Datasets:

xpertsystems
/

mfg005-sample

+{
+  "line_id": "str",
+  "plant_id": "str",
+  "work_center_id": "str",
+  "shift_id": "str",
+  "shift_date": "str",
+  "shift_number": "int64",
+  "shift_duration_minutes": "float64",
+  "product_id": "str",
+  "product_family": "str",
+  "production_order_id": "str",
+  "line_type": "str",
+  "automation_level": "str",
+  "number_of_stations": "int64",
+  "line_configuration": "str",
+  "plant_location": "str",
+  "industry_sector": "str",
+  "takt_time_seconds": "float64",
+  "designed_cycle_time_seconds": "float64",
+  "actual_cycle_time_avg_seconds": "float64",
+  "actual_cycle_time_std_seconds": "float64",
+  "actual_cycle_time_min_seconds": "float64",
+  "actual_cycle_time_max_seconds": "float64",
+  "cycle_time_percentile_p50": "float64",
+  "cycle_time_percentile_p95": "float64",
+  "cycle_time_cov": "float64",
+  "bottleneck_station_id": "str",
+  "bottleneck_cycle_time_seconds": "float64",
+  "bottleneck_utilisation_pct": "float64",
+  "secondary_bottleneck_station_id": "str",
+  "cycle_time_loss_seconds": "float64",
+  "planned_production_quantity": "int64",
+  "actual_production_quantity": "int64",
+  "good_units_produced": "int64",
+  "defective_units_produced": "int64",
+  "scrap_units": "int64",
+  "rework_units": "int64",
+  "throughput_rate_actual_uph": "float64",
+  "throughput_rate_design_uph": "float64",
+  "throughput_rate_ratio": "float64",
+  "rolled_throughput_yield_pct": "float64",
+  "total_parts_processed": "int64",
+  "wip_queue_avg_units": "float64",
+  "wip_queue_max_units": "int64",
+  "output_variance_pct": "float64",
+  "units_per_operator_hour": "float64",
+  "planned_production_time_minutes": "float64",
+  "available_time_minutes": "float64",
+  "oee_availability": "float64",
+  "oee_performance": "float64",
+  "oee_quality": "float64",
+  "oee_overall": "float64",
+  "teep_pct": "float64",
+  "loading_pct": "float64",
+  "availability_loss_minutes": "float64",
+  "performance_loss_units": "float64",
+  "quality_loss_units": "int64",
+  "six_big_losses_breakdown": "str",
+  "oee_benchmark_class": "str",
+  "oee_loss_primary_driver": "str",
+  "changeover_time_minutes": "float64",
+  "changeover_count": "int64",
+  "downtime_unplanned_minutes": "float64",
+  "downtime_planned_minutes": "float64",
+  "downtime_event_count": "int64",
+  "mtbf_minutes": "float64",
+  "mttr_minutes": "float64",
+  "largest_downtime_event_minutes": "float64",
+  "equipment_failure_flag": "str",
+  "downtime_category_mechanical": "float64",
+  "downtime_category_electrical": "float64",
+  "downtime_category_tooling": "float64",
+  "downtime_category_material": "float64",
+  "downtime_category_operator": "float64",
+  "downtime_category_quality_hold": "float64",
+  "planned_maintenance_flag": "str",
+  "maintenance_type_last": "str",
+  "equipment_age_years": "float64",
+  "equipment_condition_score": "float64",
+  "operator_headcount_planned": "int64",
+  "operator_headcount_actual": "int64",
+  "operator_utilisation_pct": "float64",
+  "operator_skill_level_avg": "str",
+  "absenteeism_rate_pct": "float64",
+  "overtime_hours": "float64",
+  "ergonomic_incident_flag": "str",
+  "operator_variance_flag": "str",
+  "cross_training_ratio_pct": "float64",
+  "shift_supervisor_id": "str",
+  "defect_rate_ppm": "float64",
+  "sigma_level": "float64",
+  "cpk_primary_characteristic": "float64",
+  "cp_primary_characteristic": "float64",
+  "primary_defect_type": "str",
+  "secondary_defect_type": "str",
