@article{BeierUllrichNiehoffetal.2020,
  author    = {Beier, Grischa and Ullrich, Andr{\´e} and Niehoff, Silke and Reißig, Malte and Habich, Matthias},
  title     = {Industry 4.0},
  series = {Journal of cleaner production},
  volume    = {259},
  journal   = {Journal of cleaner production},
  publisher = {Elsevier Science},
  address   = {Amsterdam},
  issn      = {0959-6526},
  doi       = {10.1016/j.jclepro.2020.120856},
  pages     = {13},
  year      = {2020},
  abstract  = {Industry 4.0 has had a strong influence on the debate on the digitalization of industrial processes, despite being criticized for lacking a proper definition. However, Industry 4.0 might offer a huge chance to align the goals of a sustainable development with the ongoing digital transformation in industrial development. The main contribution of this paper is therefore twofold. We provide a de-facto definition of the concept "Industry 4.0" from a sociotechnical perspective based on its most often cited key features, as well as a thorough review of how far the concept of sustainability is incorporated in it.},
  language  = {en}
}
@phdthesis{Huegle2024,
  author    = {Huegle, Johannes},
  title     = {Causal discovery in practice: Non-parametric conditional independence testing and tooling for causal discovery},
  doi       = {10.25932/publishup-63582},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-635820},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiv, 156},
  year      = {2024},
  abstract  = {Knowledge about causal structures is crucial for decision support in various domains. For example, in discrete manufacturing, identifying the root causes of failures and quality deviations that interrupt the highly automated production process requires causal structural knowledge. However, in practice, root cause analysis is usually built upon individual expert knowledge about associative relationships. But, "correlation does not imply causation", and misinterpreting associations often leads to incorrect conclusions. Recent developments in methods for causal discovery from observational data have opened the opportunity for a data-driven examination. Despite its potential for data-driven decision support, omnipresent challenges impede causal discovery in real-world scenarios. In this thesis, we make a threefold contribution to improving causal discovery in practice. (1) The growing interest in causal discovery has led to a broad spectrum of methods with specific assumptions on the data and various implementations. Hence, application in practice requires careful consideration of existing methods, which becomes laborious when dealing with various parameters, assumptions, and implementations in different programming languages. Additionally, evaluation is challenging due to the lack of ground truth in practice and limited benchmark data that reflect real-world data characteristics. To address these issues, we present a platform-independent modular pipeline for causal discovery and a ground truth framework for synthetic data generation that provides comprehensive evaluation opportunities, e.g., to examine the accuracy of causal discovery methods in case of inappropriate assumptions. (2) Applying constraint-based methods for causal discovery requires selecting a conditional independence (CI) test, which is particularly challenging in mixed discrete-continuous data omnipresent in many real-world scenarios. In this context, inappropriate assumptions on the data or the commonly applied discretization of continuous variables reduce the accuracy of CI decisions, leading to incorrect causal structures. Therefore, we contribute a non-parametric CI test leveraging k-nearest neighbors methods and prove its statistical validity and power in mixed discrete-continuous data, as well as the asymptotic consistency when used in constraint-based causal discovery. An extensive evaluation of synthetic and real-world data shows that the proposed CI test outperforms state-of-the-art approaches in the accuracy of CI testing and causal discovery, particularly in settings with low sample sizes. (3) To show the applicability and opportunities of causal discovery in practice, we examine our contributions in real-world discrete manufacturing use cases. For example, we showcase how causal structural knowledge helps to understand unforeseen production downtimes or adds decision support in case of failures and quality deviations in automotive body shop assembly lines.},
