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Institute
- FIR e. V. an der RWTH Aachen (34) (remove)
Reinforced through the pandemic and shaped by digitalization, today's professional working environment is in a state of transformation. Working remotely has become a vital component of many professions' regular routines. The design of remote work environments presents challenges to organizations of all sizes. By providing a classification, this paper reveals a comprehensive understanding of the fields of design to be considered to establish lasting remote work concepts in organizations. A hierarchical classification with four dimensions consisting of human, technology, organization, and culture, seven design elements and, twenty design parameters indicates to organizations the fields of design that need to be examined. To satisfy both the theoretical foundation and the practical application, design elements are derived by implementing a systematic review of the literature that represents key areas of interest for remote work. Additionally, these are verified and complemented by a dedicated case study research to incorporate practice-oriented design parameters.
Die resilienten Unternehmen der Zukunft kooperieren mit ihren Wettbewerbern, schreiben Gerrit Hoeborn, Daniel Spindler und Lukas Stratmann vom FIR an der RWTH Aachen. Doch nur mit einer klaren Strategie und einem gesunden Business-Ecosystem stellt sich der gewünschte Erfolg ein. In ihrem Gastbeitrag erläutern die Experten, was genau Business-Ecosystems und Koopetition sind. Sie beschreiben Strategien für den langfristigen Erfolg resilienter Unternehmen im Business-Ecosystem anhand eines Praxisbeispiels.
Ziel des Forschungsprojekts ‚PROmining‘ war die unternehmensneutrale Konzeptionierung, Entwicklung und Realisierung eines webbasierten Demonstrators zur Verbesserung der Prognosefähigkeit und Erhöhung der Kapazitätsauslastung von KMU in der deutschen Steine- und Erdenindustrie. Mit dem geplanten Demonstrator einer Plattformlösung sollte ein Anreiz für KMU geschaffen werden, die digitale Transformation anzugehen und die interne Datenhaltung zu verbessern. Das Projekt wurde vom FIR e. V. an der RWTH Aachen in Kooperation mit dem Institute of Mineral Resources Engineering der RWTH Aachen durchgeführt.
Studien zeigen, dass die meisten Business-Ecosystems langfristig an unzureichender Governance scheitern. Daher hat das FIR an der RWTH Aachen eine Entscheidungshilfe entwickelt, die eine Unterstützung zur Auswahl vertragsrechtlicher Instrumente liefert. Dieses Werkzeug richtet sich an Orchestratoren, um Rechtssicherheit zu schaffen und den langfristigen Erfolg des Business-Ecosystems zu fördern.
Ziel des Forschungsprojekts OKReady war die Entwicklung eines Konzepts zur Einführung des agilen Managementsystems Objectives and Key Results (OKR) in kleinen und mittleren Unternehmen (KMU). OKR liefern eine effektive Möglichkeit, die Priorisierungsfähigkeit sowie Kommunikation und Transparenz im Unternehmen zu verbessern, Leistung klar zu messen und Mitarbeiterengagement zu stärken. OKR ermöglicht KMU die Tätigkeiten ihrer Angestellten an einer gemeinsamen Vision auszurichten und Unternehmensziele transparent über alle Hierarchieebenen abzubilden.
Ziel des Forschungsprojekts RAcceptance war die dauerhafte Nutzung der Effizienzpotenziale von Robotic-Process-Automation (RPA) in KMU durch die Förderung der Akzeptanz. Es wurden diejenigen Faktoren bestimmt und adressiert, welche die Akzeptanz der Nutzung von RPA-Software positiv sowie negativ beeinflussen.
Die pandemiebedingt angestiegene Homeofficequote in produzierenden
Unternehmen ist seit Juli 2020 deutlich rückläufig und indiziert ein
geringes Maß an langfristig gestalteten hybriden Arbeitsplatzkonzepten.
Angesichts des Fachkräftemangels besteht Handlungsdruck, eine
attraktive Arbeitsumgebung mit industriellen Tätigkeiten zu vereinbaren.
