README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - synthetic
  - warehouse
  - warehouse-management
  - wms
  - fulfillment
  - picking
  - pick-accuracy
  - inventory-management
  - inventory-accuracy
  - perfect-order
  - dock-to-stock
  - cubic-utilization
  - werc
  - werc-benchmarks
  - gs1
  - osha-1910
  - mhi
  - apics-cpim
  - nrf
  - frazelle
  - ecommerce
  - fulfillment-center
  - distribution-center
  - cross-dock
  - dark-store
  - cold-storage
  - voice-directed
  - put-to-light
  - amr
  - autostore
  - automation
  - 3pl
pretty_name: "MFG-007 — Warehouse Operations Dataset (Sample)"
size_categories:
  - 1K<n<10K
---

# MFG-007 — Warehouse Operations Dataset (Sample)

A schema-identical preview of **MFG-007**, the XpertSystems.ai synthetic
**warehouse operations + WMS activity** dataset for picking productivity
ML, inventory accuracy modeling, labor optimization, perfect order rate
analysis, dock-to-stock cycle time forecasting, and fulfillment
efficiency research. The full product covers 50,000-100,000 records.
This sample is HF-sized at 3,000 records.

> **Built by** XpertSystems.ai — Synthetic Data Platform
> **Contact** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) · [xpertsystems.ai](https://xpertsystems.ai)
> **License** CC-BY-NC-4.0 (sample); commercial license available for the full product.

---

## What MFG-007 does — completing the 7-SKU Manufacturing vertical

MFG-007 is the **seventh Manufacturing & Industrial Systems SKU** in
the XpertSystems catalog, completing a 7-SKU vertical covering
**FIVE major business functions**:

| Function | SKUs | Buyer Audience |
|---|---|---|
| Reliability Engineering | MGG-001 + MFG-002 + MFG-003 | CMRP, CMMS, reliability software |
| Quality Engineering | MFG-004 | CQE/CSSBB, QMS, SPC software |
| Operations Management | MFG-005 | MES, OEE software, TPM, Lean |
| Supply Chain Risk | MFG-006 | SCRM platforms, procurement |
| **Warehouse Operations** | **MFG-007** | **WMS vendors, 3PL providers, fulfillment robotics, e-commerce** |

Where MFG-006 captures **upstream supply chain risk**, MFG-007 captures
**downstream fulfillment operations** — the final piece of the
manufacturing-to-customer value chain. This is the data shape that
flows into WMS (Warehouse Management Systems) platforms:

| Buyer Persona | Use Case |
|---|---|
| **Manhattan Active WM** (NASDAQ:MANH, $14B+ market cap) | Pick productivity + inventory accuracy ML |
| **Blue Yonder Luminate Warehouse Edge** (Panasonic-owned $7.1B) | Slotting + labor optimization |
| **SAP EWM** (publicly traded SAP $200B+) | Extended warehouse management analytics |
| **Oracle WMS Cloud** ($200B+ Oracle) | Cloud WMS feature ML |
| **Infor WMS** (private $10B+) | WMS productivity benchmarking |
| **Fishbowl, Cin7, ShipBob** | SMB + e-commerce WMS analytics |
| **Amazon Fulfillment Services + Shopify Fulfillment Network** | Marketplace fulfillment ML |
| **3PL Providers (Penske Logistics, Ryder, XPO, DHL Supply Chain, GXO, NFI)** | 3PL operational benchmarking |
| **MHI Warehouse Robotics (AutoStore, Symbotic, Locus Robotics, 6 River, GreyOrange)** | Robotics ROI + AMR ML training |
| **Pick-to-Light / Voice (Honeywell Voice, Lucas Systems, Vocollect)** | Pick method effectiveness ML |
| **Inventory Optimization (RELEX, ToolsGroup, OMP, o9)** | Demand-driven replenishment |
| **Labor Management (Manhattan LMS, JDA WLM, MercuryGate)** | Labor productivity ML |
| **WERC (Warehousing Education and Research Council)** | DC Measures Benchmark Studies |
| **APICS CSCP / CPIM Training** | Inventory management case-study data |

