@article{OberdorfSchaschekWeinzierletal.2023,
  author    = {Oberdorf, Felix and Schaschek, Myriam and Weinzierl, Sven and Stein, Nikolai and Matzner, Martin and Flath, Christoph M.},
  title     = {Predictive end-to-end enterprise process network monitoring},
  series = {Business \& Information Systems Engineering},
  volume    = {65},
  journal   = {Business \& Information Systems Engineering},
  number    = {1},
  issn      = {2363-7005},
  doi       = {10.1007/s12599-022-00778-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323814},
  pages     = {49-64},
  year      = {2023},
  abstract  = {Ever-growing data availability combined with rapid progress in analytics has laid the foundation for the emergence of business process analytics. Organizations strive to leverage predictive process analytics to obtain insights. However, current implementations are designed to deal with homogeneous data. Consequently, there is limited practical use in an organization with heterogeneous data sources. The paper proposes a method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks to overcome this limitation. A case study performed with a medium-sized German manufacturing company highlights the method's utility for organizations.},
  language  = {en}
}
@article{GriebelSegebarthSteinetal.2023,
  author    = {Griebel, Matthias and Segebarth, Dennis and Stein, Nikolai and Schukraft, Nina and Tovote, Philip and Blum, Robert and Flath, Christoph M.},
  title     = {Deep learning-enabled segmentation of ambiguous bioimages with deepflash2},
  series = {Nature Communications},
  volume    = {14},
  journal   = {Nature Communications},
  doi       = {10.1038/s41467-023-36960-9},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357286},
  year      = {2023},
  abstract  = {Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool's training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability.},
  language  = {en}
}
@article{LeimeisterStieglitzMatzneretal.2021,
  author    = {Leimeister, Jan Marco and Stieglitz, Stefan and Matzner, Martin and Kundisch, Dennis and Flath, Christoph and R{\"o}glinger, Maximilian},
  title     = {Quo Vadis Conferences in the Business and Information Systems Engineering (BISE) Community After Covid},
  series = {Business \& Information Systems Engineering},
  volume    = {63},
  journal   = {Business \& Information Systems Engineering},
  number    = {6},
  issn      = {2363-7005},
  doi       = {10.1007/s12599-021-00707-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-308902},
  pages     = {741-749},
  year      = {2021},
  language  = {en}
}
@article{Chevalier‐RoignantFlathTrigeorgis2019,
  author    = {Chevalier-Roignant, Beno{\^i}t and Flath, Christoph M. and Trigeorgis, Lenos},
  title     = {Disruptive Innovation, Market Entry and Production Flexibility in Heterogeneous Oligopoly},
  series = {Production and Operations Management},
  volume    = {28},
  journal   = {Production and Operations Management},
  doi       = {10.1111/poms.12995},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-356226},
  pages     = {1641-1657},
  year      = {2019},
  abstract  = {We develop a model of oligopoly competition involving innovation effort, market entry and production flexibility under demand uncertainty. Several heterogeneous firms make efforts to develop new prototypes; if they succeed, they hold a shared option to enter a new market under stochastic demand. We derive analytic results for the Markov perfect equilibrium accounting for development effort, market entry and production decisions and complement these by numerical analyses. Firm value—which embeds real options—is not convex increasing in demand but exhibits "competitive waves" due to market entries by rivals. A firm with a development advantage ("innovator") exerts greater innovation effort if the market is a niche, whereas another benefiting from economies of scale ("incumbent") invests more if the market is larger. Positive externalities benefit the incumbent in the development stage, whereas the innovator is better off in counteracting negative externalities. Demand volatility raises firm incentives to innovate as it enhances the value of firm market-entry and production flexibility.},
  language  = {en}
}