TY  - CONF
A1  - Rix, Calvin
A1  - Stein, Hannah
A1  - Chen, Qiang
A1  - Frank, Jana
A1  - Maass, Wolfgang
T1  - Conceptualizing Data Ecosystems for Industrial Food Production
T2  - 2021 IEEE 23rd Conference on Business Informatics (CBI)
N2  - 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.
KW  - Data Ecosystems
KW  - Digital Business Models
KW  - Ecosystem Design
KW  - Value Stream Mapping
KW  - Industrial Food Production
Y1  - 2022
UR  - https://epub.fir.de/frontdoor/index/index/docId/1247
UR  - https://ieeexplore.ieee.org/document/9610691
SN  - 2378-1971
SP  - 201
EP  - 210
ER  -