IIC Journal of Innovation 17th Edition Applying Solutions at the Digital Edge | Page 37

Driving Industry 4.0 at Distributed Edges with Cloud Orchestration
IDS-Association and being realized in several open source projects [ 45 ]. The question on how data sovereignty and the IDS concepts impact edge computing solutions goes beyond the scope of this paper and is subject of future contributions .
Initiatives such as Gaia-X [ 48 ] and Catena-X [ 49 ] are also considering edge computing as a fundamental infrastructure in their reference architecture . Gaia-X vision aims at establishing a federated data infrastructure fulfilling data sovereignty and security via standards and open source . Catena-X follows a similar vision of a secure and sovereign data space for companies and organizations involved in the automotive value chain . In both organizations , standards such as AAS and IDS are being considered . A detailed discourse on how these standards and initiatives such as Gaia-X and Catena-X help edge computing solutions to provide a better value to the industry should be topic of future works .
DETAILED LOGISTICS AND MANUFACTURING EXECUTION SCENARIOS
In this section , selected mission critical scenarios for logistics and manufacturing from [ 3 ] will be discussed using the edge computing approach introduce in the previous section . The goal is to elaborate the “ added value proposition ” of the solutions .
Smart Sensing at the Edge
Smart Sensing refers to automatically identifying physical objects through events , enriching them with business context and integrating them into business processes , without human intervention . Technologies typically considered as part of Smart Sensing include QR codes , RFID , optical character recognition , voice recognition and others .
Smart Sensing can identify movements of physical objects such as inbound or outbound deliveries on handling units such as pallets , containers , or tanks in real-time - even at high speed . When combined with edge computing , smart sensing can be leveraged to fully automate process steps in logistics and eliminate human errors . The edge nodes are key components as in real-life warehouse settings each scan needs to return immediate feedback to the warehouse worker . Turnaround time to the cloud interrupting the workflow on the ground can ’ t be tolerated .
Technically , cloud and edge components of SAP IoT work together to make this happen . Relevant hierarchical business context such as sales orders , deliveries , and handling units as well as customer and material master data is managed centrally in ERP – for example in S / 4HANA . SAP IoT manages subscriptions to this business data and how smart sensing events are associated with individual handling units , for example . Decoupled synchronization between cloud and edge enables low-latency execution of smart sensing scenarios at the edge . Smart sensing events from handling units captured in a warehouse are ingested into the local edge node and set into its business context – now locally available at the edge . Business rules executed in the same edge node validate the events and provide instantaneous feedback to warehouse workers so that the work can continue . Asynchronously , the respective goods issue is posted to S / 4HANA .
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