+  "inspection_method": "str",
+  "spc_control_chart_flag": "str",
+  "control_chart_out_of_control": "str",
+  "quality_alert_issued": "str",
+  "customer_complaint_flag": "str",
+  "first_pass_yield_pct": "float64",
+  "right_first_time_pct": "float64",
+  "energy_consumption_kwh": "float64",
+  "energy_per_good_unit_kwh": "float64",
+  "peak_demand_kw": "float64",
+  "compressed_air_consumption_m3": "float64",
+  "coolant_consumption_litres": "float64",
+  "energy_efficiency_score": "float64",
+  "carbon_footprint_kg_co2": "float64",
+  "direct_labour_cost_usd": "float64",
+  "overhead_cost_usd": "float64",
+  "material_cost_per_unit_usd": "float64",
+  "scrap_cost_usd": "float64",
+  "rework_cost_usd": "float64",
+  "downtime_cost_per_minute_usd": "int64",
+  "total_downtime_cost_usd": "float64",
+  "cost_of_poor_quality_usd": "float64",
+  "value_added_ratio_pct": "float64",
+  "production_cost_per_unit_usd": "float64"
+}

README.md ADDED Viewed

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+---
+license: cc-by-nc-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+  - time-series-forecasting
+language:
+  - en
+tags:
+  - synthetic
+  - manufacturing
+  - oee
+  - overall-equipment-effectiveness
+  - line-performance
+  - takt-time
+  - cycle-time
+  - bottleneck-analysis
+  - theory-of-constraints
+  - toc
+  - lean-manufacturing
+  - six-big-losses
+  - tpm
+  - total-productive-maintenance
+  - nakajima
+  - goldratt
+  - womack
+  - mes
+  - manufacturing-execution-system
+  - iso-22400
+  - semi-e10
+  - shift-performance
+  - mtbf
+  - mttr
+  - first-pass-yield
+  - value-added-ratio
+  - lean-six-sigma
+  - throughput-optimization
+  - changeover
+  - smed
+pretty_name: "MFG-005 — Manufacturing Line Performance Dataset (Sample)"
+size_categories:
+  - 1K<n<10K
+---
+# MFG-005 — Manufacturing Line Performance Dataset (Sample)
+A schema-identical preview of **MFG-005**, the XpertSystems.ai synthetic
+**shift-level manufacturing line performance** dataset for OEE ML,
+Theory of Constraints bottleneck analysis, Six Big Losses prediction,
+Lean Six Sigma improvement targeting, MES analytics, and Industrie 4.0
+production research. The full product covers 10,000-100,000 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-005 does — completing the 5-SKU Manufacturing vertical
+MFG-005 is the **fifth Manufacturing & Industrial Systems SKU** in the
+XpertSystems catalog, completing a comprehensive Manufacturing vertical
+covering BOTH reliability engineering AND quality + operations
+management:
+| SKU | Domain | Granularity | Primary Audience |
+|---|---|---|---|
+| MGG-001 | Reliability — sensor streams | 1-min to 1-hr | IIoT, anomaly detection |
+| MFG-002 | Reliability — failure events | One row per event | CMMS, reliability engineering |
+| MFG-003 | Reliability — RUL training | Multi-obs per asset | PdM ML, PHM Society |
+| MFG-004 | Quality — inspection records | One row per inspection | QMS, SPC, MSA, 6 Sigma |
+| **MFG-005** | **Operations — line performance** | **One row per shift × line** | **MES, OEE, TPM, Lean** |
+Where MFG-004 captures **per-part quality** (inspection-record granularity),
+MFG-005 captures **per-shift line performance** (production-system
+granularity). This is the data shape that flows into MES (Manufacturing
+Execution Systems) like Rockwell PlantPAx, Siemens Opcenter (formerly
+SIMATIC IT), GE Digital Plant Applications (formerly Proficy),
+Wonderware MES (AVEVA), Honeywell Forge, and SAP Digital Manufacturing.
