Business process modeling and system dynamics are different approaches that are used in the design and management of organizations. Both approaches are concerned with the processes in, and around, organizations with the aim to identify, design and understand their behavior as well as potential improvements. At the same time, these approaches differ considerably in their methodological focus. While business process modeling specifically takes the (control flow of) business processes as its primary focus, system dynamics takes the analysis of complex and multi-faceted systems as its core focus. More explicitly combining both approaches has the potential to better model and analyze (by way of simulation) complex business processes, while specifically also including more relevant facets from the environment of these business processes. Furthermore, the inherent ability for simulation of system dynamics models, can be used to simulate the behavior of processes over time, while also putting business processes in a broader multi-faceted context. In this paper, we report on initial results on making such a more explicit combination of business process modeling and system dynamics. In doing so, we also provide a step-by-step guide on how to use BPMN based models and system dynamics models together to model and analyze complex business processes, while illustrating this in terms of a case study on the maintenance of building facades.
Business process modeling and system dynamics are different approaches that are used in the design and management of organizations. Both approaches are concerned with the processes in, and around, organizations with the aim to identify, design and understand their behavior as well as potential improvements. At the same time, these approaches differ considerably in their methodological focus. While business process modeling specifically takes the (control flow of) business processes as its primary focus, system dynamics takes the analysis of complex and multi-faceted systems as its core focus. More explicitly combining both approaches has the potential to better model and analyze (by way of simulation) complex business processes, while specifically also including more relevant facets from the environment of these business processes. Furthermore, the inherent ability for simulation of system dynamics models, can be used to simulate the behavior of processes over time, while also putting business processes in a broader multi-faceted context. In this paper, we report on initial results on making such a more explicit combination of business process modeling and system dynamics. In doing so, we also provide a step-by-step guide on how to use BPMN based models and system dynamics models together to model and analyze complex business processes, while illustrating this in terms of a case study on the maintenance of building facades.
Ubiquitous computing, new sensor technologies, and increasingly available and accessible algorithms for pattern recognition and machine learning enable automatic analysis and modeling of human behavior in many novel ways. In this introductory paper of the 6th International Workshop on Human Behavior Understanding (HBU’15), we seek to critically assess how HBU technology can be used for elderly. We describe and exemplify some of the challenges that come with the involvement of aging subjects, but we also point out to the great potential for expanding the use of ICT to create many applications to provide a better life for elderly.
‘Dieren in de dijk’ aims to address the issue of animal burrows in earthen levees, which compromise the integrity of flood protection systems in low-lying areas. Earthen levees attract animals that dig tunnels and cause damages, yet there is limited scientific knowledge on the extent of the problem and effective approaches to mitigate the risk. Recent experimental research has demonstrated the severe impact of animal burrows on levee safety, raising concerns among levee management authorities. The consortium's ambition is to provide levee managers with validated action perspectives for managing animal burrows, transitioning from a reactive to a proactive risk-based management approach. The objectives of the project include improving failure probability estimation in levee sections with animal burrows and enhancing risk mitigation capacity. This involves understanding animal behavior and failure processes, reviewing existing and testing new deterrence, detection, and monitoring approaches, and offering action perspectives for levee managers. Results will be integrated into an open-access wiki-platform for guidance of professionals and in education of the next generation. The project's methodology involves focus groups to review the state-of-the-art and set the scene for subsequent steps, fact-finding fieldwork to develop and evaluate risk reduction measures, modeling failure processes, and processing diverse quantitative and qualitative data. Progress workshops and collaboration with stakeholders will ensure relevant and supported solutions. By addressing the knowledge gaps and providing practical guidance, the project aims to enable levee managers to effectively manage animal burrows in levees, both during routine maintenance and high-water emergencies. With the increasing frequency of high river discharges and storm surges due to climate change, early detection and repair of animal burrows become even more crucial. The project's outcomes will contribute to a long-term vision of proactive risk-based management for levees, safeguarding the Netherlands and Belgium against flood risks.
Road freight transport contributes to 75% of the global logistics CO2 emissions. Various European initiatives are calling for a drastic cut-down of CO2 emissions in this sector [1]. This requires advanced and very expensive technological innovations; i.e. re-design of vehicle units, hybridization of powertrains and autonomous vehicle technology. One particular innovation that aims to solve this problem is multi-articulated vehicles (road-trains). They have a smaller footprint and better efficiency of transport than traditional transport vehicles like trucks. In line with the missions for Energy Transition and Sustainability [2], road-trains can have zero-emission powertrains leading to clean and sustainable urban mobility of people and goods. However, multiple articulations in a vehicle pose a problem of reversing the vehicle. Since it is extremely difficult to predict the sideways movement of the vehicle combination while reversing, no driver can master this process. This is also the problem faced by the drivers of TRENS Solar Train’s vehicle, which is a multi-articulated modular electric road vehicle. It can be used for transporting cargo as well as passengers in tight environments, making it suitable for operation in urban areas. This project aims to develop a reverse assist system to help drivers reverse multi-articulated vehicles like the TRENS Solar Train, enabling them to maneuver backward when the need arises in its operations, safely and predictably. This will subsequently provide multi-articulated vehicle users with a sustainable and economically viable option for the transport of cargo and passengers with unrestricted maneuverability resulting in better application and adding to the innovation in sustainable road transport.
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
