Over the past forty years, the use of process models in practice has grown extensively. Until twenty years ago, remarkably little was known about the factors that contribute to the human understandability of process models in practice. Since then, research has, indeed, been conducted on this important topic, by e.g. creating guidelines. Unfortunately, the suggested modelling guidelines often fail to achieve the desired effects, because they are not tied to actual experimental findings. The need arises for knowledge on what kind of visualisation of process models is perceived as understandable, in order to improve the understanding of different stakeholders. Therefore the objective of this study is to answer the question: How can process models be visually enhanced so that they facilitate a common understanding by different stakeholders? Consequently, five subresearch questions (SRQ) will be discussed, covering three studies. By combining social psychology and process models we can work towards a more human-centred and empirical-based solution to enhance the understanding of process models by the different stakeholders with visualisation.
MULTIFILE
Introduction: Different models of frameworks for dietetic care are used in Europe. There is a substantial need for a consistent framework to compare research results and to cooperate on an international level. Therefore, one of the goals of the EU-funded project IMPECD was the development of a unified framework Dietetic Care Process (DCP) in order to foster a shared understanding of process-driven dietetic counselling. Materials and Methods:: Based on a literature review and in-depth analysis of different frameworks an iterative and incremental development process of finding solutions for decision-making within the consortium consisting of dietetic experts from 5 European HEI was passed. The developed DCP model was integrated in an online training course including 9 clinical cases (MOOC) to train students. The draft versions and the concluding final version DCP model were evaluated and re-evaluated by teachers and 25 students at two Intensive Study Programmes. Results:: The DCP model consists of five distinct, interrelated steps which the consortium agreed on: Dietetic Assessment, Dietetic Diagnosis, Planning Dietetic Intervention, Implementing Dietetic Intervention, Dietetic Outcome Evaluation. A standardized scheme was developed to define the process steps: dedication, central statement, aim and principles, and operationalization. Discussion:: Existing different process models were analyzed to create a new and consistent concept of a unified framework DCP. The variety within the European countries represented by the consortium proved to be both a challenge in decision-making and an opportunity to integrate multinational perspectives and intensify the scientific discourse. The development of a standardized scheme with precise definitions is a prerequisite for planning study designs in health services research. Besides, clarification is essential for establishing process-guided work in practice. The evaluated MOOC is now implemented in study programmes used by 5 European HEI in order to keep approaches and process-driven action comparable. The MOOC promotes the exchange of ideas between future professionals on an international level.
DOCUMENT
Process Mining can roughly be defined as a data-driven approach to process management. The basic idea of process mining is to automatically distill and to visualize business processes using event logs from company IT-systems (e.g. ERP, WMS, CRM etc.) to identify specific areas for improvement at an operational level. An event log can be described as a database entry that signifies a specific action in a software application at a specific time. Simple examples of these actions are customer order entries, scanning an item in a warehouse, and registration of a patient for a hospital check-up.Process mining has gained popularity in the logistics domain in recent years because of three main reasons. Firstly, the logistics IT-systems' large and exponentially growing amounts of event data are being stored and provide detailed information on the history of logistics processes. Secondly, to outperform competitors, most organizations are searching for (new) ways to improve their logistics processes such as reducing costs and lead time. Thirdly, since the 1970s, the power of computers has grown at an astonishing rate. As such, the use of advance algorithms for business purposes, which requires a certain amount of computational power, have become more accessible.Before diving into Process Mining, this course will first discuss some basic concepts, theories, and methods regarding the visualization and improvement of business processes.
MULTIFILE
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
Climate change adaptation has influenced river management through an anticipatory governance paradigm. As such, futures and the power of knowing the future has become increasingly influential in water management. Yet, multiple future imaginaries co-exist, where some are more dominant that others. In this PhD research, I focus on deconstructing the future making process in climate change adaptation by asking ‘What river imaginaries exist and what future imaginaries dominate climate change adaptation in riverine infrastructure projects of the Meuse and Magdalena river?’. I firstly explore existing river imaginaries in a case study of the river Meuse. Secondly, I explore imaginaries as materialised in numerical models for the Meuse and Magdalena river. Thirdly, I explore the integration and negotiation of imaginaries in participatory modelling practices in the Magdalena river. Fourthly, I explore contesting and alternative imaginaries and look at how these are mobilised in climate change adaptation for the Magdalena and Meuse river. Multiple concepts stemming from Science and Technology Studies and Political Ecology will guide me to theorise the case study findings. Finally, I reflect on my own positionality in action-research which will be an iterative process of learning and unlearning while navigating between the natural and social sciences.
