@article{HagedornHuegleSchlosser2022,
  author    = {Hagedorn, Christopher and Huegle, Johannes and Schlosser, Rainer},
  title     = {Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning},
  series = {Journal of intelligent manufacturing},
  volume    = {33},
  journal   = {Journal of intelligent manufacturing},
  number    = {7},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0956-5515},
  doi       = {10.1007/s10845-022-01952-x},
  pages     = {2027 -- 2043},
  year      = {2022},
  abstract  = {In discrete manufacturing, the knowledge about causal relationships makes it possible to avoid unforeseen production downtimes by identifying their root causes. Learning causal structures from real-world settings remains challenging due to high-dimensional data, a mix of discrete and continuous variables, and requirements for preprocessing log data under the causal perspective. In our work, we address these challenges proposing a process for causal reasoning based on raw machine log data from production monitoring. Within this process, we define a set of transformation rules to extract independent and identically distributed observations. Further, we incorporate a variable selection step to handle high-dimensionality and a discretization step to include continuous variables. We enrich a commonly used causal structure learning algorithm with domain-related orientation rules, which provides a basis for causal reasoning. We demonstrate the process on a real-world dataset from a globally operating precision mechanical engineering company. The dataset contains over 40 million log data entries from production monitoring of a single machine. In this context, we determine the causal structures embedded in operational processes. Further, we examine causal effects to support machine operators in avoiding unforeseen production stops, i.e., by detaining machine operators from drawing false conclusions on impacting factors of unforeseen production stops based on experience.},
  language  = {en}
}
@article{KononFritschKritikos2018,
  author    = {Konon, Alexander and Fritsch, Michael and Kritikos, Alexander},
  title     = {Business cycles and start-ups across industries},
  series = {Journal of Business Venturing Insights},
  volume    = {33},
  journal   = {Journal of Business Venturing Insights},
  number    = {6},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0883-9026},
  doi       = {10.1016/j.jbusvent.2018.04.006},
  pages     = {742 -- 761},
  year      = {2018},
  abstract  = {We analyze whether start-up rates in different industries systematically change with business cycle variables. Using a unique data set at the industry level, we mostly find correlations that are consistent with counter-cyclical influences of the business cycle on entries in both innovative and non-innovative industries. Entries into the large-scale industries, including the innovative part of manufacturing, are only influenced by changes in the cyclical component of unemployment, while entries into small-scale industries, like knowledge intensive services, are mostly influenced by changes in the cyclical component of GDP. Thus, our analysis suggests that favorable conditions in terms of high GDP might not be germane for start-ups. Given that both innovative and non-innovative businesses react counter-cyclically in 'regular' recessions, business formation may have a stabilizing effect on the economy.},
  language  = {en}
}