# reference_handbook

# 🌱 Seed Data Import — Reference Handbook

> This handbook documents all extraction rules, logic, and reference data
for the canteen seed data import pipeline.
> 
> 
> Sections marked **FILL_IN** need your domain expertise.
> Sections marked **✅ READY** I’ve pre-filled from data analysis.
> 

---

## 1. Customer Column → Company/Domain

**Status: ✅ READY**

The `Customer` column in the seed data maps directly to the `company` field in the database.
It’s already clean — just the business name. No parsing needed.

### Disney Customers (prefix `D-`)

Customers starting with `D-` are Disney properties. These map to `disney_park`:

| Customer prefix / pattern | Disney Park | Category |
| --- | --- | --- |
| `D-Magic Kingdom` | `magic-kingdom` | Park |
| `D-Epcot` | `epcot` | Park |
| `D-Hollywood Studios` | `hollywood-studios` | Park |
| `D-Animal Kingdom` | `animal-kingdom` | Park |
| `D-Disney Springs` | `disney-springs` | Park |
| `D-ContemporaryHotel` | `resort` | Resort |
| `D-POLYNESIAN RESORT` | `resort` | Resort |
| `D-Port Orleans` | `resort` | Resort |
| `D-CORONADO SPRINGS` | `resort` | Resort |
| `D-ART OF ANIMATION` | `resort` | Resort |
| `D-POP CENTURY` | `resort` | Resort |
| `D-All Star ...` | `resort` | Resort |
| `D-DISNEY WORLD SS` | `office` | Support services |

**FILL_IN**: Add any Disney customer patterns I missed:

- 
- 

---

## 2. Place Column → Location Fields

**Status: ✅ READY** (patterns identified)

The `Place` column has **two main patterns** — the extraction engine must detect which pattern applies and parse accordingly.

### Pattern A: Simple — ~90% of rows

**Format:** `Venue Name-Venue Detail` (dash-separated, repetitive)

**Examples:**

```
Sygma - Vending-Sygma - Vending Breakroom
Imperial Dade-Imperial Dade- Breakroom
Ancora Apt.-Ancora Apt. Breakroom
Delamarre-Delamarre Breakroom
BMW Service-BMW Breakroom
Kisselback Ford-Kisselback Ford Breakroom
Sam's Club-Sam's Club
BREAK AREA-BREAK AREA
Building 1180-Building 1180
```

**Rule:** Take the last meaningful segment (after the last dash). Strip:
- `Breakroom`, `Br`, `Break Room`, `BREAKROOM`, `BREAK AREA`
- `Vending Area`, `Vending`
- Trailing whitespace and punctuation

### Pattern B: Complex Disney — ~10% of rows

**Format:** `PersonName - Address-G-Zone FLOOR`

**Examples:**

```
Todd J - 901 Tinberline Dr-G-REAR 6TH FL
Carrianne C - 1251 Riversida Dr-G-PO RIVERSIDE 80S
Justin M - 1536  Buena Vista-Team Disney North Win
Penny D - 3520 Ft Wilderness-G-BUS STOP
Sarah W - 4401 Floridian Way-G-BUILDING 9 LOBBY
Brenda G - 2101 Epcot Resorts-Hotel by Room 1290
Jeremy B - 1960 Broadway-2nd FL Brkrm Vacat Club D
```

**Extraction steps:**
1. Strip person name prefix: `<FirstName> <Initial>` or `<FirstName> <LastInit> -`
2. Strip address segment (number + street name)
3. Extract floor info: `NTH FL`, `Nth floor`, `FL N`, `1ST FL`, etc.

1. Extract Building info: `B N`, `Bldg N`, `Mermaid N`, etc 
2. Whatever remains is the clean `place` name

### Edge Cases

```
FL8108-Vending Area                          → place="FL8108", no venue
1st FL Wing-1st Fl Breakroom                 → floor="1st Fl", place="Wing"
Epcot-Imagination                            → place="Imagination"
C-MK EASTGATE SECURITY-Be Our Guest          → place="Be Our Guest"
Southern Tech University:Sun Life-Sun Life   → place="Sun Life" (remove university prefix)
```

### Floor Extraction Patterns

| Pattern | Example | Normalized |
| --- | --- | --- |
| `NTH FL` | `6TH FL` | `6th Floor` |
| `Nth floor` | `7th floor` | `7th Floor` |
| `FL N` | `FL 2` | `Floor 2` |
| `NST FL` | `1ST FL` | `1st Floor` |