+| Buyer Persona | Use Case |
+|---|---|
+| **MES Vendors (Rockwell, Siemens Opcenter, GE Digital, AVEVA Wonderware, Honeywell Forge, SAP DM)** | OEE/TPM workflow training data |
+| **OEE Software (Vorne XL, FactoryTalk Analytics, OEE Toolkit)** | OEE benchmarking + Six Big Losses ML |
+| **Lean Manufacturing Consultancies (Toyota, Shingo Institute, Lean Enterprise Institute)** | OEE improvement case-study data |
+| **TPM (Total Productive Maintenance) Programs** | Six Big Losses framework training |
+| **Theory of Constraints (TOC) Practitioners (Goldratt Institute)** | Bottleneck analysis ML |
+| **Lean Six Sigma (Shingo / TPS)** | Value-added ratio improvement targeting |
+| **Production Engineering** | Takt time vs cycle time optimization |
+| **SMED (Single-Minute Exchange of Die)** | Changeover time reduction |
+| **Industrie 4.0 / Smart Factory** | Digital twin training data |
+| **Energy Management (ISO 50001)** | Energy per good unit ML |
+This is the substrate **MES vendors, OEE software companies, Lean Six
+Sigma consultancies, Theory of Constraints practitioners, TPM programs,
+and Industrie 4.0 research programs** have been waiting for: a coherent
+shift-level line performance dataset where OEE A×P×Q decomposition ×
+Six Big Losses × bottleneck utilization × cycle time vs takt time × FPY
+× energy × cost all interact with **Nakajima 1988 / Goldratt 1984 /
+Womack 1990 / SEMI E10 / ISO 22400-grade calibration**.
+---
+## What's inside — five related CSV files
+MFG-005 is a **multi-output relational** dataset with five CSVs sharing
+`line_id` as join key.
+| File | Rows (sample) | Columns | Size |
+|---|---:|---:|---|
+| `mfg005_synthetic_line_performance.csv` | 3,000 | 118 | ~2.3 MB |
+| `throughput_vs_takt.csv` | 3,000 | 11 | ~316 KB |
+| `bottleneck_analysis_report.csv` | 235 | 5 | ~14 KB |
+| `oee_summary_by_line.csv` | 36 | 9 | ~5 KB |
+| `downtime_pareto.csv` | 6 | 3 | ~360 B |
+Plus `mfg005_metadata.json` with run configuration.
+Schemas are provided in five matching JSON files.
+### Main schema module structure (118 columns total)
+| Module | Cols | Coverage |
+|---|---:|---|
+| Line identity | 16 | line_id, plant_id, work_center_id, shift_id, date, shift number, duration, product_id, family, line_type, automation level, stations, configuration, plant location, sector, production order |
+| Cycle time | 14 | takt time, designed CT, actual CT avg/std/min/max, P50/P95, CoV, bottleneck station + CT + util, secondary bottleneck, CT loss |
+| Production volume | 14 | planned/actual quantity, good/defective/scrap/rework units, throughput actual/design UPH, ratio, RTY, total parts, WIP queue avg/max, output variance, UPH per operator |
+| OEE (Nakajima 1988) | 13 | planned production time, available time, A/P/Q components, OEE overall, TEEP, Loading, A/P/Q losses, Six Big Losses breakdown, OEE benchmark class, primary loss driver |
+| Changeover & downtime | 17 | changeover time + count, unplanned/planned DT, DT events, MTBF, MTTR, largest DT, equipment failure flag, 6 downtime categories (mech/elec/tool/material/operator/quality), planned maintenance, last maintenance type |
+| Equipment | 2 | age years, condition score |
+| Operator | 10 | headcount planned/actual, utilization, skill level avg, absenteeism, overtime, ergonomic incidents, variance flag, cross-training ratio, supervisor |
+| Quality | 11 | defect rate PPM, sigma level, Cpk + Cp primary, primary + secondary defect type, inspection method, SPC chart flag, OOC flag, quality alert, customer complaint, FPY, RFT |
+| Energy & sustainability | 7 | energy kWh + per good unit, peak demand, compressed air, coolant, energy efficiency, carbon footprint |
+| Cost | 12 | direct labour, overhead, material per unit, scrap, rework, downtime cost per min + total, COPQ, value-added ratio, production cost per unit |
+---
+## Calibration sources
+Every distribution is anchored to **named manufacturing engineering
+standards or canonical frameworks**. The headline anchors are
+**Nakajima 1988** (Total Productive Maintenance / OEE framework),
+**Goldratt 1984** (Theory of Constraints), and **Womack 1990 Lean
+Thinking**. Other anchors:
+- **Nakajima 1988 Introduction to TPM** — Six Big Losses framework
+  (equipment failure, setup/adjustment, idling/minor stops, reduced
+  speed, process defects, startup yield loss); OEE = A × P × Q
+  decomposition.
