PROCESS MEETS PROJECT PRIORITIZATION – A DECISION MODEL FOR DEVELOPING PROCESS IMPROVEMENT ROADMAPS

Improving business processes is a key success factor for organizations and, at the same time, a major challenge for decision makers. For process improvement to be successful, effective prioritization is essential. Despite the existence of approaches for the prioritization of process improvement projects or business processes, prescriptive research at the intersection of both research streams is missing. Existing approaches do not simultaneously prioritize business processes and improvement projects. Hence, scarce corporate funds may be misallocated. To address this research gap, we propose the PMP2, an economic decision model that assists organizations in the identification of business process improvement (BPI) roadmaps. Based on stochastic processes and simulation, the decision model maps different improvement projects to individual business processes within a process network. Thereby, it caters for process dependencies and basic interactions among projects. Drawing from the principles of value-based management, the decision model determines the process improvement roadmap with the highest contribution to the long-term firm value. To evaluate the PMP2, we instantiated it as a software prototype and performed different scenario analyses based on synthetic data. The results highlight the importance of prioritizing business processes and improvement projects in an integrated manner

Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction

Predictive process monitoring aims at forecasting the behavior, performance, and outcomes of business processes at runtime. It helps identify problems before they occur and re-allocate resources before they are wasted. Although deep learning (DL) has yielded breakthroughs, most existing approaches build on classical machine learning (ML) techniques, particularly when it comes to outcome-oriented predictive process monitoring. This circumstance reflects a lack of understanding about which event log properties facilitate the use of DL techniques. To address this gap, the authors compared the performance of DL (i.e., simple feedforward deep neural networks and long short term memory networks) and ML techniques (i.e., random forests and support vector machines) based on five publicly available event logs. It could be observed that DL generally outperforms classical ML techniques. Moreover, three specific propositions could be inferred from further observations: First, the outperformance of DL techniques is particularly strong for logs with a high variant-to-instance ratio (i.e., many non-standard cases). Second, DL techniques perform more stably in case of imbalanced target variables, especially for logs with a high event-to-activity ratio (i.e., many loops in the control flow). Third, logs with a high activity-to-instance payload ratio (i.e., input data is predominantly generated at runtime) call for the application of long short term memory networks. Due to the purposive sampling of event logs and techniques, these findings also hold for logs outside this study

The biggest business process management problems to solve before we die

It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one’s field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology

The biggest business process management problems to solve before we die

It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one's field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology

Data-driven process prioritization in process networks

Business process management (BPM) is an essential paradigm of organizational design and a source of corporate performance. Receiving constant attention from corporate decision-makers, process improvement is the most value-creating activity in the BPM lifecycle. With ineffective process prioritization capabilities being a key failure factor of process improvement, we propose the Data-Driven Process Prioritization (D2P2) approach. The D2P2 extends existing approaches to process prioritization as it accounts for structural and stochastic process dependencies and predicts risky future process performance based on data from process logs. The D2P2 returns a priority list that indicates in which periods the processes from a given business process architecture should undergo an in-depth analysis to check whether they require improvement. Thus, the D2P2 contributes to the prescriptive knowledge on process prioritization. To evaluate the D2P2, we discussed its design specification against theory-backed design objectives and competing artefacts. We also implemented the D2P2 as a software prototype and report on an extensive demonstration example including a scenario analysis

AMARYLLIS: A User-Centric Information System for Automated Privacy Policy Analysis

Internet users typically do not read the privacy policies of websites since they are written as complex legal texts and at great length. Recent research addresses this issue by applying Natural Language Processing and Machine Learning approaches in order to strengthen the digital sovereignty of Internet users by extraction of relevant information of privacy policies. These approaches achieve an accuracy of up to 90%. However, none of these have successfully prevailed, due to insufficient consideration of the requirements of especially privacy-aware Internet users. Therefore, we present the architecture of AMARYLLIS (AutoMAted pRivacY poLicy anaLysIS), a user-centric information system, as well as its use cases, applying a Design Science Research methodology. Our information system maps the entire privacy policy analysis process against users’ preferences, provides a scalable solution, incorporates a usercentric design, and includes Privacy by Design. An evaluation with potential users and experts reveals significant satisfaction with these features. The results highlight the importance of features currently not considered by existing solutions. Therefore, these features should serve as fundamental design principles of an information system analyzing privacy policies

Customers Like It Hot and Fast – Incorporating Customer Effects into the Meal Delivery Process

Delivering meal orders as fast as possible and the meal itself as hot as possible are the most important factorsin the meal delivery process as they drive customer satisfaction. High customer satisfaction leads to loyal customers, implying a higher rate of recurring orders, in return. Existing approaches tackle the meal delivery process by taking a short-term perspective on a single optimization criterion (e.g. minimizing delivery costs). Still missing is an alternative perspective that also incorporates the long-term value contribution of individual customers. By neglecting this customer-centric perspective, frequent out-of-town located ordering customers might be disadvantaged as they are repeatedly served at the end of the route. To close this research gap, we propose a decision model (C2RG) that incorporates a long-term customer-centric view. Depending on different short- and long-term preferences, the model can be appropriately customized. We observe a significant increase in a long-term factor, such as customer fairness by only slightly reducing short-term route performance. We instantiated a software prototype of the C2RG and evaluated it with real-world data of a local platform-to-consumer delivery service located in Germany. The results show the importance of considering a customer-centric long-term perspective in the meal delivery process