the management initiatives that will improve their business and the sustainability of their companies.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Modern Digital Systems and Approaches to Business Process Management

et al. 2022

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“…This threshold is then optimized with respect to a net gain function. The method by Weinzierl et al [22] uses predictive models to determine which activity, among the most likely next-activities in a case, is correlated with higher values of a given KPI. Meanwhile, Metzger et al [10] use deep learning models to generate predictions about the future state of an ongoing case and feed these predictions to an online reinforcement learning technique, which triggers an intervention based on the predictions and their reliability.…”

Section: Related Workmentioning

confidence: 99%

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

Bozorgi

Teinemaa²,

Dumas

et al. 2021

2021 3rd International Conference on Process Mining (ICPM)

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Reducing cycle time is a recurrent concern in the field of business process management. Depending on the process, various interventions may be triggered to reduce the cycle time of a case, for example, using a faster shipping service in an order-to-delivery process or giving a phone call to a customer to obtain missing information rather than waiting passively. Each of these interventions comes with a cost. This paper tackles the problem of determining if and when to trigger a time-reducing intervention in a way that maximizes the total net gain. The paper proposes a prescriptive process monitoring method that uses orthogonal random forest models to estimate the causal effect of triggering a time-reducing intervention for each ongoing case of a process. Based on this causal effect estimate, the method triggers interventions according to a userdefined policy. The method is evaluated on two real-life logs.

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“…Yet, this approach does not take into account the notion of earliness, i.e., the fact that firing an alarm earlier has a different effect than firing it at a later point. In the context of prescriptive process monitoring, in [33], the authors, instead of alarms, as addressed in this paper, provide recommendations in the form of actions to be executed next. Finally, [15] propose a general architecture for prescriptive process monitoring.…”

Section: Related Workmentioning

confidence: 99%

Fire now, fire later: alarm-based systems for prescriptive process monitoring

et al. 2021

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Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood that it will lead to an undesired outcome. These techniques, however, focus on generating predictions and do not prescribe when and how process workers should intervene to decrease the cost of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect. The framework incorporates a parameterized cost model to assess the cost–benefit trade-off of generating alarms. We show how to optimize the generation of alarms given an event log of past process executions and a set of cost model parameters. The proposed approaches are empirically evaluated using a range of real-life event logs. The experimental results show that the net cost of undesired outcomes can be minimized by changing the threshold for generating alarms, as the process instance progresses. Moreover, introducing delays for triggering alarms, instead of triggering them as soon as the probability of an undesired outcome exceeds a threshold, leads to lower net costs.

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Prescriptive Business Process Monitoring for Recommending Next Best Actions

Cited by 41 publications

References 21 publications

Application of Modern Digital Systems and Approaches to Business Process Management

Application of Modern Digital Systems and Approaches to Business Process Management

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

Fire now, fire later: alarm-based systems for prescriptive process monitoring

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