Data Spaces for Sustainability and Resilience in Manufacturing

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    Architecture Design Options for Federated Data Spaces
    ( 2023-01-03) Schleimer, Anna Maria ; Jahnke, Nils ; Otto, Boris
    The massive growth of data and the increasing potential of data analytics in industrial production fuel the emergence of data spaces and corresponding platforms that realize data ecosystems and enable data-driven sustainability applications. To leverage their benefits of demand-driven and scalable data integration, the stakeholders of emerging data space initiatives must make informed decisions about their data space support platforms (DSSPs). This study proposes a conceptual framework based on federated architectures and by considering existing endeavors of data infrastructures. Based on existing literature about data ecosystem resources and an explorative single case study of an industrial data space with sustainability-focused applications, we elaborate on the key design options of data, services, and computing infrastructures. The resulting conceptual framework guides design decisions for DSSPs. The framework captures not only the resources involved but also the operational concepts of federated services and shared services to introduce governance mechanisms and sustainability policies.
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    Introduction to the Minitrack on Data Spaces for Sustainability and Resilience in Manufacturing
    ( 2023-01-03) Jarke, Matthias ; Koren, István ; Piller, Frank ; Elmaraghy, Hoda
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    Sustainability and Resilience in Alliance-Driven Manufacturing Ecosystems: A Strategic Conceptual Modeling Perspective
    ( 2023-01-03) Koren, István ; Jarke, Matthias ; Piller, Frank ; Elmaraghy, Hoda
    The challenge of sustainability rests on the ability of organizations to change their practices to meet the needs of current and future generations. To date, most research on organizational change has focused on how to change within a single organization. However, an increasing number of sustainability challenges require changes across multiple organizations. In this paper, we summarize strategic challenges faced in such a setting and outline a conceptual modeling approach for strategic analysis of alliance-driven solutions. We illustrate our ideas with a case study in digital agriculture, a field particularly relevant to sustainability, and end with the identification of issues for further research.
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    A Strategic Framework for Achieving Sustainability and Resilience in Global Supply Chains
    ( 2023-01-03) Klement, Peter ; Auktor, Georgeta ; Pal, Timea ; Olivetti, Elsa ; Blondaut, Josèphe ; Birn, Lukas ; Anding, Markus ; Knipfer, Kristin
    In order to achieve sustainability and resilience at the same time in global supply chains, a strategic framework and ecosystem collaboration is required to orchestrate the activities of the different supply chain participants to achieve a common goal. While the necessity of ecosystems is understood and accepted, the successful implementation of those remains a challenge. This paper looks from the perspective of practitioners at this challenge, identifying the critical success factors to make a collaboration ecosystem work. Based on the analysis of existing strategy concepts, ESG frameworks and of several ecosystems, a strategy framework is developed that can serve as a blueprint to successfully create global value networks that balance sustainability and resilience concerns using data and analytics.
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    Anticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning
    ( 2023-01-03) Janzen, Sabine ; Gdanitz, Natalie ; Abdel Khaliq, Lotfy ; Munir, Talha ; Franzius, Christoph ; Maass, Wolfgang
    Power outages and fluctuations represent serious crisis situations in energy-intensive process industry like glass and paper production, where substances such as oil, gas, wood fibers or chemicals are processed. Power disruptions can interrupt chemical reactions and produce tons of waste as well as damage of machine parts. But, despite of the obvious criticality, handling of outages in manufacturing focuses on commissioning of expensive proprietary power plants to protect against power outages and implicit gut feeling in anticipating potential disruptions. With AISOP, we introduce a model for AI-based scenario planning for predicting crisis situations. AISOP uses conceptual, well-defined scenario patterns to capture entities of crisis situations. Data streams are mapped onto these patterns for determining historic crisis scenarios and predicting future crisis scenarios by using inductive knowledge and machine learning. The model was exemplified within a proof of concept for energy-driven disruption prediction. We were able to evaluate the proposed approach by means of a set of data streams on weather and outages in Germany in terms of performance in predicting potential outages for manufacturers of paper industry with promising results.