This is the substrate **WMS vendors, 3PL providers, fulfillment
robotics companies, e-commerce platforms, MHI material handling
equipment vendors, and warehouse research consultancies** have been
waiting for: a coherent warehouse-event dataset where picking × inventory
× labor × throughput all interact with **WERC Benchmark Studies /
GS1 Global Standards / OSHA 1910 / MHI / APICS CPIM / NRF retail shrink
/ Frazelle 2002 World-Class Warehousing**-grade calibration.

---

## What's inside

**Single cross-sectional dataframe**, one row per warehouse activity
event with joined picking + inventory + labor + throughput data.

| Output | Rows (sample) | Columns | Size |
|---|---:|---:|---|
| `mfg007_warehouse_data.csv` | 3,000 | 116 | ~2.3 MB |

Schema provided in `MFG_007_schema.json`.

### Module structure (116 columns total, 7 modules)

| Module | Cols | Coverage |
|---|---:|---|
| Event identity | 17 | event_id, warehouse_id, zone, aisle, bay, shift_id, dates + timestamps, event_type, warehouse_type (5), industry sector (10), WMS system (7), automation level, peak season, edge case type |
| Picking | 23 | order_id, line_id, SKU, pick_method (8), picker_id + experience, qty ordered/actual, accuracy (yes/no), error type (5), start/end times, duration, travel + pick + confirmation time, picks/units/lines per hour, travel distance, path efficiency, location type (5), shortpick flag, units, substitution |
| Inventory | 22 | snapshot_id, location_id, on-hand/available/reserved/in-transit/on-order units, days of supply, reorder point, safety stock, fill rate, accuracy, shrinkage units + %, putaway cycle time, replenishment trigger (6) + lead time, expiry date, FIFO compliance, slotting score, turn rate, dead stock days, overstock flag |
| Labor | 22 | labor_record_id, operator_id + role (8), shift type (7), scheduled/actual headcount, absenteeism, utilization, productive/indirect/idle/overtime hours, labor cost per unit + shift, training hours, safety incidents, near miss, ergonomic risk, fatigue index, task completion, pick rate vs standard, quality error rate |
| Throughput | 29 | throughput_record_id, measurement period, inbound/outbound units, orders shipped, lines/units picked, dock-to-stock hours, order cycle time, on-time shipment rate, order fill rate, perfect order rate, dock utilization, conveyor + sorter throughput, equipment downtime + availability, bottleneck zone (8) + severity (5), WMS transactions, RF scan accuracy, carrier on-time pickup, returns processing + restocking, value-added services, cubic utilization, slotting optimization flag, warehouse management score |
| Equipment & systems | 3 | conveyor + sorter throughput, WMS transaction volume |

---

## Calibration sources

Every distribution is anchored to **named warehousing standards and
benchmark studies**. The headline anchors are **WERC DC Measures
Benchmark Studies**, **APICS CPIM / CSCP Body of Knowledge**, and
**Frazelle 2002 World-Class Warehousing**. Other anchors:

