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Process mining has emerged as a crucial technology for digitalization, enabling companies to analyze, visualize, and optimize their processes using system data. Despite significant developments in the field over the years, companies—notably small and medium-sized enterprises—are not yet familiar with the discipline, leaving untapped potential for its practical application in the business domain. They often struggle with understanding the potential use cases, associated benefits, and prerequisites for implementing process mining applications. This lack of clarity and concerns about the effort and costs involved hinder the widespread adoption of process mining. To address this gap between process mining theory and real-world business application, we introduce the “Process Mining Use Case Canvas,” a novel framework designed to facilitate the structured development and specification of suitable use cases for process mining applications within manufacturing companies. We also connect to established methodologies and models for developing and specifying use cases for business models from related domains targeting data analytics and artificial intelligence projects. The canvas has already been tested and validated through its application in the ProMiConE research project, collaborating with manufacturing companies.
Based on the increasingly complex value creation networks, more and more event-based systems are being used for decision support. One example of a category of event-based systems is supply chain event management. The aim is to enable the best possible reaction to critical exceptional events based on event data. The central element is the event, which represents the information basis for mapping and matching the process flows in the event-based systems. However, since the data quality is insufficient in numerous application cases and the identification of incorrect data in supply chain event management is considered in the literature, this paper deals with the theoretical derivation of the necessary data attributes for the identification of incorrect event data. In particular, the types of errors that require complex identification strategies are considered. Accordingly, the relevant existing error types of event data are specified in subtypes in this paper. Subsequently, the necessary information requirements and information available regarding identification are considered using a GAP analysis. Based on this gap, the necessary data attributes can then be derived. Finally, an approach is presented that enables the generation of the complete data set. This serves as a basis for the recognition and filtering out of erroneous events in contrast to standard and exception events.
The complexity and volatility of companies’ environment increase the relevance of disruption preparation. Resilience enables companies to deal with disruptions, reduce their impact and ensure competitiveness. Especially in the context of procurement, disruptions can cause major challenges while resilience contributes to ensuring material availability. Even though past disruptions have posed various challenges and companies have recognized the need to increase resilience, resilience is often not designed systematically. One major challenge is the number of potential measures to increase resilience. The systematic design of resilience thus requires a detailed understanding of domain-specific measures. This also includes an understanding of the contribution of these measures to different resilience components and their interdependencies. This paper proposes a systematic approach for configuring resilience in procurement which enables the evaluation and selection of resilience measures. Based on a resilience framework, a resilience configurator is developed. The basis of the configurator are resilience potentials that have been characterized and clustered. Overarching approaches to design resilience and indicators to evaluate resilience are presented. Moreover, a procedure is proposed to ensure practical applicability. To evaluate the results two case studies are conducted. The results enable companies to systematically design their resilience in procurement.
Nachhaltiges Wirtschaften und verantwortungsvoller Umgang mit Ressourcen und Umwelt haben in der deutschen Gesellschaft einen hohen Stellenwert erlangt. Durch eine bessere Produktrückverfolgung und höhere Transparenz in Supply-Chains wird ressourcenschonendere Wertschöpfung ermöglicht. Zusätzlich fordern Kunden eine Einsicht in die Lieferkette und wollen über Produktion und Herkunft der Produkte informiert werden. Die Blockchain als verteilte Datenbank mit außerordentlicher Datensicherheit, Verfügbarkeit von Informationen in Echtzeit im gesamten Netzwerk und hoher Verlässlichkeit bietet dabei die technologische Grundlage, die Transparenz in den Lieferketten zu erhöhen. So können Daten zu Emissionen, Arbeitsbedingungen, Materialherkunft und weiteren Nachhaltigkeitskriterien entlang der Lieferkette aufgenommen und verteilt werden.
Die Anforderungen von Anwendern und Lösungsanbietern an eine Blockchain-Applikation flossen in eine Referenzarchitektur für diese ein. Dabei wurden z. B. die Gestaltung von Schnittstellen, benötigte Daten und Zugangsrichtlinien definiert. Gemeinsam mit dem DIN wurden die Ergebnisse in eine Standardisierung überführt. Anschließend wurden Gestaltungsempfehlungen zur Integration einer Blockchain-Applikation abgeleitet und die Ergebnisse in Unternehmen validiert.
Die Referenzarchitektur dient der erleichterten Entwicklung und Implementierung von Blockchain-Applikationen und damit einer Reduzierung von Kosten, Risiken und Zeitaufwand für KMU. Dem Kunden wird ein besserer Zugang zu Informationen über die Herkunft seiner Produkte ermöglicht, um ökologisch sinnvolle und nachhaltige Kaufentscheidungen treffen zu können.
In der Ära, in der Daten als „neues Gold“ bezeichnet werden, deckt eine Expertise die Kluft zwischen Erkennen und Nutzen dieses Schatzes auf. Sie bietet präzise Einblicke in die Datenmonetarisierung in Deutschland, legt verborgene Potenziale offen und liefert praxisnahe Handlungsempfehlungen, um produzierenden Unternehmen zu helfen, den wahren Wert ihrer Daten gewinnbringend zu nutzen.