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

Process Improvement and Innovation : Identification and Planning of Process Redesign Ideas

The dissertation mainly focuses on organizational capabilities to facilitate process improvement and innovation, i.e., the most value-creating activities in the business process management lifecycle. The resulting process as well as project management methods and models include, but are not limited to, the identification and performance forecast of critical processes, the automated process redesign based on evolutionary algorithms, and the prioritization (selection and sequential order) of process improvement projects taking different perspectives. Thereby, prototypical implementations complement the scientific findings. Besides, this dissertation also contributes to the field of open innovation in providing insights in how to benefit as an organization from external knowledge. The findings obtained are just as relevant in science and practice, particularly while improving existing processes.Die Dissertation befasst sich in erster Linie mit wichtigen organisationalen Prozess- und Projektmanagementfähigkeiten im Kontext von Prozessverbesserung und -innovation, der Prozessmanagementlebenszyklusaktivität mit dem höchsten Wertbeitrag. Hieraus resultieren u.a. Methoden und Modelle zur Performanceprognose inklusive Identifikation von kritischen Prozessen, zur automatischen Generierung neuer Prozessdesigns mithilfe evolutionärer Algorithmen, sowie zur Priorisierung (Auswahl und Reihenfolge) von Prozessverbesserungsprojekten aus unterschiedlichen Perspektiven. Die wissenschaftlichen Erkenntnisse werden durch prototypische Implementierungen ergänzt. Darüber hinaus befasst sich die Dissertation mit der Frage, wie Unternehmen im Rahmen von „Open Innovation“ von externem Wissen profitieren können. Die durch die Dissertation gewonnenen Erkenntnisse sind für Wissenschaft und Praxis gleichermaßen interessant und können insbesondere bei der Verbesserung bereits bestehender Prozesse Verwendung finden

Process Improvement and Innovation : Identification and Planning of Process Redesign Ideas

The dissertation mainly focuses on organizational capabilities to facilitate process improvement and innovation, i.e., the most value-creating activities in the business process management lifecycle. The resulting process as well as project management methods and models include, but are not limited to, the identification and performance forecast of critical processes, the automated process redesign based on evolutionary algorithms, and the prioritization (selection and sequential order) of process improvement projects taking different perspectives. Thereby, prototypical implementations complement the scientific findings. Besides, this dissertation also contributes to the field of open innovation in providing insights in how to benefit as an organization from external knowledge. The findings obtained are just as relevant in science and practice, particularly while improving existing processes.Die Dissertation befasst sich in erster Linie mit wichtigen organisationalen Prozess- und Projektmanagementfähigkeiten im Kontext von Prozessverbesserung und -innovation, der Prozessmanagementlebenszyklusaktivität mit dem höchsten Wertbeitrag. Hieraus resultieren u.a. Methoden und Modelle zur Performanceprognose inklusive Identifikation von kritischen Prozessen, zur automatischen Generierung neuer Prozessdesigns mithilfe evolutionärer Algorithmen, sowie zur Priorisierung (Auswahl und Reihenfolge) von Prozessverbesserungsprojekten aus unterschiedlichen Perspektiven. Die wissenschaftlichen Erkenntnisse werden durch prototypische Implementierungen ergänzt. Darüber hinaus befasst sich die Dissertation mit der Frage, wie Unternehmen im Rahmen von „Open Innovation“ von externem Wissen profitieren können. Die durch die Dissertation gewonnenen Erkenntnisse sind für Wissenschaft und Praxis gleichermaßen interessant und können insbesondere bei der Verbesserung bereits bestehender Prozesse Verwendung finden

Roadmap to Flexible Service Processes - A Project Portfolio Selection and Scheduling Approach

Process flexibility has evolved into a desired corporate capability as it promises to cope with demand uncertainty and variety. Particularly service processes have a high intrinsic need for flexibility. Many approaches have been proposed to determine the business value and an appropriate level of flexibility for service processes. Most of these approaches focus on a distinct flexibility type and on single projects for implementing flexible service processes. The question how to reasonably combine flexibility projects has not been addressed yet. Moreover, most approaches do not conduct a full-fledged economic analysis of process flexibility. We therefore propose a decision model that helps determine an optimal flexibility roadmap, i.e., a scheduled portfolio of flexibility projects with different effects on service processes. The decision model accounts for different request types (i.e., runners, repeaters, and strangers), different capacity types (i.e., internal, external, dedicated, and flexible), different flexibility types (i.e., volume and functional), and related project archetypes. The decision model was evaluated using feature comparison, prototype construction, and a demonstration example including an extensive scenario analysi

Design it like Darwin - A value-based application of evolutionary algorithms for proper and unambiguous business process redesign

Business process management (BPM) is an acknowledged source of corporate performance. Despite the mature body of knowledge, computational support is considered as a highly relevant research gap for redesigning business processes. Therefore, this paper applies Evolutionary Algorithms (EAs) that, on a conceptual level, mimic the BPM lifecycle - the most popular BPM approach - by incrementally improving the status quo and bridging the trade-off between maintaining well-performing design structures and continuously evolving new designs. Beginning with describing process elements and their characteristics in matrices to aggregate process information, the EA then processes this information and combines the elements to new designs. These designs are then assessed by a function from value-base d management. This economic paradigm reduces designs to their value contributions and facilitates an objective prioritization. Altogether, our triad of management science, BPM and information systems research results in a promising tool for process redesign and avoids subjective vagueness inherent to current redesign projects

Inspection coming due!: How to determine the service interval of your processes!

Just like cars, processes require a general inspection from time to time. As, in reality, process portfolio managers are in charge of many processes, they do not have enough resources to deeply inspect all processes simultaneously. Nor would this be reasonable from a process performance point of view. Process portfolio managers therefore require guidance on how to determine the service interval of their processes, i.e., when they should analyze which process in depth to find out whether to initiate redesign projects. Despite the profound knowledge on process improvement, monitoring, and controlling, existing approaches are only able to rank processes or redesign projects. They do not indicate when to conduct an in-depth analysis. To overcome this research gap, we propose the critical process instance method (CPIM) that analytically predicts after which number of executed instances a process should undergo an in-depth analysis. The CPIM combines ideas from process performance management, value-based business process management, and stochastic processes. It accounts for variations in process performance induced by the paths and tasks included in a process model as well as by the positive and negative deviance experienced during past executions. For demonstration purposes, we apply the CPIM to an approval process for loan applications from the banking industry including a scenario analysis

V3PM: A DECISION SUPPORT TOOL FOR VALUE-BASED PROCESS PROJECT PORTFOLIO MANAGEMENT

In the context of Business Process Management (BPM), organizations strive to develop their BPM ca-pabilitiy and to improve their individual business processes in an integrated manner. Planning models assist in selecting and ordering implementable BPM- and process-level projects maximizing the firm value, catering for the projects’ effects on process performance and for interactions among projects. To facilitate process managers for calculating scenarios of non-trivial complexity, the Value Based Process Project Portfolio Management (V3PM) tool has been developed. The V3PM tool is a stand-alone pro-gram that effectively and efficiently selects one project portfolio for which the net present value takes the highest value. It is designed to fulfil a twofold objective: the scientific perspective in terms of an adequate evaluation for existing design science research artefacts as well as the user’s point of view in terms of a first step towards a full-featured version for decision support in daily business operations. In this paper, we describe the application’s architecture focusing on the data management, the roadmap engine and the graphical user interface. Deeper insights into the functionality for creating and analyzing persistent problem sets highlight the strengths of the V3PM tool as well as its usefulness and practical applicability for decision support

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