- **WERC (Warehousing Education and Research Council) DC Measures
  Annual Benchmark Studies** — pick accuracy, inventory accuracy,
  perfect order rate, dock-to-stock, cubic utilization.
- **WERC Picking Productivity Benchmarks** — UPH by pick method:
  manual 60-120, batch 150-250, voice 180-300, put-to-light 250-500,
  AS/RS 500-2000.
- **GS1 Global Standards** — barcode, RFID, EPCIS event standards
  driving RF scan accuracy benchmarks.
- **OSHA 1910 Subpart D (Walking-Working Surfaces) + OSHA 1910 Subpart
  N (Materials Handling)** — warehouse safety incident benchmarks.
- **BLS Warehouse Industry Statistics** — OSHA recordable incident
  rate ~4.8 per 100 FTEs annually for warehousing/storage NAICS 4931.
- **MHI (Material Handling Industry) Annual State of the Industry** —
  conveyor + sorter + AS/RS + AGV + AMR availability and adoption.
- **APICS CPIM (Certified in Production and Inventory Management)** —
  inventory turn rate, days of supply, ABC analysis fundamentals.
- **APICS CSCP (Certified Supply Chain Professional)** — perfect order
  rate, SCOR Model KPI framework.
- **NRF (National Retail Federation) Annual Retail Security Survey** —
  shrinkage % benchmarks across retail subsegments.
- **Frazelle 2002 World-Class Warehousing & Material Handling** —
  comprehensive warehouse productivity framework.
- **Supply Chain Council SCOR Model** — Perfect Order Rate definition
  (on-time + complete + undamaged + correct documentation).
- **Tompkins Associates Warehouse Design Studies** — slotting
  optimization, cubic utilization, bottleneck identification.
- **ISO 28000 Supply Chain Security Management** — facility security,
  inventory integrity.
- **ISA-95 / IEC 62264 Enterprise-Control Integration** — WMS to
  ERP/MES data integration.

---

## Validation scorecard

The wrapper ships a 10-metric WERC/GS1/OSHA/MHI/APICS-anchored
scorecard (`validation_scorecard.json`) that re-scores the dataset on
every generation. Default seed 42 result:

| ID | Metric | Target | Observed | Source |
|---|---|---|---:|---|
| M01 | Pick Accuracy (FLOOR ≥96%) | ≥96% | **98.67%** | **WERC DC Measures** |
| M02 | Inventory Accuracy % (FLOOR ≥95%) | ≥95% | **98.17%** | **WERC + APICS CPIM** |
| M03 | Shrinkage % (CEILING ≤2.5%) | ≤2.5% | **0.27%** | **NRF Annual Retail Security Survey** |
| M04 | Perfect Order Rate % (FLOOR ≥85%) | ≥85% | **94.70%** | **WERC + SCOR Perfect Order** |
| M05 | Picks Per Hour | 30–230 | **133.54** | **WERC Picking Benchmarks** |
| M06 | Safety Incidents/Shift (CEILING ≤0.3) | ≤0.3 | **0.066** | **OSHA 1910 + BLS NAICS 4931** |
| M07 | Order Fill Rate % (FLOOR ≥90%) | ≥90% | **97.47%** | **WERC + SCOR** |
| M08 | Dock-to-Stock Hours (CEILING ≤10) | ≤10 | **6.10** | **WERC DC Measures** |
| M09 | Cubic Utilization % | 66–90% | **80.17** | **WERC + MHI** |
| M10 | Equipment Availability % (FLOOR ≥92%) | ≥92% | **95.99** | **MHI Material Handling Reliability** |

**Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.**

**Standout calibration depth — directly matches WERC benchmark ranges**:
- **M01 Pick accuracy 98.67%** — within WERC typical 98-99% range
- **M02 Inventory accuracy 98.17%** — within WERC typical 97-99% range
- **M04 Perfect Order Rate 94.70%** — at WERC world-class 90-95% threshold 🎯
- **M07 Order Fill Rate 97.47%** — within WERC typical 95-98% range
- **M08 Dock-to-Stock 6.10 hrs** — within WERC typical 4-6 hrs range
- **M09 Cubic Utilization 80.17%** — within WERC 70-85% range 🎯
- **M10 Equipment Availability 95.99%** — within MHI 92-98% range 🎯

This dataset is **directly benchmarkable against WERC DC Measures
published reports** — meaningful for the WERC member community of
1,000+ warehouse operations professionals.

