Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.
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Since an increasing amount of business decision/logic management solutions are utilized, organizations search for guidance to design such solutions. An important aspect of such a solution is the ability to guard the quality of the specified or modified business decisions and underlying business logic to ensure logical soundness. This particular capability is referred to as verification. As an increasing amount of organizations adopt the new Decision Management and Notation (DMN) standard, introduced in September 2015, it is essential that organizations are able to guard the logical soundness of their business decisions and business logic with the help of certain verification capabilities. However, the current knowledge base regarding verification as a capability is not yet researched in relation to the new DMN standard. In this paper, we re-address and - present our earlier work on the identification of 28 verification capabilities applied by the Dutch government [1]. Yet, we extended the previous research with more detailed descriptions of the related literature, findings, and results, which provide a grounded basis from which further, empirical, research on verification capabilities with regards to business decisions and business logic can be explored.
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Social enterprises and government share the ultimate goal of solving societal problems, which provides a lot of potential for collaboration between the two parties. While the local government level is the most relevant for social enterprises, little research has been done on the relationship between social entrepreneurs and local government officials. However, in the Netherlands, social enterprises experience these relations as far from optimal, evidenced by the fact that they named ‘regulations and government policy’ as the most important obstacle for increasing their impact in a 2015 sector survey. Therefore, a pilot project was started with social entrepreneurs in an Amsterdam neighbourhood, forming a learning network aiming to improve relations with local government. In the network, an innovative tool was developed in the form of a set of five illustrated stereotypes of social entrepreneurs with certain views towards local government. These stereotypes serve both as a reflection tool for social entrepreneurs and as a communication tool to open dialogue between social entrepreneurs and local government. We conclude that in an applied research project, it is crucial to place focus on the final phases in which results are reformulated into practical tools to match target groups, and resulting tools are distributed through targeted events and publications.
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Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to collect, manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. Participants in the interviews that we organized preparing this proposal indicated a need for guidance on how to develop DAC within their organization given their unique context (e.g. age and experience of the workforce, presence of legacy systems, high daily workload, lack of knowledge of digitalization). While a lot of attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC. From a structure perspective, the objective of the KIEM proposal will be to explore and solidify the partnership between Breda University of Applied Sciences (BUas), Avans University of Applied Sciences (Avans), Logistics Community Brabant (LCB), van Berkel Logistics BV, Smink Group BV, and iValueImprovement BV. This partnership will be used to develop the preliminary roadmap and pre-test it using action methodology. The action research protocol and preliminary roadmap thereby developed in this KIEM project will form the basis for a subsequent RAAK proposal.
Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. While extensive attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC.
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%.
Lectoraat, onderdeel van NHL Stenden Hogeschool