  language  = {en}
}
@phdthesis{Mangelsdorf2009,
  author    = {Mangelsdorf, Stefan},
  title     = {Die Transformation des Verarbeitenden Gewerbes in Berlin/Brandenburg unter Ber{\"u}cksichtigung der Exporte : eine empirische Analyse mit amtlichen Mikrodaten},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-068-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-44414},
  school      = {Universit{\"a}t Potsdam},
  pages     = {241},
  year      = {2009},
  abstract  = {20 Jahre sind mittlerweile vergangen seit die friedliche Protestbewegung zur Abdankung des alten Regimes der Deutschen Demokratischen Republik f{\"u}hrte. Im darauf folgenden Jahre kam es zur Wiedervereinigung der beiden deutschen Staaten. Der anschließende Transformationsprozess ist aufgrund der besonderen Umst{\"a}nde in Deutschland einzigartig unter den ehemaligen sozialistischen Staaten Mittel- und Osteuropas. Der Schwerpunkt dieser Arbeit liegt in der Transformation des Verarbeitenden Gewerbes in den Bundesl{\"a}ndern Berlin und Brandenburg. Mit der Wiedervereinigung der beiden deutschen Staaten ver{\"a}nderte sich die Situation f{\"u}r die Betriebe im ehemals sozialistischen Teil auf dramatische Weise. Die Auswirkungen werden anhand von Makro- und Mikrodaten analysiert. Untersuchungsgegenst{\"a}nde sind verschiedene {\"o}konomische Indikatoren, wie Zahl von Betrieben und Arbeitspl{\"a}tzen, Strukturen (nach Gr{\"o}ße und Branchen), Ums{\"a}tze (im In- und Ausland) sowie Investitionen. Der Vergleich von Brandenburg und Ostberlin mit Westberlin bietet dabei die M{\"o}glichkeit, Aufschluss {\"u}ber den erreichten Stand des Transformationsprozesses zu erhalten. Die Datenbasis dieser Arbeit besteht neben Angaben aus der Volkswirtschaftlichen Gesamtrechnung der L{\"a}nder aus verschiedenen betriebsbasierten Erhebungen der amtlichen Statistik. Der Beobachtungszeitraum umfasst dabei die Jahre 1991 bis 2005. Zur Analyse von Betriebs- und Besch{\"a}ftigungszahlen und ihrer Dynamik steht sogar eine Totalerhebung f{\"u}r die Jahre 1991 bis 2000 zur Verf{\"u}gung. Ein besonderer Schwerpunkt dieser Arbeit ist die Rolle der Exporte f{\"u}r die betriebliche Entwicklung. Die deutsche Wirtschaftspolitik f{\"o}rdert Unternehmen bei ihrem Schritt auf ausl{\"a}ndische M{\"a}rkte, da man sich von Exporten eine Wachstumsstimulation erhofft. Damit eine solche F{\"o}rderung auch langfristige positive Effekte entfalten kann, muss einerseits der Export positiven Einfluss auf das Produktivit{\"a}tswachstum des betreffenden Betriebes haben, und andererseits muss das Exportverhalten eine gewisse Persistenz aufweisen. Beide Bedingungen werden innerhalb der Arbeit detailliert untersucht.},
  language  = {de}
}
@article{SorgeStreeck2018,
  author    = {Sorge, Arndt and Streeck, Wolfgang},
  title     = {Diversified quality production revisited},
  series = {Socio-economic review},
  volume    = {16},
  journal   = {Socio-economic review},
  number    = {3},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1475-1461},
  doi       = {10.1093/ser/mwy022},
  pages     = {587 -- 612},
  year      = {2018},
  abstract  = {We revisit the concept of Diversified Quality Production (DQP), which we introduced about 30 years ago. Our purpose is to examine the extent to which the concept can still be considered tenable for describing and explaining the development of the interaction between the political economy and concepts of production, notably in Germany. First, we show why and in which ways DQP was more heterogeneous than we had originally understood. Then, on the basis of evidence with respect to political, business, and economic changes in Germany, we show that DQP Mark I, a regime by and large characteristic of the 1980s, turned into DQP Mark II. In the process, major 'complementarities' disappeared between the late 1980s and now—mainly the complementarity between production modes on the one hand and industrial relations and economic regulation on the other. While the latter exhibit greater change, business strategies and production organization show more continuity, which helps explain how Germany maintained economic performance after the mid-2000s, more than other countries in Europe. Conceptually, our most important result is that the complementarities emphasized in political economy are historically relative and limited, so that they should not be postulated as stable configurations.},
  language  = {en}
}