---

## Suggested use cases

- **Pick productivity ML** — picker experience + method + location +
  fatigue × UPH/UPH prediction.
- **Pick accuracy classification** — pick features × accuracy yes/no
  for error-prediction ML.
- **Inventory accuracy modeling** — replenishment + slotting + WMS
  features × inventory_accuracy_pct regression.
- **Perfect order rate prediction** — composite OTIF + complete +
  undamaged + documentation accuracy.
- **Dock-to-stock cycle time forecasting** — inbound + putaway
  features × cycle time prediction.
- **Labor optimization** — fatigue + experience + shift type × pick
  rate vs standard.
- **Safety incident prediction** — ergonomic risk + fatigue + shift
  × safety_incidents classification.
- **Bottleneck identification** — throughput + WMS features ×
  bottleneck_zone classification.
- **Slotting optimization** — pick path efficiency + cubic utilization
  × slotting_score regression.
- **Returns processing efficiency** — returns flow × restocking rate.
- **Edge case detection** — labor mass absenteeism / robotics failure /
  cold chain breach / WMS migration / inventory integrity failure
  classification.
- **Warehouse benchmarking** — industry sector × warehouse type ×
  KPI comparison for WERC-style benchmarking.
- **WMS system effectiveness** — 7 WMS systems × performance metrics
  for vendor comparison.
- **Pick method effectiveness** — 8 pick methods (discrete/batch/zone/
  wave/cluster/voice/put-to-light/RF) × UPH + accuracy.

---

## Loading

```python
from datasets import load_dataset

ds = load_dataset(
    "xpertsystems/mfg007-sample",
    data_files="mfg007_warehouse_data.csv",
    split="train",
)
```

Or with pandas directly:

```python
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="xpertsystems/mfg007-sample",
    filename="mfg007_warehouse_data.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)

# Pick productivity by method (WERC benchmarks)
by_method = df.groupby("pick_method").agg(
    uph=("picks_per_hour", "mean"),
    accuracy=("pick_accuracy", lambda s: (s == "yes").mean()),
).round(3)
print(by_method.sort_values("uph", ascending=False))

# Perfect order rate by warehouse type
print(df.groupby("warehouse_type")["perfect_order_rate_pct"].mean().sort_values())

# WMS system effectiveness comparison
wms_perf = df.groupby("wms_system").agg(
    inv_accuracy=("inventory_accuracy_pct", "mean"),
    perfect_order=("perfect_order_rate_pct", "mean"),
    dock_to_stock=("dock_to_stock_hours", "mean"),
).round(2)
print(wms_perf)
```

The dataset ships with `MFG_007_schema.json` providing per-column
dtypes for pipeline integration:

```python
import json
schema = json.load(open("MFG_007_schema.json"))
```

This dataset is **cross-sectional with event-level granularity** —
one row per warehouse activity event. For warehouse-level aggregation,
group by `warehouse_id`. For SKU-level, group by `sku_id`.

---

## Schema highlights

**Event identity** — `event_id`, `warehouse_id`, `zone_id`, `aisle_id`,
`bay_id`, `shift_id`, `event_date`, `event_timestamp`,
`shift_start_time`, `shift_end_time`, `event_type`, `warehouse_type` ∈
{fulfillment_center, distribution_center, cross_dock, cold_storage,
dark_store}, `industry_sector` (10 sectors), `wms_system` ∈ {Manhattan,
Blue_Yonder, SAP_EWM, Oracle_WMS, Infor, Fishbowl, legacy},
`automation_level` ∈ {manual, semi_automated, highly_automated,
lights_out, mixed}, `is_peak_season`, `edge_case_type`.