+- **Goldratt 1984 The Goal + Theory of Constraints** — bottleneck
+  identification, drum-buffer-rope synchronization, throughput
+  optimization.
+- **Womack 1990 The Machine That Changed the World + Lean Thinking** —
+  value-added ratio, 8 wastes (muda), value stream mapping.
+- **Ohno 1988 Toyota Production System** — takt time, standard work,
+  cycle time stability.
+- **SEMI E10 Standard** — equipment performance metrics for
+  semiconductor manufacturing.
+- **ISO 22400-1/-2** — KPIs for manufacturing operations management
+  (OEE, NEE, OEE_PR, Quality Rate).
+- **ANSI/ISA-95** — enterprise-control system integration.
+- **SAE J4000** — Identification and Measurement of Best Practice in
+  Implementation of Lean Operation.
+- **ARC Advisory Group + SME (Society of Manufacturing Engineers)
+  Industry Benchmarks** — OEE/availability/performance benchmarks by
+  sector.
+- **Shingo Prize Model + Toyota Production System** — operational
+  excellence assessment framework.
+- **AIAG / VDA standards** — automotive manufacturing benchmarks.
+- **SMED (Single-Minute Exchange of Die) — Shingo 1985** — changeover
+  time reduction methodology.
+- **ISO 50001** — energy management systems for manufacturing.
+- **Pyzdek 2003 Six Sigma Handbook + Motorola 1986** — sigma level
+  framework for manufacturing line quality.
+---
+## Validation scorecard
+The wrapper ships a 10-metric Nakajima/TOC/Lean/ISO 22400-anchored
+scorecard (`validation_scorecard.json`) that re-scores the dataset on
+every generation. Default seed 42 result:
+| ID | Metric | Target | Observed | Source |
+|---|---|---|---:|---|
+| M01 | OEE Overall Mean | 0.53–0.77 | **0.653** | **Nakajima 1988 / ARC / SME** |
+| M02 | OEE Availability | 0.75–0.95 | **0.858** | **Nakajima 1988 / SMRP** |
+| M03 | OEE Quality (FLOOR ≥95%) | ≥0.95 | **0.994** | **Nakajima 1988 / Six Sigma** |
+| M04 | Bottleneck Utilization % | 82–102 | **95.01** | **Goldratt 1984 TOC** |
+| M05 | World-Class OEE Share (≥0.85) | 0.00–0.20 | **0.065** | Nakajima 1988 / ARC |
+| M06 | Performance Loss Primary Driver | 0.40–0.90 | **0.752** | **Six Big Losses / TPM** |
+| M07 | Sigma Level Mean (FLOOR ≥2.5σ) | ≥2.5σ | **4.67σ** | Motorola 1986 / ASQ |
+| M08 | Throughput Ratio | 0.60–1.00 | **0.819** | **ISO 22400-2 / SEMI E10** |
+| M09 | Cycle Time CoV (CEILING ≤0.23) | ≤0.23 | **0.094** | **Lean / Toyota Production System** |
+| M10 | Value-Added Ratio % (FLOOR ≥25%) | ≥25% | **50.10** | **Womack 1990 Lean Thinking** |
+**Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.**
+**Standout calibration depth — this is the most precisely-centered
+Manufacturing SKU**:
+- **M01 OEE 65.28% vs target 65%** — *0.28pp deviation* 🎯
+- **M02 Availability 85.84% vs target 85%** — *0.84pp deviation* 🎯
+- **M10 Value-added ratio 50.10% vs Womack target 50%** — *0.10pp deviation* 🎯
+- **M08 Throughput ratio 0.819 vs target 0.80** — *1.9pp deviation*
+**OEE A×P×Q math verifies**: 0.858 × 0.763 × 0.994 = **0.651** (matches
+observed OEE 0.653 within rounding).
+**OEE by sector** reproduces published Nakajima benchmarks:
+- Medical 0.69, Pharma 0.67, Industrial 0.65, Electronics 0.66,
+  Automotive 0.64, Food/Bev 0.65
+**Six Big Losses primary driver** matches TPM textbook: performance_loss
+75% / availability_loss 25% / quality_loss <1% (mature plants are
+performance-bound, not availability-bound or quality-bound).
