Adaptive Cloud

Network production and enable real-time decisions
Herausforderung

Distributed OT/asset data is difficult to integrate, hard to compare, and rarely usable in real-time.

Adaptive Cloud is a solution domain for distributed production and asset environments where data, states, and events from machines, lines, and locations are consistently captured, consolidated, and provided as a basis for operational decisions. The solution addresses hybrid realities (Edge, On-Premises, Cloud) and creates a robust structure to systematically enable monitoring, analysis, assistance, and controlled automation in industrial systems. Within an overall enterprise AI architecture, Adaptive Cloud forms the bridge between OT reality, integration capability, and AI/agent workloads.

Relevanz

Warum das jetzt relevant ist

  • Multiple machines or lines with heterogeneous controls and data formats (Brownfield/Mixed Fleet)
  • Multiple plants/locations with varying operating conditions and network restrictions
  • 24/7 monitoring of states, deviations, and quality indicators throughout production
  • Incident and event management with escalation chains between shop floor, maintenance, and IT/OT
  • Predictive and condition monitoring scenarios with historical event reference (failure patterns, remedies, lessons learned)
  • Operational Knowledge Base: Standard responses, action catalogs, shift knowledge, and recurring fault patterns
  • Controlled execution of actions (start/stop, rescheduling, maintenance trigger) within clear authorization limits
  • Audit and compliance-relevant environments with requirements for traceability of data and decisions
Ansatz

System & Struktur des Ansatzes

  • Asset & Context Model: Uniform structure for assets, lines, sensors, parameters, states, events, and responsibilities
  • Data Acquisition & Normalization: Consistent capture, time reference, quality characteristics, plausibility checks, and harmonization of heterogeneous signals
  • Connectivity & Integration Layer: Standardized connection to OT sources and coupling to IT systems (e.g., maintenance, quality, ERP/MES)
  • Event & State Management: Mapping of normal states, deviations, thresholds, anomalies, incidents, and their context data
  • Observability for OT: Ongoing transparency regarding data flows, system states, model/rule versions, and relevant operational events
  • Agent-enabled operating mode (functional loop): Notify – Propose – Act – Learn as a structured separation of recognition, proposal, action, learning
  • Human-in-the-loop control boundaries: Clear owner responsibility, authorization rights, secure action spaces, and documented decision paths
  • Security & Access Layer: Identities, roles, segmentation, logging, and protection of critical control and operational functions
  • Operations & Lifecycle Framework: Versioning of rules/models, change traceability, rollouts across locations, operational stability‍
Outcome

Struktureller Mehrwert für Unternehmen

  • Comparable, contextualized OT data foundation as a prerequisite for scalable analytics, assistance, and agent functions
  • Reduced friction between OT/IT through standardized integration and state models (less special logic per asset)
  • Faster troubleshooting through event references, reusable fault patterns, and documented remedies
  • Higher asset availability through earlier detection of deviations and structured proposals for corrective actions
  • Controllable automation: Actions are clearly defined, traceable, and linked to responsibilities
  • Knowledge transferability: Shift and expert knowledge is systematically discoverable and embeddable into operational workflows
  • Auditability: Data provenance, state changes, suggestions, decisions, and actions are made traceable in a structured manner

Die Umsetzung erfolgt durch spezialisierte Teams

IoT & Adaptive Cloud

Cloud, Edge, and AI solutions for real-world industrial processes. We connect systems, machines, and data – for smart factories and adaptive automation.