Analysis of the Tool Support for Business Model Innovation

Organizations adapt their business models (BM) frequently to remain competitive. One reason for this is the digitalization of products and services. A change in strategy often brings along an adaption of the BM or a complete business model innovation (BMI). In this context, software tools can support users in the presentation and analysis of their BM by providing methodological knowledge. The assessment of a software tool is influenced by different orientations and functionalities, e.g., for pure BM representation or simulation. This contribution provides a procedure for systematically assessing BMI tools and offers an overview of existing BM tools as well as their support for BMI and a related transformation process. The results show that early phases of BMI are well supported, while later phases are hardly represented. A possible reason for this lies in the complexity of later BMI phases. First steps towards supporting later phases through software are discussed

A Conceptual Architecture for an ICT-based Personal Health System for Cardiac Rehabilitation

Traditional cardiac rehabilitation is strongly based on supervised exercise therapy. However, studies show that after leaving a rehabilitative care facility it is difficult for cardiac patients to continue with exercise therapy and overall to conduct the necessary behavioral changes for a healthier life. The novel approach for cardiac rehabilitation presented in this paper is based on including health services (e.g. exercise, support with healthy behavior, etc.) in everyday life. One goal is to foster physical activities which are not solely conducted with the goal to exercise. Another goal is to provide personalized and context-aware health services which proactively assist patients with their disease management. Therefore, a conceptual architecture of an ICT-based personal health system is presented in this paper. The goal is a better alignment with the individual needs of patients and the inclusion of life-long disease management into daily life

Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications

The recent financial crisis has forced politics to overthink regulatory structures and compliance mechanisms for the financial industry. Faced with these new challenges the financial industry in turn has to reevaluate their risk assessment mechanisms. While approaches to assess financial risks, have been widely addressed, the compliance of the underlying business processes is also crucial to ensure an end-to-end traceability of the given business events. This paper presents a novel approach to predict entry times and other key performance indicators of such events in a business process. A loan application process is used as a data example to evaluate the chosen feature modellings and algorithms

Conceiving Adaptability for Business Models: A Literature-based Approach

A rapidly changing economy and peer pressure amongst competitors lead business to continuously reconsider and readjust their current business models. Thus, business models must be flexible and adaptive towards external changes and should be controlled and managed dynamically. This paper develops a conceptual framework for adaptive business models, which enables decision makers in strategy and IT management to intertwine business models with strategy and business processes, in order to analyze the complex relationships amongst these different description levels of an enterprise. Based on the core elements of business models, the interplay of these elements with aspects from enterprise strategy and business processes are investigated and potentials for IT innovations are being identified to live up to the vision of adaptive business models. For each of the innovations, key measures are considered and improvement possibilities within an enterprise’s IT infrastructure are being identified. The paper concludes with an outlook on possible implementations and future research

Improving Business Model Configuration through a Question-based Approach

In the competitive context of agile innovation cycles, it is necessary for companies to construct their business model leading them to a creative strategy innovation. There already are a number of methods to create business models, many of which are also implemented in software. However, these are often unstructured, unguided and static, resulting in diverse and heterogeneous business models. This complicates automated evaluation allowing recommendations. The aim is to develop a question-based tool yielding for comparable and, thus, analyzable business models based on a developed standardized taxonomy. The questions guiding through the configurator were derived from this taxonomy. A tool was developed implementing the question-based concept. User tests were conducted as part of an evaluation showing promising results concerning usability in addition to the already achieved standardization

Time Series Classification using Deep Learning for Process Planning: A Case from the Process Industry

Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper, we propose a framework to extract the features in an unsupervised (or self-supervised) manner using deep learning, particularly stacked LSTM Autoencoder Networks. The compressed representation of the time-series data obtained from LSTM Autoencoders are then provided to Deep Feedforward Neural Networks for classification. We apply the proposed framework on sensor time series data from the process industry to detect the quality of the semi-finished products and accordingly predict the next production process step. To validate the efficiency of the proposed approach, we used real-world data from the steel industry

Personalized And Situation-Aware Recommendations For Runners

The project uService investigates the transformation of a mobile user into a service super prosumer, i.e., a producer, provider and consumer of services at the same time. The goal is to develop a platform which enables a user to create, discover and consume mobile services anywhere and at any time on the mobile device. uRun is an application scenario of the project in the field of mobile health and fitness. The uRun framework provides a mobile assistance system particularly for runners, which combines Web 2.0 and Web 3.0 technologies and personalized and situation-aware recommendation mechanisms. The ability to create individual and mobile health and fitness services as well as a personalized and situation-aware assistance system based on a semantic knowledge base are considered to provide an edge over existing consumer-centric health care systems. In this article, we describe the recommendation mechanism and the incorporation of semantic knowledge for the uService platform and the uRun framework

Assessment of Local Reaction to Vaccines in Live Piglets with Magnetic Resonance Imaging Compared to Histopathology

The safety of veterinary vaccines is assessed in clinical trials in Europe. The assessment of the local tissue reaction to vaccination by magnetic resonance imaging (MRI) could reduce the number of animals needed because repeated examinations can be performed in the same animal over time. The present study compared the evaluation of local tissue reactions to vaccination using MRI in live pigs with histopathology of porcine tissue, the current gold standard in regulatory safety testing. Eight piglets each were administered one of two commercial vaccines into marked injection sites. All animals were sedated and scanned repeatedly by MRI using a contrast agent up to day 29 after vaccination. On day 29, the animals were euthanized and underwent a pathological examination. The MRI results were compared with the pathomorphological findings at the injection site by regression analysis. The MR images and the pathological examinations yielded matching results concerning the sizes of the affected tissue volumes or areas. The use of MRI for regulatory safety testing can reduce the number of animals needed to 8 per examination group. The volume of a local reaction and its progression over time can be evaluated and documented. If persistent lesions develop a final pathomorphological examination is needed to identify the kind and local distribution of the reaction

Specific immune modulation of experimental colitis drives enteric alpha-synuclein accumulation and triggers age-related Parkinson-like brain pathology

Background: In some people with Parkinson’s disease (PD), a-synuclein (αSyn) accumulation may begin in the enteric nervous system (ENS) decades before development of brain pathology and disease diagnosis. Objective: To determine how different types and severity of intestinal inflammation could trigger αSyn accumulation in the ENS and the subsequent development of αSyn brain pathology. Methods: We assessed the effects of modulating short- and long-term experimental colitis on αSyn accumulation in the gut of αSyn transgenic and wild type mice by immunostaining and gene expression analysis. To determine the long-term effect on the brain, we induced dextran sulfate sodium (DSS) colitis in young αSyn transgenic mice and aged them under normal conditions up to 9 or 21 months before tissue analyses. Results: A single strong or sustained mild DSS colitis triggered αSyn accumulation in the submucosal plexus of wild type and αSyn transgenic mice, while short-term mild DSS colitis or inflammation induced by lipopolysaccharide did not have such an effect. Genetic and pharmacological modulation of macrophage-associated pathways modulated the severity of enteric αSyn. Remarkably, experimental colitis at three months of age exacerbated the accumulation of aggregated phospho-Serine 129 αSyn in the midbrain (including the substantia nigra), in 21- but not 9-month-old αSyn transgenic mice. This increase in midbrain αSyn accumulation is accompanied by the loss of tyrosine hydroxylase-immunoreactive nigral neurons. Conclusions: Our data suggest that specific types and severity of intestinal inflammation, mediated by monocyte/macrophage signaling, could play a critical role in the initiation and progression of PD