| title | Manufacturing Compliance Dashboard |
|---|---|
| emoji | 🏭 |
| colorFrom | green |
| colorTo | yellow |
| sdk | streamlit |
| sdk_version | 1.45.1 |
| app_file | app.py |
| pinned | false |
| license | mit |
| short_description | BRC/HACCP food safety compliance dashboard |
BRC/HACCP food safety compliance — replaces your Excel spreadsheets
Supports BRC compliance for fish production including RSPCA/GG grading, waterfall yield tracking, and batch traceability from catch area to packed product.
- GitHub
- Live Dashboard
- Hugging Face Space
- Profile
- Contributing:
CONTRIBUTING.md·GOVERNANCE.md·CODE_OF_CONDUCT.md·SECURITY.md·NOTICE·CHANGELOG.md
If this dashboard is useful to you, a GitHub star helps discoverability for other food-safety teams with the same BRCGS audit problem.
The FastAPI layer has two layers of test coverage:
- Unit / in-process —
tests/test_api.pyuses FastAPI'sTestClientfor fast in-process tests (no network, runs in the main test job). - Integration / real HTTP —
scanapi/scanapi.yamldrives every endpoint over HTTP via ScanAPI (by Camila Maia and the ScanAPI org, MIT). Catches middleware / CORS / serialisation bugs TestClient skips. Runs locally viamake scanapiand on CI via theScanAPI integration testsworkflow.
Report lands in scanapi-report/ (gitignored). Point at a staging
deploy by overriding the base URL: make scanapi BASE_URL=https://staging.example.com.
Compliance officers in food manufacturing spend hours in Excel tracking batch traceability, temperature logs, allergen records, and weight variance. Auditors need instant answers. This dashboard replaces those spreadsheets with a live, searchable system that traces any product from catch area to packed product in seconds.
- Full batch traceability — trace any product from ocean catch to packed shelf in 2 seconds
- Real-time temperature monitoring — automatic alerts when cold room readings breach limits
- Allergen matrix — covers all 14 EU allergens with changeover sequence tracking
- Weight variance analysis — z-score anomaly detection flags suspicious batches
- One-click audit reports — give auditors what they need without opening Excel
- RSPCA/GG golden rule enforcement — automated compliance checks against grading standards
- Natural language queries — auditors ask questions in plain English, get structured results
- MCP server — expose compliance data as tools for LLM agents (5 tools via FastMCP)
- Declarative validation — temperature, batch, and allergen data validated before import
- SLO monitoring — temperature compliance (95%), traceability completeness (90%)
- Centralized chart themes — consistent light/dark palette across dashboard and PDF reports
Auditor asks for Batch 4821's traceability chain. Someone spends 20 minutes in Excel. This dashboard does it in 2 seconds.
Try the live dashboard → · Hugging Face
| Batch Traceability | Temperature Monitoring | Allergen Matrix |
|---|---|---|
 |
 |
 |
$ make setup && make run
┌─────────────────────────────────────────────────────────────┐
│ Manufacturing Compliance Dashboard │
│ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ COMPLIANCE SCORE │ │
│ │ Temperature Control: 99.1% ██████████████████████░ │ │
│ │ Traceability: 97.2% █████████████████████░░ │ │
│ │ Overall: 98.1% ██████████████████████░ │ │
│ │ Status: ✅ PASS │ │
│ └──────────────────────────────────────────────────────┘ │
│ │
│ Batches tracked: 674 │
│ Temperature readings: 8,640 │
│ Orders monitored: 271 │
│ Products in matrix: 16 │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ TAB 1: BATCH TRACEABILITY │
│ │
│ Search: Batch COD-2024-0847 │
│ │
│ Raw Material Intake │
│ Supplier: FreshCatch Ltd │ Batch: RM-4821 │
│ Date: 2024-03-15 06:12 │ Temp: 1.8°C ✓ │
│ │
│ Production │
│ Line: 2 │ Shift: Morning │ Operator: J. Smith │
│ Input: 120kg │ Output: 114kg │ Waste: 6kg │
│ Yield: 95.0% │
│ │
│ Despatch │
│ Customer: Tesco DC North │ Order: ORD-9182 │
│ Temp at load: 2.1°C ✓ │ FEFO: ✓ │
│ │
│ Chain complete: Raw material → Production → Despatch ✓ │
│ Traceability score: 100% │
├─────────────────────────────────────────────────────────────┤
│ TAB 2: TEMPERATURE MONITORING │
│ │
│ ┌─ Cold Room 1 ──────────────────────────────────────┐ │
│ │ Current: 2.3°C │ Min: -2.0°C │ Max: 5.0°C │ │
│ │ Status: ✅ IN RANGE │ │
│ │ ──────────────────────────────────────── │ │
│ │ 3.1 2.8 2.3 2.5 2.1 2.4 2.3 (30-day trend) │ │
│ └────────────────────────────────────────────────────┘ │
│ │
│ ┌─ Zone D ────────────────────────────────────────┐ │
│ │ Current: -19.4°C │ Min: -25.0°C │ Max: -15.0°C │ │
│ │ Status: ✅ IN RANGE │ │
│ └────────────────────────────────────────────────────┘ │
│ │
│ Anomaly Detection (z-score): │
│ ⚠ Cold Room 2: 5.3°C at 14:22 — exceeds 5.0°C threshold │
│ ⚠ Duration: 47 minutes │ Trend: rising since 13:00 │
│ │
├─────────────────────────────────────────────────────────────┤
│ TAB 3: ALLERGEN MATRIX │
│ │
│ Product │ Fish │ Milk │ Egg │ Soy │ Gluten │ Nuts │
│ ───────────────┼──────┼──────┼─────┼─────┼────────┼──────│
│ Cod Fillets │ ● │ │ │ │ │ │
│ Fish Cakes │ ● │ ● │ ● │ │ ● │ │
│ Prawn Cocktail │ ● │ │ │ ● │ │ │
│ Fish Fingers │ ● │ │ ● │ │ ● │ │
│ │
│ BRC Section 5 compliant │ Export to CSV │ │
│ │
├─────────────────────────────────────────────────────────────┤
│ TAB 4: AUDIT REPORT │
│ │
│ [Generate Report] │
│ → Compliance score: 98.1% PASS │
│ → Temperature excursions: 3 (all <60 min) │
│ → Traceability gaps: 2 batches (97.2% coverage) │
│ → Allergen matrix: 16 products × 6 allergens │
│ → Shelf life concessions: 1 │
│ → Export: [PDF] [JSON] │
└─────────────────────────────────────────────────────────────┘
Temperature monitoring with breach detection linked to shelf life extensions (+2/+3 superchill). Excursions are flagged in real time with duration tracking, and breaches automatically trigger shelf life concession reviews.
