Initial commit: 3-year shopfloor IT/OT transformation plan

Core plan: current-state, goal-state (layered architecture, OtOpcUa,
Redpanda EventHub, SnowBridge, canonical model, UNS posture + naming
hierarchy, digital twin use cases absorbed), roadmap (7 workstreams x 3
years), and status bookmark.

Component detail files: legacy integrations inventory (3 integrations,
pillar 3 denominator closed), equipment protocol survey template (dual
mandate with UNS hierarchy snapshot), digital twin management brief
(conversation complete, outcome recorded).

Output generation pipeline: specs for 18-slide mixed-stakeholder PPTX
and faithful-typeset PDF, with README, design doc, and implementation
plan. No generated outputs yet — deferred until source data is stable.

digital_twin_usecases.md.txt

@@ -0,0 +1,74 @@
+1) Standardized Equipment State / Metadata Model
+
+Use case:
+Create a consistent, high-level representation of machine state derived from raw signals.
+
+What it does:
+	•	Converts low-level sensor/PLC data into meaningful states (e.g., Running, Idle, Faulted, Starved, Blocked)
+	•	Normalizes differences across equipment types
+	•	Aggregates multiple signals into a single, authoritative “machine state”
+
+Examples:
+	•	Deriving true run state from multiple interlocks and status bits
+	•	Calculating actual cycle time vs. theoretical
+	•	Identifying top fault instead of exposing dozens of raw alarms
+
+Value:
+	•	Provides a single, consistent view of equipment behavior
+	•	Reduces complexity for downstream systems and users
+	•	Improves accuracy of KPIs like OEE and downtime tracking
+
+⸻
+
+2) Virtual Testing / Simulation (FAT, Integration, Validation)
+
+Use case:
+Use a digital representation of equipment to simulate behavior for testing without requiring physical machines.
+
+What it does:
+	•	Emulates machine signals, states, and sequences
+	•	Allows testing of automation logic, workflows, and integrations
+	•	Supports replay of historical scenarios or generation of synthetic ones
+
+Examples:
+	•	Simulating startup, shutdown, and fault conditions
+	•	Testing alarm handling and recovery workflows
+	•	Validating system behavior under edge cases (missing data, delays, abnormal sequences)
+
+Value:
+	•	Enables earlier testing before equipment is available
+	•	Reduces commissioning time and risk
+	•	Improves quality and stability of deployed systems
+
+⸻
+
+3) Cross-System Data Normalization / Canonical Model
+
+Use case:
+Act as a common semantic layer between multiple systems interacting with manufacturing data.
+
+What it does:
+	•	Defines standardized data structures for equipment, production, and events
+	•	Translates system-specific formats into a unified model
+	•	Provides a consistent interface for all consumers
+
+Examples:
+	•	Mapping different machine tag structures into a common equipment model
+	•	Standardizing production counts, states, and identifiers
+	•	Providing uniform event definitions (e.g., “machine fault,” “job complete”)
+
+Value:
+	•	Simplifies integration between disparate systems
+	•	Reduces duplication of transformation logic
+	•	Improves data consistency and interoperability across the enterprise
+
+⸻
+
+Combined Outcome
+
+Together, these three use cases position a digital twin as:
+	•	A translator (raw signals → meaningful state)
+	•	A simulator (test without physical dependency)
+	•	A standard interface (consistent data across systems)
+
+This approach focuses on practical operational value rather than high-fidelity modeling, aligning well with discrete manufacturing environments.