Refine
Document Type
- Book (2)
- Part of a Book (1)
- Conference Proceeding (3)
- Report (2)
Is part of the Bibliography
- no (8)
Keywords
- 02 (1)
- 03 (1)
- Big Data (1)
- Data Ecosystems (1)
- Data-Analytics (1)
- Datenbilanz (1)
- Datenkapital (1)
- Digital Business Models (1)
- Digitalisierung (2)
- Ecosystem Design (1)
- ICS 35.240.63; 67.020 (1)
- Industrial Food Production (1)
- Industrie 4.0 (1)
- SV7263 (1)
- Smart Farming (2)
- Smart-Service-Welt (2)
- Value Stream Mapping (1)
- case study research (1)
- data ecosystems (1)
- data sharing (1)
- data valuation framework (1)
- data value (2)
- food industry (1)
- industry 4.0 (1)
- intangible assets (1)
- rev (1)
Institute
- FIR e. V. an der RWTH Aachen (8) (remove)
Smart-Farming-Welt
(2019)
Smart-Farming-Welt
(2019)
Das Ziel des Forschungsprojekts "Future Data Assets" bestand in der monetären Bewertung des unternehmerischen Datenkapitals. Dazu wurden die Entwicklung und Instanziierung einer sogenannten "Datenbilanz" angestrebt. Die Datenbilanz soll dem Reporting der unternehmerischen Fähigkeit der Datenbewirtschaftung dienen und damit eine Lücke im Hinblick auf die klassische Berichterstattung schließen, in der Daten kaum betrachtet bzw. systematisch bewertet werden.
Since data becomes more and more important in industrial context, the question arises on how data-driven added value can be measured consistently and comprehensively by manufacturing companies. Currently, attempts on data valuation are primarily taking place on internal company level and qualitative scale. This leads to inconclusive results and unused opportunities in data monetization. Existing approaches in theory to determine quantitative data value are seldom used and less sophisticated. Although quantitative valuation frameworks could enable entities to transfer data valuation from an internal to an external level to take account of progress in digital transformation into external reporting. This paper contributes to data value assessment by presenting a four-part valuation framework that specifies how to transfer internal, qualitative to external, quantitative data valuation. The proposed framework builds on insights derived from practice-oriented action research. The framework is finally tested with a machine tool manufacturer using a single case study approach. Placing value on data will contribute to management’s capability to manage data as well as to realize data-driven benefits and revenue. [https://link.springer.com/chapter/10.1007/978-3-030-85902-2_19]
DIN SPEC 91452:2022-01
(2022)
Big data are collected along the entire food industry value chain, but remain mostly unused. Data sharing in data ecosystems could lead to efficiency gains and new revenue streams. We investigate data sharing within food industry and derive challenges and opportunities for data sharing in this context. We conducted interviews with ten qualified experts from the German food industry. The results reveal that mainly trust, usefulness and value influence users’ attitude towards data sharing. Our results confirm social exchange theory in conjunction with technology acceptance model as relevant underlying IS theories of data sharing.
Industrial food production represents one of the largest industries, accounting for a share of ten percent of the world’s gross domestic product. Simultaneously, it is responsible for 26 percent of global greenhouse gas emissions. Due to increasing CO2 taxes and population’s call for sustainability and CO2 reduction, it is facing challenges in terms of economic profitability and stakeholder demands. These challenges could partly be overcome by participating in data ecosystems in which data are refined as data products, understood, exchanged and monetized as economic goods. Despite large amounts of data, collected parenthetically along the value chain in food production, potentials of data analytics and data ecosystems are only marginally exploited. Food production mainly focuses on traditional, product-centric business models. This work shows the conceptualization of a data ecosystem for food production, enabling data-based business models. Therefore, resources, ac- tors, roles and underlying relationships of future ecosystem are analyzed. Building on these, corresponding architectural and analytical artifacts that support data ecosystem exploitation are presented. A food production data ecosystem is exemplified by applying data analytics to compressor data, which reveals high potentials for CO2 reduction.
ZusammenfassungDieser Beitrag stellt dar, welche Chancen und Herausforderungen mit der Bewertung von Daten sowie der Abbildung monetärer Datenwerte verbunden sind und geht auf mögliche Lösungsansätze zur Bewertung von Unternehmensdatenbeständen, insbesondere im Kontext der industriellen Produktion, ein. Zunächst werden Grundlagen zur Charakterisierung, Nutzung und Verwertung von Daten sowie bestehende Methoden zur Bewertung von immateriellen Vermögensgegenständen dargestellt. Darauf aufbauend werden Chancen und Herausforderungen spezifiziert, potenzielle Lösungsansätze zur Datenbewertung abgeleitet und anschließend Anforderungen für die Datenbewertung beschrieben sowie die nutzenorientierte Datenbewertung skizziert.