IT-enabled Process Innovation: A Literature Review

The importance of Information Technology (IT) is growing, and in a hypercompetitive market IT must be used as a strategic asset for companies to succeed. In order to gain strategic benefits from IT, companies need to be innovative when deploying IT. This can be achieved by reengineering business processes to take advantage of the possibilities IT provides. In 1993 Thomas H. Davenport presented a framework describing the role of IT in process innovation . Based on this framework, the purpose of this paper is to conduct a literature review to answer the following research question: What kind of opportunities does IT provide for process innovation? . Davenport\u27s framework is used as an analytical lens to review articles from the top 20 IS and management journals. The paper provides an overview and an in-depth analysis of the literature on IT-enabled process innovation and suggests avenues for future research as well as recommendations for practitioners. Our analyses reveal five distinct themes related to opportunities for IT-enabled process innovation, all of which offer guidance to practitioners and highlight gaps in our current knowledge about how to leverage IT for innovation purposes

Fast and effortless computation of profile likelihoods using CONNECT

The frequentist method of profile likelihoods has recently received renewed attention in the field of cosmology. This is because the results of inferences based on the latter may differ from those of Bayesian inferences, either because of prior choices or because of non-Gaussianity in the likelihood function. Consequently, both methods are required for a fully nuanced analysis. However, in the last decades, cosmological parameter estimation has largely been dominated by Bayesian statistics due to the numerical complexity of constructing profile likelihoods, arising mainly from the need for a large number of gradient-free optimisations of the likelihood function. In this paper, we show how to accommodate the computational requirements of profile likelihoods using the publicly available neural network framework CONNECT together with a novel modification of the gradient-based $basin$-$hopping$ optimisation algorithm. Apart from the reduced evaluation time of the likelihood due to the neural network, we also achieve an additional speed-up of 1$-$2 orders of magnitude compared to profile likelihoods computed with the gradient-free method of $simulated$ $annealing$, with excellent agreement between the two. This allows for the production of typical triangle plots normally associated with Bayesian marginalisation within cosmology (and previously unachievable using likelihood maximisation because of the prohibitive computational cost). We have tested the setup on three cosmological models: the $\Lambda$CDM model, an extension with varying neutrino mass, and finally a decaying cold dark matter model. Given the default precision settings in CONNECT, we achieve a high precision in $\chi^2$ with a difference to the results obtained by CLASS of $\Delta\chi^2\approx0.2$ (and, importantly, without any bias in inferred parameter values) $-$ easily good enough for profile likelihood analyses.Comment: 23 pages, 9 figure

Aeolus Toolbox for Dynamics Wind Farm Model, Simulation and Control

This paper presents the wind farm simulation model developed in the EU-FP7 project, AEOLUS. The idea is to provide a publicly available simulation package for researchers developing farm level con-trol solutions. With the software it is possible to auto generate a wind farm simulation model in Mat-lab/Simulink based on turbine parameters and farm geometry. The input to the farm simulator is power set points of individual turbines. Outputs from the farm simulation are power production, nacelle wind speed and fatigue loads (damage equivalent loads) of each turbine.

Data Acquisition for Quality Loss Function Modelling

AbstractQuality loss functions can be a valuable tool when assessing the impact of variation on product quality. Typically, the input for the quality loss function would be a measure of the varying product performance and the output would be a measure of quality. While the unit of the input is given by the product function in focus, the quality output can be measured and quantified in a number of ways. In this article a structured approach for acquiring stakeholder satisfaction data for use in quality loss function modelling is introduced