Ivo Adan

Business processes are vital to an organisation. Occasionally, those processes lead to undesired outcomes such as delays, quality issues, excessive costs, or low throughput, often caused by complex dependencies between process conditions and resources. Identifying the underlying causes of the process outcomes enables targeted process improvements, a form of post-hoc decision support in which, given an unfavourable completed execution, the system proposes a small set of high-impact, evidence-based changes to improve future process outcomes. Counterfactual explanation is a promising technique to provide such decision support. However, existing techniques lack the ability to deal with constraints specific to processes with complex dependencies. In this work, we propose a counterfactual decision support approach leveraging the object-centric process modelling paradigm to explain how a process execution must be modified to change its outcome to a desired one. The resulting explanations are constrained by the underlying process and domain rules to ensure that the suggested explanations are feasible, actionable, and minimally invasive. The proposed approach is evaluated on multiple synthetic and real-world process datasets and benchmarked against a method for generating generic graph counterfactuals. The results demonstrate that our approach is effective in generating minimal counterfactual explanations and outperforms the tested competitor.

Allocating the right resource to perform a task for a case is essential forthe efficient execution of business processes. Traditionally, simple rule-basedheuristics, such as Shortest Processing Time (SPT) and First-In-First-Out(FIFO), are used for this purpose. These heuristics are easy to understandand explain, but their effectiveness depends strongly on process characteristicsand goals. Recently, Deep Reinforcement Learning (DRL) approacheshave been developed to learn effective policies for resource allocation problems.While these methods can improve performance, they produce blackboxpolicies that are difficult to interpret and explain, limiting their practicaladoption. This paper introduces a method for learning explainable resourceallocationpolicies that combines the strengths of rule-based heuristics andDRL. The approach uses DRL to learn a policy that selects the most suitableheuristic for each process state and then applies a post-hoc explanation stepthat distills the learned policy into a decision tree. The resulting policy canbe expressed as human-readable rules, such as selecting SPT when processingtimes are high and FIFO otherwise. We evaluate the DRL policy and thedistilled decision-tree policies on six synthetic and five real-world businessprocesses. The DRL approach matches or outperforms the best individualheuristic in all considered, demonstrating the effectiveness and scalability ofadaptive heuristic selection. Moreover, in most cases, the distilled decisiontreematches the performance of the original DRL policy while providingtransparent and executable decision rules.

With the advent of digital transformation, organisations are increasingly generatinglarge volumes of data through the execution of various processesacross disparate systems. By integrating and aggregating data from theseheterogeneous sources, it becomes possible to derive new insights essential fortasks such as monitoring and analysing process performance. Typically, thisinformation is extracted during a data pre-processing or engineering phase.However, this step is often performed in an ad-hoc manner and is time consumingand labour-intensive. To streamline this process, we introducea reference model and a collection of patterns designed to enrich productionevent data. The reference model provides a standard way to store andextract production event data. The patterns describe common data aggregationand information extraction tasks. The reference model is constructed bycombining the ISA-95 industry standard with the Event Knowledge Graphformalism. The patterns are developed based on empirical observations fromevent data sets originating in manufacturing processes and are formalisedusing the reference model. The ability of the proposed patterns to acceleratedata aggregation and facilitate knowledge exchange has been validatedthrough an empirical evaluation involving manufacturing professionals fromdifferent roles and production environments.