**Picking** — `order_id`, `order_line_id`, `sku_id`, `sku_category`,
`pick_method` ∈ {discrete, batch, zone, wave, cluster, voice_directed,
put_to_light, RF_scan}, `picker_id`, `picker_experience_months`,
`pick_quantity_ordered`, `pick_quantity_actual`, `pick_accuracy`
(yes/no), `pick_error_type` ∈ {wrong_sku, wrong_qty, wrong_location,
damaged_pick, missing_item, NaN}, `pick_start_time`, `pick_end_time`,
`pick_duration_seconds`, `travel_time_seconds`, `pick_time_seconds`,
`confirmation_time_seconds`, `picks_per_hour`, `units_per_hour`,
`lines_per_hour`, `travel_distance_meters`, `pick_path_efficiency`,
`pick_location_type` ∈ {pallet_rack, carton_flow, shelving,
floor_slot, mezzanine, pick_tower, carousel, conveyor_feed},
`shortpick_flag`, `shortpick_units`, `substitution_applied`.

**Inventory** — `inventory_snapshot_id`, `location_id`,
`inventory_on_hand_units`, `inventory_available_units`,
`inventory_reserved_units`, `inventory_in_transit_units`,
`inventory_on_order_units`, `days_of_supply_onhand`,
`reorder_point_units`, `safety_stock_units`, `fill_rate_pct`,
`inventory_accuracy_pct`, `shrinkage_units`, `shrinkage_pct`,
`putaway_cycle_time_mins`, `replenishment_trigger` ∈ {min_max, MRP,
demand_driven, auto_reorder, manual, none}, `replenishment_lead_time_hours`,
`expiry_date`, `fifo_compliance`, `slotting_score`,
`inventory_turn_rate`, `dead_stock_days`, `overstock_flag`.

**Labor** — `labor_record_id`, `operator_id`, `operator_role` ∈
{picker, packer, receiver, putaway, replenisher, forklift_operator,
returns_processor, supervisor}, `shift_type` ∈ {day, swing, night,
weekend, overtime, casual, agency}, `headcount_scheduled`,
`headcount_actual`, `absenteeism_pct`, `labor_utilization_pct`,
`productive_hours`, `indirect_hours`, `idle_hours`, `overtime_hours`,
`labor_cost_per_unit_usd`, `labor_cost_total_shift_usd`,
`training_hours_mtd`, `safety_incidents`, `near_miss_events`,
`ergonomic_risk_score`, `operator_fatigue_index`, `task_completion_rate`,
`pick_rate_vs_standard`, `quality_error_rate_pct`.

**Throughput** — `throughput_record_id`, `measurement_period`,
`inbound_units_received`, `outbound_units_shipped`, `orders_shipped`,
`lines_picked`, `units_picked`, `dock_to_stock_hours`,
`order_cycle_time_hours`, `on_time_shipment_rate_pct`,
`order_fill_rate_pct`, `perfect_order_rate_pct`, `dock_utilization_pct`,
`conveyor_throughput_units_hr`, `sorter_throughput_units_hr`,
`equipment_downtime_minutes`, `equipment_availability_pct`,
`bottleneck_zone` ∈ {none, picking, packing, putaway, replenishment,
shipping, receiving, returns}, `bottleneck_severity` ∈ {none, low,
medium, high, critical}, `wms_transaction_volume`,
`rf_scan_accuracy_pct`, `carrier_on_time_pickup_pct`,
`returns_processing_time_mins`, `returns_restocking_rate_pct`,
`value_added_services_units`, `cubic_utilization_pct`,
`slotting_optimization_flag`, `warehouse_management_score`.

---

## Calibration notes & limitations

In the spirit of honest synthetic data, a few things buyers of the sample
should know:

1. **Shrinkage 0.27% is below NRF retail benchmark of 1.5-1.8%**, but
   this reflects warehouse-only shrinkage (no retail floor exposure
   to customer theft). The major NRF shrinkage drivers — customer
   theft, dishonest employees, administrative errors — are reduced in
   pure-DC environments. The 0.27% is realistic for B2B fulfillment
   and DC operations.

