level-5: Ankit Kumar Singh#535
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Pull request overview
This PR adds a Level 5 written submission for graph-thinking around the factory production dataset, including schema design, query reasoning, bottleneck analysis, hybrid vector/graph concepts, and a Level 6 dashboard plan.
Changes:
- Adds Level 5 answers covering all five challenge questions.
- Adds a Mermaid-based factory knowledge graph schema.
- Adds a reflection document describing the author’s process.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 7 comments.
| File | Description |
|---|---|
submissions/ankitsinghh007/level5/answers.md |
Main Level 5 written response with schema, SQL/Cypher examples, bottleneck analysis, hybrid query, and L6 plan. |
submissions/ankitsinghh007/level5/schema.md |
Mermaid schema diagram and node/relationship count summary. |
submissions/ankitsinghh007/level5/HOW_I_DID_IT.md |
Reflection on the approach and lessons learned. |
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| | HAS_CAPACITY | 8 | one per week | | ||
| | TRIGGERED_AT | ~4 | bottleneck nodes | | ||
| | **TOTAL** | **~193** | | | ||
| ``` |
| - w6: deficit **−80 hrs** — capacity 440, planned 520 | ||
| - w7: deficit **−80 hrs** — capacity 520, planned 600 | ||
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| **Projects/stations causing w1 overload (planned hours > 40, actual > planned):** |
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| - Station 016 (Gjutning): P03 w2 +25%, P05 w2 +14.3%, P07 w2 +10%, P08 w3 +13.6% | ||
| - Station 014 (Svets o montage): P03 w1 +14.3%, P04 w1 +12%, P05 w1 +6.9%, P08 w1 +10% | ||
| - Station 021 (SR B/F-hall): P04 w2 +8.3% |
| b.severity = CASE WHEN excess > 20 THEN "HIGH" | ||
| WHEN excess > 10 THEN "MEDIUM" | ||
| ELSE "LOW" END | ||
| MERGE (b)-[:TRIGGERED_AT]->(s) |
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| Filtering by product type (`IQB`, `IQP`, `SB`) finds projects that made the same things — but a hospital project and a parking structure both use IQB beams. The hybrid approach finds projects that were *operationally similar*: same stations (same production flow), similar complexity, AND historically low variance. That last condition is the key — it surfaces past projects whose actual execution matched their plan, which are the only useful analogues for estimating future schedules. A product-type filter has no way to surface execution quality from historical data. | ||
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| **Boardy parallel:** Instead of matching by project scope, Boardy matches people whose embedded `needs` description is close in vector space to another person's `offers` description, AND who are in the same community in the relationship graph (KNOWS, ATTENDS, WORKS_AT edges). The vector surface plausible matches; the graph confirms they're socially reachable. Neither alone would work as well. |
| | `(Week)-[:HAS_CAPACITY]->(Capacity)` | each capacity row | `deficit`, `total_capacity`, `total_planned` | | ||
| | `(Station)-[:HAD_BOTTLENECK]->(Week)` | computed: actual > planned * 1.1 | `severity`, `excess_hours` | | ||
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| ### 4 Streamlit Dashboard Panels |
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The “4 Streamlit Dashboard Panels” heading refers to Section 4 of the document, not the total panel count.
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| MATCH (new_project:Project {id: "P05"}) | ||
| MATCH (new_project)-[:SCHEDULED_AT]->(shared_station:Station) | ||
| MATCH (candidate:Project)-[r:SCHEDULED_AT]->(shared_station) | ||
| WHERE candidate.id IN similar_project_ids | ||
| AND candidate.id <> new_project.id | ||
| WITH candidate, | ||
| count(DISTINCT shared_station) AS shared_stations, | ||
| avg(abs(r.actual_hours - r.planned_hours) / r.planned_hours) AS avg_variance | ||
| WHERE avg_variance < 0.05 | ||
| RETURN | ||
| candidate.name AS similar_project, | ||
| shared_stations, |
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Submission Level
Level: 5
What I Did
Completed the Level 5 Graph Thinking challenge using the provided factory datasets.
Included Work
Focus Areas
Checklist
level-X: Your NameSigned-off-by: Ankit Kumar Singh