Discovering Business Process Simulation Models in the Presence of Multitasking

Business process simulation is a versatile technique for analyzing business processes from a quantitative perspective. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, several authors have proposed to discover simulation models from process execution logs so that the resulting simulation models more closely match reality. Existing techniques in this field assume that each resource in the process performs one task at a time. In reality, however, resources may engage in multitasking behavior. Traditional simulation approaches do not handle multitasking. Instead, they rely on a resource allocation approach wherein a task instance is only assigned to a resource when the resource is free. This inability to handle multitasking leads to an overestimation of execution times. This paper proposes an approach to discover multitasking in business process execution logs and to generate a simulation model that takes into account the discovered multitasking behavior. The key idea is to adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original processing times. The proposed approach is evaluated using a real-life dataset and synthetic datasets with different levels of multitasking. The results show that, in the presence of multitasking, the approach improves the accuracy of simulation models discovered from execution logs.Comment: Accepted at The 14th International Conference on Research Challenges in Information Science (RCIS 2020). 17 pages, 4 figure

Discovering business process simulation models in the presence of multitasking and availability constraints

Business process simulation is a versatile technique for quantitative analysis of business processes. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, various authors have proposed to discover simulation models from process execution logs, so that the resulting simulation models more closely match reality. However, existing techniques in this field make certain assumptions about resource behavior that do not typically hold in practice, including: (i) that each resource performs one task at a time; and (ii) that resources are continuously available (24/7). In reality, resources may engage in multitasking behavior and they work only during certain periods of the day or the week. This article proposes an approach to discover process simulation models from execution logs in the presence of multitasking and availability constraints. To account for multitasking, we adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original times. Meanwhile, to account for availability constraints, we use an algorithm for discovering calendar expressions from collections of time-points to infer resource timetables from an execution log. We then adjust the parameters of this algorithm to maximize the similarity between the simulated log and the original one. We evaluate the approach using real-life and synthetic datasets. The results show that the approach improves the accuracy of simulation models discovered from execution logs both in the presence of multitasking and availability constraintsEuropean Research Council PIX 834141Ministerio de Ciencia, Innovación y Universidades OPHELIA RTI2018-101204-B-C22Junta de Andalucía EKIPMENTPLUS (P18–FR–2895

A systematic literature review on IoT-aware business process modeling views, requirements and notations

The Internet of things has been adopted in several sectors both influencing how people work and enhancing organizations' business processes. This resulted in the rise of relevant research topics such as IoT-aware business processes. The modeling of these processes makes it possible to better understand working scenarios and to support the adoption of model-driven development approaches for IoT-aware and process-oriented software systems. Since much research has been performed on this topic, a better awareness of the current status is needed. This paper reports a systematic literature review to develop a map on modeling notations for IoT-aware business processes. The survey mainly adopts an academic point of view, resulting in the detailed analysis of 84 research works from the leading computer science digital libraries. The output of the review is in the form of schemes and reflections. In particular, our research aims to shed light on (1) the relevant modeling views referring to different types of IoT-aware business processes; (2) the IoT requirements supported by the modeling notations; and (3) the modeling notations proposed and/or adopted to model IoT-aware business processes. Finally, our research work highlights possible future research lines needing further investigations

Integrating technology and organization for manufacturing sector performance: evidence from Finland

This dissertation investigates the complex factors shaping the future of manufacturing, focusing on innovation, competitiveness, and employment trends within the European context. Leveraging the extensive 2022 European Manufacturing Survey dataset, it models relationships between critical technological and organizational variables impacting manufacturing resilience using cross-lagged panel path analysis. Against the 2019–2021 economic and environmental backdrop, the research examines manufacturers’ integral survival strategies derived from challenges faced. Factors like business innovation models, organizational concepts, key technologies, and relocation approaches are assessed for performance. The study reveals competitive standards: automation, robotics, additive manufacturing, accessbased business models, maintenance services, and production organization. These discoveries have profound implications for enabling the transition to next-generation sustainable manufacturing through technology integration frameworks. The research marks the need for investments in cross-sectoral research coordination. As climate change intensifies, reimagining manufacturing is critical. While acknowledging limitations like sample size and scope, the dissertation offers a detailed understanding of the manufacturing system’s components and the relationships of success, forward strategies, and human-technology-environment interlinkages. This multidimensional perspective provides insight to catalyze the creation of integrated manufacturing ecosystems worldwide

Tackling Dierent Business Process Perspectives

Business Process Management (BPM) has emerged as a discipline to design, control, analyze, and optimize business operations. Conceptual models lie at the core of BPM. In particular, business process models have been taken up by organizations as a means to describe the main activities that are performed to achieve a specific business goal. Process models generally cover different perspectives that underlie separate yet interrelated representations for analyzing and presenting process information. Being primarily driven by process improvement objectives, traditional business process modeling languages focus on capturing the control flow perspective of business processes, that is, the temporal and logical coordination of activities. Such approaches are usually characterized as \u201cactivity-centric\u201d. Nowadays, activity-centric process modeling languages, such as the Business Process Model and Notation (BPMN) standard, are still the most used in practice and benefit from industrial tool support. Nevertheless, evidence shows that such process modeling languages still lack of support for modeling non-control-flow perspectives, such as the temporal, informational, and decision perspectives, among others. This thesis centres on the BPMN standard and addresses the modeling the temporal, informational, and decision perspectives of process models, with particular attention to processes enacted in healthcare domains. Despite being partially interrelated, the main contributions of this thesis may be partitioned according to the modeling perspective they concern. The temporal perspective deals with the specification, management, and formal verification of temporal constraints. In this thesis, we address the specification and run-time management of temporal constraints in BPMN, by taking advantage of process modularity and of event handling mechanisms included in the standard. Then, we propose three different mappings from BPMN to formal models, to validate the behavior of the proposed process models and to check whether they are dynamically controllable. The informational perspective represents the information entities consumed, produced or manipulated by a process. This thesis focuses on the conceptual connection between processes and data, borrowing concepts from the database domain to enable the representation of which part of a database schema is accessed by a certain process activity. This novel conceptual view is then employed to detect potential data inconsistencies arising when the same data are accessed erroneously by different process activities. The decision perspective encompasses the modeling of the decision-making related to a process, considering where decisions are made in the process and how decision outcomes affect process execution. In this thesis, we investigate the use of the Decision Model and Notation (DMN) standard in conjunction with BPMN starting from a pattern-based approach to ease the derivation of DMN decision models from the data represented in BPMN processes. Besides, we propose a methodology that focuses on the integrated use of BPMN and DMN for modeling decision-intensive care pathways in a real-world application domain