Um zukunftsorientierte Arbeitsplatzkonzepte zu gestalten, nennt
das vorgestellte Vorgehen systematisch die menschlichen Tätigkeiten
in produzierenden Unternehmen und bewertet deren Remotefähigkeit.
The use of Business Analytics (BA) helps to improve the quality of decisions and reduces reaction latencies, especially in uncertain and volatile market situations. This expectation leads a continuously rising number of companies to make large investments in BA. The successful use of Business Analytics is increasingly becoming a differentiator. At the same time, the use of BA is not trivial, rather, it is subject to high socio-technical requirements. If these are not addressed, high risks arise that stand in the way of successful use. In particular, it is important to consider the risks in relation to the different types of BA in a differentiated way. So far, there is a lack of suitable approaches in the literature to consider these type-specific risks with regard to the socio-technical dimensions: people, technology, and organization. This paper addresses this gap by initially identifying risks in the use of Business Analytics. For this purpose, possible risks are identified using a systematic literature review and verified with a Delphi survey with various partners experienced in dealing with BA. Subsequently, the identified and validated risks are assigned to three different types of Business Analytics (Descriptive, Predictive and Prescriptive Analytics) and assessed in order to systematically address and reduce the risks. The result of this paper is an overview of the interactions between the socio-technically assigned risks, summarized in a risk catalog, and the different types of Business Analytics.
The manufacturing industry consumes 54% of global energy and attributes for 20% of global CO2 emissions, demonstrating the industry’s role as global driver of climate change. Therefore, reducing its carbon footprint has become a major challenge as its current energy and resource consumption are not sustainable. Industrie 4.0 presents a chance to transform the prevailing paradigms of industrial value creation and advance sustainable developments. By using information and communication technologies for the intelligent networking of machines and processes, it has the potential to reduce energy and material consumption and is considered a key contributor to sustainable manufacturing as proclaimed by the European Commission in the term “twin transition”. As organizations still struggle to utilize the potential of Industrie 4.0 for a sustainable transformation, this paper presents a framework to successfully align their own twin transition. The framework is built upon three key design principles (micro level: leverage eco-efficient operations, meso level: facilitate circularity and macro level: foster value co-creation) derived using case study research by Eisenhardt, and four structural dimensions (resources, information systems, organizational structure and culture) based on the acatech Industrie 4.0 Maturity Index. Eleven interconnected areas of action are defined within the framework and offer a holistic and practical approach on how to leverage an organization’s twin transition. Within the conducted research, the framework was applied to the challenge of information quality and transparency required for high-value secondary plastics in the manufacturing industry. The result is a digital platform design that enables information transactions for secondary plastics and establishes a circular ecosystem. This shows the applicability of the framework and its potential to facilitate a structured approach for designing twin transitions in the manufacturing industry.
The European Commission set out the goal of carbon neutrality by 2050, which shall be achieved by fostering the twin transition - sustainability through digitalization. A keystone in this transition is the implementation of a prospering Circular Economy (CE). However, product information required to establish a flourishing CE is hardly available or even accessible. The Digital Product Passport (DPP) offers a solution to that problem but in the current discussion, two separate topics are focused on: its architecture and its application on batteries. The content of the DPP has not been an essential part of the discussion, although access to high-quality data about a product's state, composition and ecological footprint is required to enable sustainable decision-making. Therefore, this paper presents a classification of product data for circularity in the manufacturing industry to emphasize the discussion about the DPP's content. Developed through a systematic literature review combined with a case-study-research based on common operational information systems, the classification comprises three levels with 62 data points in four main categories: (1) Product information, (2) Utilization information, (3) Value chain information and (4) Sustainability information. In this paper, the potential content structure of a DPP is demonstrated for a use case in the machinery sector. The contribution to the science and operations community is twofold: Building a guideline for DPP developers that require scientific input from available real-world data points as well as motivating manufacturers to share the presented data points enabling a circular product information management.