+---
+## Suggested use cases
+- **OEE prediction ML** — line characteristics + shift features ×
+  OEE overall prediction (regression).
+- **Six Big Losses classification** — multi-class classifier for
+  primary OEE loss driver (equipment failure / setup / idling /
+  reduced speed / process defects / startup yield).
+- **Bottleneck identification (TOC)** — line topology + cycle times
+  × bottleneck station prediction.
+- **Takt time vs cycle time gap analysis** — designed CT + automation
+  + skill × actual CT prediction.
+- **Throughput optimization** — actual/design UPH ratio prediction
+  for capacity planning.
+- **Changeover time reduction (SMED)** — changeover_time +
+  changeover_count × throughput impact modeling.
+- **Lean value-added improvement** — value_added_ratio × waste
+  category for 8-wastes (muda) prioritization.
+- **Energy efficiency benchmarking** — energy_per_good_unit + peak
+  demand × sector for ISO 50001 EnPI baselining.
+- **Cost-of-poor-quality modeling** — scrap + rework + COPQ × defect
+  type × economic outcomes.
+- **Downtime Pareto analysis** — 6 downtime category aggregates ×
+  improvement targeting (mechanical-dominant lines vs
+  electrical-dominant).
+- **Operator productivity** — headcount + skill_level + cross-training
+  × OEE for HR + Lean training ROI modeling.
+---
+## Loading
+```python
+from datasets import load_dataset
+main = load_dataset(
+    "xpertsystems/mfg005-sample",
+    data_files="mfg005_synthetic_line_performance.csv",
+    split="train",
+)
+oee_summary = load_dataset(
+    "xpertsystems/mfg005-sample",
+    data_files="oee_summary_by_line.csv",
+    split="train",
+)
+```
+Or with pandas directly:
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+main_path = hf_hub_download(
+    repo_id="xpertsystems/mfg005-sample",
+    filename="mfg005_synthetic_line_performance.csv",
+    repo_type="dataset",
+)
+df = pd.read_csv(main_path)
+# OEE A×P×Q decomposition by sector
+for sector, sub in df.groupby("industry_sector"):
+    a = sub["oee_availability"].mean()
+    p = sub["oee_performance"].mean()
+    q = sub["oee_quality"].mean()
+    oee = sub["oee_overall"].mean()
+    print(f"{sector:14s}: OEE={oee:.3f} = A({a:.3f}) × P({p:.3f}) × Q({q:.3f})")
+# Six Big Losses primary driver distribution
+print(df["oee_loss_primary_driver"].value_counts(normalize=True))
+# Bottleneck utilization by line type (TOC)
+bn_by_type = df.groupby("line_type")["bottleneck_utilisation_pct"].mean()
+print(bn_by_type.sort_values(ascending=False))
+```
+Five schema JSON files are bundled for pipeline integration:
+```python
+import json
+schema_main = json.load(open("MFG_005_main_schema.json"))
+schema_oee = json.load(open("MFG_005_oee_summary_schema.json"))
+schema_bn = json.load(open("MFG_005_bottleneck_schema.json"))
+schema_takt = json.load(open("MFG_005_throughput_schema.json"))
+schema_dt = json.load(open("MFG_005_downtime_pareto_schema.json"))
+```
+This dataset is **cross-sectional with shift-level granularity** —
+one row per shift × line, ordered by shift_date but not strictly
+longitudinal per asset. For time-series shift trend analysis, group
+by `line_id` and sort by `shift_date`.
+---
+## Schema highlights
+**Line identity** — `line_id`, `plant_id`, `work_center_id`, `shift_id`,
+`shift_date`, `shift_number` ∈ {1, 2, 3}, `shift_duration_minutes`,
+`product_id`, `product_family`, `production_order_id`, `line_type` ∈
+{assembly_line, machining_cell, packaging_line, fabrication,
+chemical_process, discrete_manufacturing, batch_process,
+continuous_process, hybrid}, `automation_level` ∈ {manual,
+semi_automated, highly_automated, lights_out, cobotic},
+`number_of_stations`, `line_configuration` ∈ {serial, parallel,
+u_shaped, flexible, transfer_line, job_shop}, `plant_location` (12
+global locations), `industry_sector` ∈ {automotive, electronics,
+pharma, food_bev, aerospace, industrial, consumer, medical, chemical,
+packaging}.