**FILL_IN**: Add any special Place patterns or corrections you know about:

- 
- 
- 

---

## 3. GPS Derivation for Multi-Floor Machines

**Status: ⚠️ NEEDS FILL_IN**

### Base GPS Strategy

For machines with no GPS data, we need to derive coordinates:
1. Use existing GPS from other machines at the same address

### Floor Offset Rules

When a base GPS coordinate is found for the building, adjust for floor:

**FILL_IN**: What offset values should we use per floor?

- Does the lat/lng change measurably per floor?  No, Machines are usually in the same area directly above. Usually, not always

### GPS Priority

When multiple machines exist at the same address:

1. Use existing GPS from the DB (field-collected is authoritative)
2. Look for other machines on the same floor, on floors above or below,  and in the same building 
3. Never overwrite existing GPS (preserve policy)

---

## 4. Serial Number Validation & OCR Correction

**Status: ✅ READY** (data analyzed)

### Standard Serial Formats

| Format | Count | Example | Notes |
| --- | --- | --- | --- |
| All digits (10-12 chars) | 1,220 | `168020939`, `471011958` | Crane standard format |
| Starts with `1` (10+ digits) | 175 | `1-16018993`, `112034419` | AMS/Crane prefixed |
| Starts with `222` | various | `222001280013` | Newer Crane format |
| Crane model-prefixed | various | `472-052119`, `449-011276` | Model + serial |
| All digits shorter | various | `124473010109` | USI/Mercato format |

### Likely OCR Errors Found

| Machine ID | Raw Serial | Suspected Correct | Pattern |
| --- | --- | --- | --- |
| 60017 | `15S491811573` | `155491811573` | `S` → `5` |
| 60025 | `15S491811581` | `155491811581` | `S` → `5` |
| 60028 | `I5s331807595` | `155331807595` | `I` → `1`, `s` → `5` |
| 60032 | `I5SS331807593` | `1555331807593` | `I5SS` → `1555` |

### Known Character Confusions

| Mistyped | Should Be | Context |
| --- | --- | --- |
| `S` | `5` | In numeric serials — S looks like 5 in some fonts |
| `s` | `5` | Same — lowercase s scanned as 5 |
| `I` | `1` | Capital I looks like 1 |
| `O` | `0` | Letter O vs zero |
| `B` | `8` | B scanned as 8 (less common) |
| `G` | `6` | G scanned as 6 (less common) |

**FILL_IN**: Add any other character confusions you’ve seen:

- ___________ → ___________
- ___________ → ___________

### Placeholder / Invalid Serials

| Machine ID | Serial | Action |
| --- | --- | --- |
| 60031 | `520` | Flag — likely incomplete or placeholder |
| 68194 | `123` | Flag — clear placeholder |
| 99660 | `00` | Flag — clear placeholder |

**FILL_IN**: How should we handle flagged serials? clear it.

---

### Unknown Machine Identification by Serial

**FILL_IN**: How can we identify an unknown machine from its serial number?
- What do different serial prefixes mean? We can look at known good serials and compare with makes/models to determine structure
- Can we cross-reference with Make/Model/Type? We look at known good machines, and look at their serials to figure out structure
- Any known serial → machine type mappings?

---

---

## 5. Type/Class/Make/Model Consolidation

**Status: ✅ READY** (data analyzed)

### The Priority Rule

The `Type` column is the **best source** for Class/Make/Model. Parse it first,
then fill gaps from the standalone columns.

### Type Column Format

> `CLASS (MAKE MODEL)` — e.g., `Snack (AMS Sensit 3)`
> 

**Extraction:**

```
"Snack (AMS Sensit 3)"
    → Class = "Snack"
    → Make  = "AMS"
    → Model = "Sensit 3"

"GF Food (Crane 472)"
    → Class = "Food"
    → Make  = "Crane"
    → Model = "472"

"Bev (Royal GIII)"
    → Class = "Bev"
    → Make  = "Royal"
    → Model = "GIII"

"Unknown"
    → All three = "Unknown" (leave as-is)
```

Make sure that if the Class, make, model, etc has a GF prefix then strip it.

