| --- |
| 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). |
|
|