+**Cycle time** — `takt_time_seconds`, `designed_cycle_time_seconds`,
+`actual_cycle_time_avg_seconds`, std/min/max, P50/P95, `cycle_time_cov`,
+`bottleneck_station_id`, `bottleneck_cycle_time_seconds`,
+`bottleneck_utilisation_pct`, `secondary_bottleneck_station_id`,
+`cycle_time_loss_seconds`.
+**Production volume** — `planned_production_quantity`,
+`actual_production_quantity`, `good_units_produced`,
+`defective_units_produced`, `scrap_units`, `rework_units`,
+`throughput_rate_actual_uph`, `throughput_rate_design_uph`,
+`throughput_rate_ratio`, `rolled_throughput_yield_pct`,
+`total_parts_processed`, `wip_queue_avg_units`, `wip_queue_max_units`,
+`output_variance_pct`, `units_per_operator_hour`.
+**OEE (Nakajima 1988)** — `planned_production_time_minutes`,
+`available_time_minutes`, `oee_availability`, `oee_performance`,
+`oee_quality`, `oee_overall`, `teep_pct`, `loading_pct`,
+`availability_loss_minutes`, `performance_loss_units`,
+`quality_loss_units`, `six_big_losses_breakdown`, `oee_benchmark_class`
+∈ {world_class, good, average, poor}, `oee_loss_primary_driver` ∈
+{availability_loss, performance_loss, quality_loss}.
+**Changeover & downtime** — `changeover_time_minutes`,
+`changeover_count`, `downtime_unplanned_minutes`,
+`downtime_planned_minutes`, `downtime_event_count`, `mtbf_minutes`,
+`mttr_minutes`, `largest_downtime_event_minutes`,
+`equipment_failure_flag`, `downtime_category_mechanical`,
+`downtime_category_electrical`, `downtime_category_tooling`,
+`downtime_category_material`, `downtime_category_operator`,
+`downtime_category_quality_hold`, `planned_maintenance_flag`,
+`maintenance_type_last` ∈ {predictive, preventive, corrective,
+emergency}.
+**Equipment** — `equipment_age_years`, `equipment_condition_score`.
+**Operator** — `operator_headcount_planned`, `operator_headcount_actual`,
+`operator_utilisation_pct`, `operator_skill_level_avg` ∈ {trainee,
+semi_skilled, skilled, expert, multi_skilled}, `absenteeism_rate_pct`,
+`overtime_hours`, `ergonomic_incident_flag`, `operator_variance_flag`,
+`cross_training_ratio_pct`, `shift_supervisor_id`.
+**Quality** — `defect_rate_ppm`, `sigma_level`,
+`cpk_primary_characteristic`, `cp_primary_characteristic`,
+`primary_defect_type`, `secondary_defect_type`, `inspection_method`,
+`spc_control_chart_flag`, `control_chart_out_of_control`,
+`quality_alert_issued`, `customer_complaint_flag`,
+`first_pass_yield_pct`, `right_first_time_pct`.
+**Energy & sustainability** — `energy_consumption_kwh`,
+`energy_per_good_unit_kwh`, `peak_demand_kw`,
+`compressed_air_consumption_m3`, `coolant_consumption_litres`,
+`energy_efficiency_score`, `carbon_footprint_kg_co2`.
+**Cost** — `direct_labour_cost_usd`, `overhead_cost_usd`,
+`material_cost_per_unit_usd`, `scrap_cost_usd`, `rework_cost_usd`,
+`downtime_cost_per_minute_usd`, `total_downtime_cost_usd`,
+`cost_of_poor_quality_usd`, `value_added_ratio_pct`,
+`production_cost_per_unit_usd`.
+---
+## Calibration notes & limitations
+In the spirit of honest synthetic data, a few things buyers of the sample
+should know:
+1. **Industry sector mix is skewed at n=3,000**. Only 6 of 10 configured
+   sectors are well-represented (industrial 38%, electronics 24%,
+   automotive 22%, with smaller pharma/medical/food-bev shares). The
+   full product (10K-100K records) distributes evenly across all 10
+   sectors. For sector-specific modeling at this sample size, filter
+   carefully or use the full product.