### Type Patterns Found

| Type Pattern | Count | Class | Make | Model |
| --- | --- | --- | --- | --- |
| `Bev (Royal GIII)` | 299 | Bev | Royal | GIII |
| `Snack (Crane Merchant Media)` | 287 | Snack | Crane | Merchant Media |
| `Bev (Vendo 621/721/821)` | 235 | Bev | Vendo | 621/721/821 |
| `GF Food (Crane 472)` | 151 | Food | Crane | 472 |
| `Snack (Crane 15x/16x)` | 76 | Snack | Crane | 15x/16x |
| `Unknown` | 67 | Unknown | Unknown | Unknown |
| `GF Bev (DN 200E)` | 49 | Bev | DN | 200E |
| `GF Bev (DN 5800)` | 35 | GF Bev | DN | 5800 |
| `Snack (Crane 186)` | 34 | Snack | Crane | 186 |
| `GF Bev (DN BevMax 4)` | 34 | GF Bev | DN | BevMax 4 |
| `Bev (DN 501E/600E/276E)` | 34 | Bev | DN | 501E/600E/276E |
| `Snack (Crane 472)` | 26 | Snack | Crane | 472 |
| `Bev (DN 276E)` | 25 | Bev | DN | 276E |
| `GF Bev (DN 3800 BevMax 4)` | 20 | GF Bev | DN | 3800 BevMax 4 |
| `GF Bev (DN Baby BevMax)` | 19 | GF Bev | DN | Baby BevMax |
| `Bev (DN 501E)` | 18 | Bev | DN | 501E |
| `GF Bev (DN BevMax)` | 18 | GF Bev | DN | BevMax |
| `Snack/Bev (Crane 472)` | 16 | Snack/Bev | Crane | 472 |
| `Snack (Crane 187)` | 16 | Snack | Crane | 187 |
| `NATIONAL - 168 SERIES` | 16 | Snack | Crane | 168 |
| `VENDO - 721` | 14 | Bev | Vendo | 721 |
| `Snack (AMS 3561)` | 12 | Snack | AMS | 3561 |
| `Snack (USI Mercato)` | 10 | Snack | USI | Mercato |
| `Snack (VE)` | 10 | Snack | VE | Unknown |
| `NATIONAL - 186` | 10 | Snack | Crane | 186 |
| `NATIONAL - 187` | 10 | Snack | Crane | 187 |

### Special Parse Cases

| Raw Type | Rule |
| --- | --- |
| `NATIONAL - 168 SERIES` | Class=Snack, Make=Crane (National=Crane brand), Model=168 |
| `VENDO - 721` | Class=Bev (if Type doesn’t have Class), Make=Vendo, Model=721 |
| `Snack` (no parens) | Class=Snack, check Make/Model columns for fill |
| `Bev` (no parens) | Class=Bev, check Make/Model columns |
| `Unknown` | Leave all Unknown |

### Filling Unknowns

After parsing Type, check the standalone `Make` and `Model` columns to fill gaps.
If Type-based Make is empty but standalone Make has a value, use it.
If both are empty/Unknown, flag for review.

**FILL_IN**: Are there any Type→Make/Model mappings I’ve missed?

- 
- 

### Make Normalization

**FILL_IN**: Consolidate these make variants:

| Raw Value | Normalized To |
| --- | --- |
| `Crane`, `Crane Co`, `Crane Nat`, `NATIONAL` | `Crane` |
| `DN`, `Dixie Narco` | `DN` |
| `Royal`, `Royal Vendors` | `Royal` |
| `Vendo`, `Vendo Co` | `Vendo` |
| `USI`, `U Select It` | `USI` |
| `AMS`, `Automatic Merchandising` | `AMS` |
| `VE`, `Vending Equipment` | ___________ |
| `AP`, `Automatic Products` | ___________ |
| `Faz`, `Fas` | ___________ |

### Class Normalization

| Raw Value | Normalized To |
| --- | --- |
| `Snack`, `Snack/Food` | `Snack` |
| `Bev` | `Bev` |
| `GF Food` | `GF Food` |
| `GF Bev` | `GF Bev` |
| `Snack/Bev` | `Snack/Bev` |
| `Food` | `GF Food` — verify this |

**FILL_IN**: Any other class consolidations?

- 

---

## 6. Remote Pricing Status — Meanings

**Status: ✅ READY** (data analyzed)

### Distribution

| Status | Count | Meaning |
| --- | --- | --- |
| **On** | 614 | Remote pricing is active and working normally |
| **Action Required** | 718 | Remote pricing needs attention — may have failed, needs configuration, or other issue |
| **Incompatible** | 516 | Machine does not support remote pricing (older model, EEEPROM Upgrade Required) |

**FILL_IN**: Clarify what each status actually means in practice:

**On** — Working

**Action Required** — No Dex Passcode / Bad Coil Count

**Incompatible** — achine does not support remote pricing (older model, EEEPROM Upgrade Required

Are there any other status values not present in this data?

