The authors introduce "Template-as-Ontology," a framework where a single Python configuration file defines both a manufacturing simulator and the runtime schema for AI analytics tools, guaranteeing alignment by construction. A five-layer pipeline generates causally coherent, MES-shaped synthetic data across six industry domains (aerospace, pharma, automotive, etc.) mapped to ISA-95 standards. They validate that ontology-constrained tool parameters eliminate hallucination—0% fabricated tool parameters when constrained versus 43% unconstrained for Qwen3-32B—because structural constraints are enforced at the architecture level, not learned.
Main takeaways:
- A single configuration module serves as both the simulator spec and the AI tool schema, ensuring structural alignment automatically.
- Five-layer pipeline produces synthetic manufacturing data spanning 66 entity types across four operational domains.
- Validated on six industry templates running identical framework code; observed KPIs fall within configured ranges.
- Ontology-constrained parameters achieve 0% tool-parameter hallucination versus 43% unconstrained (Fisher's exact test p < 10^-12).
- The 0% hallucination rate is an architectural guarantee from enforced constraints, independent of which model you use.