When it comes to hard to solve problems, the significance of situational knowledge construction and network coordination must not be underrated. Professional deliberation is directed toward understanding, acting and analysis. We need smart and flexible ways to direct systems information from practice to network reflection, and to guide results from network consultation to practice. This article presents a case study proposal, as follow-up to a recent dissertation about online simulation gaming for youth care network exchange (Van Haaster, 2014).
Although the contribution of user participation to information sys-tems/information technology (IS/IT) project success is generally acknowledged in the literature, empirical evidence of the different attributes of user participation practices and the role of management in this process is still largely absent. This paper addresses two research questions: first, what determines the attitudes of users to ‘go with a new workflow’ in the case of a Business Process Management (BPM) system implementation? Second, how are these attitudes related to their user satisfaction and use of a BPM system? Based on theories of user participa-tion, management support and implementation effectiveness, a conceptual model is developed that defines a relationship between user participation, user attitudes and success metrics. To test the model, different research methods were used. First, survey data was collected among 78 end-users. Second, nine in-depth open interviews were held with the project manager, key-users and devel-opers. All respondents were employed by a large Dutch administrative social in-surance organisation that had customised and implemented a new and integra-tive BPM system.
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In this thesis several studies are presented that have targeted decision making about case management plans in probation. In a case management plan probation officers describe the goals and interventions that should help offenders stop reoffending, and the specific measures necessary to reduce acute risks of recidivism and harm. Such a plan is embedded in a judicial framework, a sanction or advice about the sanction in which these interventions and measures should be executed. The topic of this thesis is the use of structured decision support, and the question is if this can improve decision making about case management plans in probation and subsequently improve the effectiveness of offender supervision. In this chapter we first sketch why structured decision making was introduced in the Dutch probation services. Next we describe the instrument for risk and needs assessment as well as the procedure to develop case management plans that are used by the Dutch probation services and that are investigated in this thesis. Then we describe the setting of the studies and the research questions, and we conclude with an overview of this thesis.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
Family Dairy Tech Sustainable and affordable stable management systems for family dairy farms in India. An example of Dutch technology that is useful to an ?emerging economy?. Summary Problem The demand for dairy products in India is increasing. Small and medium-sized family farmers want to capitalize on this development and the Indian government wants to support them. Dutch companies offer knowledge and a wide range of products and services to improve dairy housing systems and better milk quality, in which India is interested. However, the Dutch technology is sophisticated and expensive. For a successful entry into this market, entrepreneurs have to develop affordable and robust (?frugal?) systems and products adapted to the Indian climate and market conditions. The external question is therefore: ?How can Dutch companies specialised on dairy housing systems adapt their products and offer these on the Indian market to contribute to sustainable and profitable local dairy farming??. Goal Since 2011, VHL University of Applied Sciences (VHL) is collaborating with a college and an agricultural information center Krishi Vigyan Kendra (KVK), Baramati, Pune district, Maharashtra State India. In this region many small-scale dairy farmers are active. Within this project, KVK wants to support farmers to scale up their farm form one or a few cows up to 15 to 100 cows, with a better milk quality. In this innovative project, VHL and Saxion Universities of Applied Sciences, in collaboration with KVK and several Dutch companies want to develop integrated solutions for the growing number of dairy farms in the State of Maharashtra, India. The research questions are: 1. "How can, by smart combinations of existing and new technologies, the cow-varieties and milk- and stable-management systems in Baramati, India, for family farmers be optimized in an affordable and sustainable way?" 2. "What are potential markets in India for Dutch companies in the field of stable management and which innovative business models can support entering this market?" Results The intended results are: 1. A design of an integral stable management system for small and medium-sized dairy farms in India, composed of modified Dutch technologies. 2. A cattle improvement programme for robust cows that are adapted to the conditions of Maharashtra. 3. An advice to Dutch entrepreneurs how to develop their market position in India for their technologies. 4. An advice to Indian family farmers how they can increase their margins in a sustainable way by employing innovative technologies.
