Predicting next events in predictive process monitoring enables companies to manage and control processes at an early stage and reduce their action distance. In recent years, approaches have steadily moved from classical statistical methods towards the application of deep neural network architectures, which outperform the former and enable analysis without explicit knowledge of the underlying process model. While the focus of prior research was on the long short-term memory network architecture, more deep learning architectures offer promising extensions that have proven useful for other applications of sequential data. In our work, we introduce a gated convolutional neural network and a key-value-predict attention network to the task of next event prediction. In a comprehensive evaluation study on 11 real-life benchmark datasets, we show that these two novel architectures surpass prior work in 34 out of 44 metric-dataset combinations. For our evaluation, we consider the effects of process data properties, such as sparsity, variation, and repetitiveness, and discuss their impact on the prediction quality of the different deep learning architectures. Similarly, we evaluate their classification properties in terms of generalization and handling class imbalance. Our results provide guidance for researchers and practitioners alike on how to select, validate, and comprehensively benchmark (novel) predictive process monitoring models. In particular, we highlight the importance of sufficiently diverse process data properties in event logs and the comprehensive reporting of multiple performance indicators to achieve meaningful results.

Digital transformation forces companies to rethink their processes to meet current customer needs. Business Process Management (BPM) can provide the means to structure and tackle this change. However, most approaches to BPM face restrictions on the number of processes they can optimize at a time due to complexity and resource restrictions. Investigating this shortcoming, the concept of the long tail of business processes suggests a hybrid approach that entails managing important processes centrally, while incrementally improving the majority of processes at their place of execution. This study scrutinizes this observation as well as corresponding implications. First, we define a system of indicators to automatically prioritize processes based on execution data. Second, we use process mining to analyze processes from multiple companies to investigate the distribution of process value in terms of their process variants. Third, we examine the characteristics of the process variants contained in the short head and the long tail to derive and justify recommendations for their management. Our results suggest that the assumption of a long-tailed distribution holds across companies and indicators and also applies to the overall improvement potential of processes and their variants. Across all cases, process variants in the long tail were characterized by fewer customer contacts, lower execution frequencies, and a larger number of involved stakeholders, making them suitable candidates for distributed improvement

Robotic process automation (RPA) is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes. In contrast to other process automation technologies, RPA only accesses the presentation layer of IT systems and imitates human behavior. Due to the novelty of this approach and the varying approaches when implementing the technology, up to 50% of RPA projects fail. To tackle this issue, we use a design science research approach to develop a framework for the initiation of RPA projects. We analyzed a total of 23 case studies of RPA implementation projects to derive a preliminary sequential model. We then used expert interviews to validate and refine the model. The result is a consolidated framework with variable stages, that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments. We conclude the paper with a discussion and an outlook on research opportunities on adapting and scaling RPA technology in projects.

Digital transformation dominates the practical and scientific discourse. Still, many companies do not have a clear plan on how to approach it. Particularly, small- and medium-sized enterprises struggle to initiate their digital journey as they lack resources and expertise. In response, we examine how five companies use business process management (BPM) to implement digital transformation. We perform a qualitative interview study, and analyze the capabilities of BPM based on six requirements of digital transformation. Thereby, we carve out 17 recommendations, which must be adapted according to companies’ meta objectives. We derive three strategy archetypes to serve as implementation blueprints.

Business processes orchestrate service requests in a structured fashion. Process knowledge, however, has rarely been used to predict and decide about cloud infrastructure resource usage. In this paper, we present an approach for BPM-aware cloud computing that builds on process knowledge to improve the timeliness and quality of resource scaling decisions. We introduce an IaaS resource controller based on fuzzy theory that monitors process execution and that is used to predict and control resource requirements for subsequent process tasks. In a laboratory experiment, we evaluate the controller design against a commercially available state-of-the-art auto scaler. Based on the results, we discuss improvements and limitations, and suggest directions for further research.

Enterprise systems (ES) can be understood as the de facto standard for holistic operational and managerial support within an organization. Most commonly ES are offered as commercial off-the-shelf packages, requiring customization in the user organization. This process is a complex and resource-intensive task, which often prevents small and midsize enterprises (SME) from undertaking configuration projects. Especially in the SME market independent software vendors provide pre-configured ES for a small customer base. The problem of ES configuration is shifted from the customer to the vendor, but remains critical. We argue that the yet unexplored link between process configuration and business document configuration must be closer examined as both types of configuration are closely tied to one another