Allergen matrix supporting fish production allergens: fish, crustaceans, molluscs, wheat, egg, milk, and mustard. BRC Section 5 compliant with full cross-contact risk assessment and CSV export.
Batch traceability supporting OCM scan-back lineage with parent-child batch mapping. Every batch is traceable from catch area through processing to packed product, with full chain scoring.
Weight variance reconciliation for BRC compliance across customer product lines. Input/output yields are tracked per batch with waterfall reporting to identify loss points.
The dashboard enforces the golden rule: RSPCA material can cascade down to GG products, but GG material can never be upgraded to RSPCA. Tail pieces are never packed into RSPCA products.
ERP / PostgreSQL
|
v
[SQLAlchemy] --- database connection with retry
|
+---> [Batch Traceability] --- catch area to packed product
|
+---> [Temperature Monitor] --- z-score anomaly detection
|
+---> [Allergen Matrix] --- 7 allergen types
|
+---> [Weight Variance] --- BRC compliance
|
v
[Streamlit Dashboard] --- live at streamlit.app
|
v
[Sentry] --- error monitoring (opt-in)
# Step 1: Clone and install
git clone https://github.com/Pawansingh3889/manufacturing-compliance-dashboard.git
cd manufacturing-compliance-dashboard
pip install -r requirements.txt
# Step 2: Seed 60 days of demo data
python data/seed_demo.py
# → Products: 10 | Production: ~500 | Orders: ~270
# → Temperature logs: ~19,000 (5 locations, 8 readings/day)
# → Raw materials: ~200 supplier deliveries
# Step 3: Run
streamlit run app.py
# → Dashboard at http://localhost:8501Or one-liner: make setup && make run
# config.yaml — edit thresholds per location
temperature:
locations:
Cold Room 1:
min: -2.0
max: 5.0
Cold Room 2:
min: -2.0
max: 5.0
Zone D:
min: -25.0
max: -15.0
Chiller (Dispatch):
min: 0.0
max: 5.0
Ambient Store:
min: 10.0
max: 25.0
allergens:
- Fish
- Crustaceans
- Molluscs
- Wheat
- Egg
- Milk
- MustardUpload your own data via the Excel/CSV upload tab — columns are validated automatically.
| Component | Tool |
|---|---|
| Dashboard | Streamlit (4 tabs) |
| Charts | Plotly |
| Database | SQLite (demo) / PostgreSQL (production) |
| Batch Analytics | PySpark, Databricks |
| Anomaly Detection | scipy z-score, excursion duration, trend forecasting |
| Reports | fpdf2 (PDF), JSON |
| Deployment | Docker, Streamlit Cloud |
Pattern borrowed from Frank Rust and Thomas Prexl's "It Works on My Machine" talk (PyCon DE 2026): tell users upfront what the app can't do so they don't form ChatGPT-style expectations. A dashboard that answers some questions perfectly and silently fails on others erodes trust faster than one that refuses out-of-scope questions cleanly.
- Traces any batch code from catch area to packed shelf using the six ERP tables listed in Stack
- Flags temperature excursions, allergen-changeover gaps, weight-variance outliers, and shelf-life risk based on pre-loaded rules
- Exposes the same data over a FastAPI REST layer (
api.py) and an MCP server (mcp_server.py) for LLM agents - Answers a fixed set of natural-language questions via
modules/nl_query.py(regex-based, offline — no LLM call) - Reports Four Golden Signals on
GET /metricsso operators can see when the API itself is struggling
- No free-form SQL. The NL query module only understands the patterns coded into it; questions outside that set return "not supported" rather than guessing.
- No writes to the ERP. Every connection is read-only; the dashboard surfaces data, it does not correct it.
- No live ERP integration in the public demo. The hosted Streamlit / Hugging Face versions run on anonymised synthetic data. Production deployments point at a read-only user on the real ERP.
- No multi-tenant auth. Password via Streamlit secrets, single-tenant. If you need RBAC, put the app behind an identity-aware proxy.
- No PII. The anonymised seed intentionally contains no real employee names, customer identifiers, batch codes, or vessel registration numbers. Production deployments must keep that rule — the app is designed for factory-floor use, not as a customer-data store.
- No unbounded history. Queries that would return more than a few thousand rows are paginated or capped. Bulk export lives on the production-analytics-pipeline side, not here.
Optional error monitoring via Sentry. Set the SENTRY_DSN environment variable to enable. If not set, monitoring is completely disabled (no-op).
export SENTRY_DSN="https://[email protected]/0"
export ENVIRONMENT="production" # optional, defaults to "development"Built by someone who works on the factory floor. 674 batches tracked, 8,640 temperature readings monitored, one-click audit reports.