---

---

## 7. Telemetry ID → Card Reader Decoder

**Status: ✅ READY** (data analyzed)

### Telemetry Provider Patterns

| Telemetry ID Pattern | Provider | Card Reader Type |
| --- | --- | --- |
| `VJ... (ePort)` | ePort (USI/Crane) | ePort telemetry module |
| `K3CT... (ePort)` | ePort (Crane) | ePort telemetry module |
| `VK... (ePort)` | ePort | ePort telemetry module |
| `... (NAYAX)` | Nayax | Nayax card reader |
| `... (CMS)` | Crane Merchandising Systems | CMS telemetry/reader |
| All-numeric (no suffix) | Unknown | Unknown/other |

### Distribution

| Provider | Count |
| --- | --- |
| ePort | 924 |
| CMS | 628 |
| Nayax | 275 |
| Other/unknown | 21 |

**FILL_IN**: More detail on what each provider means for card readers:

**ePort** —Looknuo cantaloupe devices_______________________________________________________________

**NAYAX** — look up nayax devices_______________________________________________________________

**CMS** — Crane integrated and external solutions_________________________________________________________________

**FILL_IN**: Do these map to specific credit card reader models?

| Provider | Reader Brand | Reader Model |
| --- | --- | --- |
| ePort | Cantaloupe___________ | ___________ |
| NAYAX | Nayax___________ | ___________ |
| CMS | Crane___________ | ___________ |

---

## 8. Alert Meanings

**Status: ✅ READY** (data analyzed)

### Alert Types Found

| Alert Text | Count | Severity | Meaning |
| --- | --- | --- | --- |
| `(empty)` | majority | none | No alerts |
| `This machine has been scheduled for service on Monday, May 25, 2026` | many | ⚠️ info | Scheduled maintenance |
| `This machine is scheduled for service today` | several | ⚠️ info | Same-day service scheduled |
| `Out of touch for 19 hours - contact Crane Merchandising Systems for assistance` | several | 🔴 critical | Telemetry offline > 19 hours |
| `Out of touch for 19 hours...` (combined with service notice) | few | 🔴 critical | Multiple issues |

**FILL_IN**: Categorize these alerts properly. What severity should each get?

| Alert | Severity (Info/Warning/Critical) | Display in app? |
| --- | --- | --- |
| Scheduled service in future | ___________ | ___________ |
| Scheduled service today | ___________ | ___________ |
| Out of touch (telemetry lost) | ___________ | ___________ |
| Out of touch + service needed | ___________ | ___________ |

**FILL_IN**: Are there other alert patterns that could appear?

---

---

## 9. Sales Priority Scoring

**Status: ⚠️ NEEDS INPUT**

The current `import_seed.csv` has **no sales data columns** — they’re marked as “Deleted” in the header map.

### Available Alternative Signals

Without sales data, we can score priority using:

| Signal | What it tells us | Priority hint |
| --- | --- | --- |
| `Class = "GF Food"` | Perishable food — needs frequent service | Higher priority |
| `Class = "GF Bev"` | Perishable drinks — needs frequent service | Higher priority |
| `Remote Pricing = "Action Required"` | Needs technical attention | Higher priority |
| `Alerts = "Out of touch"` | Telemetry down | Higher priority |
| `Customer = Disney` | High-traffic, high-visibility | Higher priority |
| `Type = "Unknown"` | Unknown machine — needs identification | Lower priority |

**FILL_IN**: How do you want to score priority?

| Criteria | Priority (Low / Mid / High) |
| --- | --- |
| GF Food or GF Bev class | ___________ |
| Action Required pricing | ___________ |
| Critical alert (out of touch) | ___________ |
| Disney property | ___________ |
| Active pricing (On) + Snack class | ___________ |
| Unknown Type/Make/Model | ___________ |
| Incompatible pricing | ___________ |

**FILL_IN**: Do you have sales data from a separate export? Where does it come from?

---

**FILL_IN**: If sales data is available, what are the priority thresholds?

| Annual Sales Range | Priority |
| --- | --- |
| $_______ - $_______ | Low |
| $_______ - $_______ | Mid |
| $_______+ | High |

---

## 10. Asset List & Detail Screen Spec

**Status: ⚠️ NEEDS FILL_IN**

### Asset List Card

Each asset card in the main app list should show:

