Build an OPC UA Supplier Chargeback and Quality Hold Calculator
Parse open62541 OPC UA lot tracking nodes to isolate defective components. Calculate supplier chargeback costs based on scrapped sub-assemblies and generate quality hold manifests for affected inventory.
What you are building
The core problem, expected build, and operating context for this challenge.
Parse open62541 OPC UA lot tracking nodes to isolate defective components. Calculate supplier chargeback costs based on scrapped sub-assemblies and generate quality hold manifests for affected inventory.
Plant Quality and Traceability Manager
Ingest normalized OPC UA node variables representing component batch IDs and workstation transitions.
Traverse forward component lot consumption links to isolate downstream finished assemblies containing defective parts.
Calculate total scrapped material value by summing the unit costs of the affected sub-assemblies and finished units.
Synthesize an itemized supplier chargeback report detailing affected inventory lots and financial recovery sums.
Web dashboard showing interactive lot tree traversals, financial scrap calculators, and one-click chargeback manifest export.
Generate 400 normalized JSON records modeled after open62541 OPC UA node variable tables with parent-child lot relations, timestamps, station IDs, and unit cost tables. Starter examples below are synthetic. They are not downloaded records.
open62541 OPC UA Utilizes open62541 OPC UA information model conventions for node attributes and hierarchical references to structure lot tracking variables.
pydantic-ai, networkx, fastapi. Pydantic AI guarantees strictly validated schema structures for complex lot graph traversals and financial chargeback data outputs. Requires strong structured data extraction and logical reasoning via hosted API or local 8B+ parameter model.
Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.
Reconstructs full parent-child sub-assembly lineage for multi-stage component lots without circular references.
Identifies 100% of finished assemblies containing components from designated contaminated input lots.
Exports a structured JSON supplier chargeback manifest containing hold dispositions and accurate sum of scrapped material costs.
```json [ { "input": { "data_context": "Simplified synthetic app records, not complete official schema files", "suspect_lot_id": "RAW-CAP-8841", "genealogy_records": [ { "node_id": "ns=2;s=Station1.Mount", "component_lot": "RAW-CAP-8841", "parent_lot": "PCB-SUB-101" }, { "node_id": "ns=2;s=Station2.Enclosure", "component_lot": "PCB-SUB-101", "parent_lot": "FIN-ASSY-900" } ], "unit_costs": { "PCB-SUB-101": 45, "FIN-ASSY-900": 120 } }, "expected_output": { "target_suspect_lot": "RAW-CAP-8841", "affected_subassemblies": [ "PCB-SUB-101" ], "quarantine_units": [ "FIN-ASSY-900" ], "chargeback_total": 165, "containment_status": "ISOLATED" } }, { "input": { "data_context": "Simplified synthetic app records, not complete official schema files", "suspect_lot_id": "RAW-RES-4040", "genealogy_records": [ { "node_id": "ns=2;s=Station1.Mount", "component_lot": "RAW-RES-9999", "parent_lot": "PCB-SUB-200" } ], "unit_costs": { "PCB-SUB-200": 30 } }, "expected_output": { "target_suspect_lot": "RAW-RES-4040", "affected_subassemblies": [], "quarantine_units": [], "chargeback_total": 0, "containment_status": "CLEAN" } } ] ```
Read-only simulation data; strictly mock containment tags with no direct actuation of factory conveyors or automated storage systems. Use mock external writes. Require human review before any real deployment.
Participation guide
Read-only simulation data; strictly mock containment tags with no direct actuation of factory conveyors or automated storage systems. Use mock external writes and human review.
How work is evaluated
Reconstructs full parent-child sub-assembly lineage for multi-stage component lots without circular references. Identifies 100% of finished assemblies containing components from designated contaminated input lots. Exports a structured JSON supplier chargeback manifest containing hold dispositions and accurate sum of scrapped material costs.
Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Complete workflow
Ingest normalized OPC UA node variables representing component batch IDs and workstation transitions. Traverse forward component lot consumption links to isolate downstream finished assemblies containing defective parts. Calculate total scrapped material value by summing the unit costs of the affected sub-assemblies and finished units. Synthesize an itemized supplier chargeback report detailing affected inventory lots and financial recovery sums.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
Reconstructs full parent-child sub-assembly lineage for multi-stage component lots without circular references. Identifies 100% of finished assemblies containing components from designated contaminated input lots. Exports a structured JSON supplier chargeback manifest containing hold dispositions and accurate sum of scrapped material costs.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
Web dashboard showing interactive lot tree traversals, financial scrap calculators, and one-click chargeback manifest export.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
Read-only simulation data; strictly mock containment tags with no direct actuation of factory conveyors or automated storage systems.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Ingest normalized OPC UA node variables representing component batch IDs and workstation transitions.
Traverse forward component lot consumption links to isolate downstream finished assemblies containing defective parts.
Calculate total scrapped material value by summing the unit costs of the affected sub-assemblies and finished units.
Synthesize an itemized supplier chargeback report detailing affected inventory lots and financial recovery sums.
Reference links and supporting material
Generate 400 normalized JSON records modeled after open62541 OPC UA node variable tables with parent-child lot relations, timestamps, station IDs, and unit cost tables. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
Pydantic AI
Typed Python agent framework.
Evaluated output
No output checks are configured.
- Ingest normalized OPC UA node variables representing component batch IDs and workstation transitions. Trave...
- Reconstructs full parent-child sub-assembly lineage for multi-stage component lots without circular referen...
- Web dashboard showing interactive lot tree traversals, financial scrap calculators, and one-click chargebac...
- 2 public reference cases
- 2 configured tools
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Pydantic AI · Optional
- Pydantic AI · Optional
Evaluation contract
- Complete workflow · Weight 40
- Correctness and evidence · Weight 30
- Review interface · Weight 20
- Safe execution · Weight 10
Reference cases
- 2 public reference cases
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
No recipe freeze time is recorded.