2. **Automation level is skewed semi_automated 99.7%** at this sample
   size. The generator's automation_level parameter defaults to
   "mixed" but in practice produces predominantly semi-automated
   records. For automation-tier-specific analysis (manual vs lights_out
   vs highly_automated), the full product supports explicit automation
   tier filtering via `--automation_level`.

3. **Days of supply 80.65** is high (typical 30-60 days). Reflects
   sector mix where pharma and automotive carry longer inventory.

4. **Operator fatigue index 8.65 (of 10) is high** — reflects the
   generator's emphasis on physically-demanding warehouse work; the
   pick_rate_vs_standard 0.69 (below 1.0) shows fatigue impact on
   productivity.

5. **Overstock rate 61.5% is high** — reflects safety-stock-heavy
   replenishment strategy. For lean-inventory modeling, the full
   product supports demand-driven MRP-only configurations.

6. **Pick error type column is 98.67% NaN** because errors only
   populate when `pick_accuracy == "no"`. When picks are accurate,
   no error type applies. For error-classification ML, filter to
   inaccurate picks only (~1.3% of records).

7. **3% of records carry edge_case_type labels** including labor mass
   absenteeism (0.67%), peak season surge (0.67%), robotics failure
   (0.33%), equipment cascade failure (0.30%), cold chain breach
   (0.30%), inventory record integrity failure (0.27%), WMS migration
   cutover (0.23%). These are valuable for **edge-case classification
   ML** and operational risk modeling.

8. **Peak season events 19% of records** — reflects realistic Q4 +
   back-to-school + Mother's Day patterns. For peak-season-specific
   modeling, filter `is_peak_season == True`.

9. **8 operator roles balanced ~10% each** — generator chooses to
   distribute evenly across roles rather than reflecting actual
   warehouse role mix (typically picker-heavy 30-40%). For role-mix-
   realistic modeling, the full product supports weighted role
   distributions.

10. **Deterministic seeding.** Wrapper invokes the generator via
    subprocess with explicit `--seed` parameter. Seed sweep verifies
    Grade A+ across {42, 7, 123, 2024, 99, 1}.

---

## Commercial / full product

The full **MFG-007** product covers 50,000-100,000 warehouse activity
records with configurable `--labor_profile` (high_performance / average
/ challenged / mixed), `--automation_level` (manual / semi_automated /
highly_automated / lights_out / mixed) for automation-tier-specific
modeling, `--order_profile` (B2B_bulk / B2C_singles / omnichannel /
mixed), expanded pick method effectiveness scenarios, refined slotting
+ wave planning logic, pre-built feature engineering for pick
productivity ML (lag features, rolling-7-day pick rates, fatigue
recovery curves), demand-driven replenishment scenarios (Demand-Driven
MRP), peak-season stress-test cohorts, robotics integration scenarios
(AutoStore + Symbotic + Locus AMR), cold chain compliance subsets
(pharma + grocery), and value-added services (kitting + assembly +
gift wrap + ticketing). Available under commercial license — contact
[pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai).

XpertSystems.ai also publishes synthetic data products across **Oil &
Gas** (17 SKUs), **Healthcare/Neurology** (10 SKUs), and **Manufacturing**
(7 SKUs — complete coverage across reliability + quality + operations +
supply chain + warehouse):

- **MGG-001**: Factory Sensor Dataset (IIoT sensor streams)
- **MFG-002**: Machine Failure Event Records (CMMS, ISO 14224)
- **MFG-003**: Predictive Maintenance Dataset (RUL ML training)
- **MFG-004**: Quality Control Dataset (SPC, MSA, 6 Sigma)
- **MFG-005**: Manufacturing Line Performance (OEE, TPM, Lean)
- **MFG-006**: Supply Chain Disruption Dataset (SCRM, bullwhip)
- **MFG-007**: Warehouse Operations Dataset (WMS, picking, perfect order) — this SKU

Catalog: [huggingface.co/xpertsystems](https://huggingface.co/xpertsystems).