**FILL_IN**: What fields belong on the asset card?

- [ ]  Machine ID
- [ ]  Name
- [ ]  Make / Model
- [ ]  Location (place, building, floor)
- [ ]  Priority badge (low/mid/high)
- [ ]  Status badge (active/maintenance/retired)
- [ ]  Remote Pricing status
- [ ]  Disney park badge
- [ ]  Alert indicator
- [ ]  GPS coordinates badge (has/don’t have)
- [ ]  Last contact / last dex time
- [ ]  Card reader type
- [ ]  Other: _______________

**FILL_IN**: Priority badge colors?

| Priority | Color |
| --- | --- |
| Low | ___________ |
| Mid | ___________ |
| High | ___________ |

### Asset Detail Screen

Full detail view should show:

**FILL_IN**: Section layout for the detail screen (order and contents):

1. 
2. 
3. 
4. 
5. 
6. 

**FILL_IN**: Which sections should be editable?

- 
- 

**FILL_IN**: Should the approval workflow fields be editable here too?

---

---

## 11. Update Policy — Per-Field

**Status: ⚠️ NEEDS FILL_IN**

For each field, specify how updates from new seed data should be handled:

| DB Column | Source CSV Column | Update Policy | Notes |
| --- | --- | --- | --- |
| `machine_id` | Machine ID | preserve | Primary key, never changes |
| `serial_number` | Serial Number | ___________ |  |
| `name` | — (generated) | ___________ |  |
| `make` | Type→Make (parsed) | ___________ |  |
| `model` | Type→Model (parsed) | ___________ |  |
| `class`*) | Type→Class (parsed) | ___________ |  |
| `company` | Customer | ___________ |  |
| `place` | Place (parsed) | ___________ |  |
| `building_name` | Place (parsed) | ___________ |  |
| `building_number` | Place (parsed) | ___________ |  |
| `floor` | Place (parsed) | ___________ |  |
| `room` | Place (parsed) | ___________ |  |
| `trailer_number` | Place (parsed) | ___________ |  |
| `address` | Address | ___________ |  |
| `location_area` | City | ___________ |  |
| `latitude` | — (derived) | preserve | Field-collected is authoritative |
| `longitude` | — (derived) | preserve | Field-collected is authoritative |
| `photo_path` | — | preserve | Never overwrite |
| `dex_report_date` | Last Dex Report Time | ___________ |  |
| `install_date` | Added Date | ___________ |  |
| `deployed` | — | ___________ |  |
| `pulled_date` | — | ___________ |  |
| `disney_park` | Customer (parsed) | ___________ |  |
| `remote_pricing_status` | Remote Pricing | ___________ |  |
| `alerts` | Alerts | ___________ |  |
| `telemetry_provider` | Telemetry ID (parsed) | ___________ |  |
| `card_reader_brand` | Telemetry ID (parsed) | ___________ |  |
| `card_reader_model` | Telemetry ID (parsed) | ___________ |  |
| `priority` | — (scored) | ___________ |  |
| `prepick_group` | Prepick Group | ___________ |  |
- `class` is not a current DB column — needs to be added via migration.

**Policy values:** `auto` (always overwrite), `approval` (queue for review), `preserve` (never touch)

**FILL_IN**: Add or change any policies above.

---

## Appendix: Column Mapping

### Raw Excel → CSV → DB Mapping

| Xlsx Column | CSV Column | DB Column | Notes |
| --- | --- | --- | --- |
| Device | Telemetry ID | — | Parsed to provider/brand |
| Location | Location | — | Redundant (Customer + Address) |
| Asset ID | Machine ID | `machine_id` | Primary key |
| Place | Place | `place` + derived fields | Main extraction target |
| Type | Type | — | Parsed to Class/Make/Model |
| City | City | `location_area` | Direct map |
| Address | Address | `address` | Direct map |
| Last Contact Time | Last Contact Time | — | Freshness indicator |
| Last Dex Report Time | Last Dex Report Time | `dex_report_date` |  |
| Prepick Group | Prepick Group | — | Routing info |
| Customer | Customer | `company` | Direct map |
| Route | Route | — | Routing info |
| State | State | — |  |
| Postal Code | Postal Code | — |  |
| Serial Number | Serial Number | `serial_number` | Validate + correct |
| Class | Class | — | Source for `class` |
| Make | Make | `make` (fallback) | Type column takes priority |
| Model | Model | `model` (fallback) | Type column takes priority |
| Added Date | Added Date | `install_date` |  |
| Status | Remote Pricing | — | Direct to app |
| Alerts | Alerts | — | Parsed to structured alerts |

**FILL_IN**: Any missing columns or corrections?

---