+2. **TEEP averages 20.3% and Loading 31.1%** — both significantly below
+   typical 24/7 plant values. TEEP = OEE × Loading; the generator's
+   Loading parameter reflects partial-utilization plants (e.g., 1-2
+   shifts/day rather than continuous 3-shift). For 24/7 continuous-
+   process plants, the full product supports configurable Loading
+   targets.
+3. **Quality component of OEE is 99.36%** (very high). Real-world OEE
+   quality components vary: world-class >99%, typical 95-99%, low-yield
+   <95%. The generator centers Quality at the upper end; for lower-yield
+   modeling (electronics PCB rework, pharmaceutical batch yield), the
+   full product calibrates per sector.
+4. **Absenteeism averages 8.5%** — above typical industrial 3-5%. The
+   generator's absenteeism model is skewed slightly high; for benchmark
+   absenteeism modeling, target 3-5% in the full product configuration.
+5. **Scrap cost ($2/shift) and rework cost ($9/shift) are very low** in
+   absolute terms because they're rolled up into the broader COPQ
+   metric ($1,901/shift). For per-event cost modeling, use the COPQ
+   composite rather than the scrap/rework components individually.
+6. **OEE by automation level is non-monotonic**: cobotic 0.69 > lights_out
+   0.68 ≈ manual 0.68 > semi-auto 0.65 > highly_auto 0.62. This
+   reflects the real-world observation that highly-automated lines
+   often have more downtime than well-run manual lines — automation
+   amplifies both performance AND failure modes. For automation-ROI
+   modeling, this non-monotonicity is realistic.
+7. **Cycle time exceeds takt time on 81% of shifts** (CT > takt). This
+   is realistic — most production lines run slower than designed takt
+   under real-world conditions (downtime, setup, quality losses). The
+   takt time represents customer-demand-driven design rate; actual CT
+   includes all losses.
+8. **MTBF 199 minutes / MTTR 17 minutes** — realistic shift-level
+   reliability metrics. Different from MFG-002/MFG-003 which use
+   hours-scale MTBF (asset-level vs shift-level reliability differs).
+9. **6 downtime categories show realistic Pareto** (mechanical 30 min >
+   electrical 12 > tooling 8 > quality 5 > material 5 > operator 2).
+   Mechanical dominance reflects rotating-equipment-heavy fleet; for
+   electronics/assembly lines, the full product supports different
+   downtime Pareto profiles.
+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-005** product covers 10,000-100,000 shift records with
+configurable `--automation_profile` (modern_greenfield /
+brownfield_mixed / manual_intensive / lights_out), `--oee_target_class`
+(world_class / good / average / poor / mixed), refined sector-specific
+OEE benchmarks per Nakajima 1988 published targets, configurable
+absenteeism profiles per region (US/EU/APAC industrial benchmarks),
+24/7 vs 2-shift vs 1-shift Loading configurations, pre-built feature
+engineering pipelines for OEE prediction ML (shift lag features,
+rolling MTBF/MTTR, seasonal patterns), Industrie 4.0 / smart factory
+extension columns (digital twin sync flags, edge compute latency,
+OPC-UA tag counts), and energy management ISO 50001 baseline /
+performance period decomposition for EnPI tracking. Available under
+commercial license — contact
+[pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai).
+XpertSystems.ai also publishes synthetic data products across **Oil &
+Gas** (17 SKUs, OREDA/ISO 14224/API/IPIECA standards),
+**Healthcare/Neurology** (10 SKUs, ENROLL-HD/PRO-ACT/TRACK-HD/CLARITY-AD
+clinical trial calibration), and **Manufacturing** (5 SKUs covering
+reliability engineering AND quality + operations management):
+- **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) — this SKU
+Catalog: [huggingface.co/xpertsystems](https://huggingface.co/xpertsystems).

bottleneck_analysis_report.csv ADDED Viewed

	@@ -0,0 +1,236 @@

+line_id,bottleneck_station_id,count,avg_utilisation,avg_oee
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+MFG-LINE-PLT-006-02,WC-008,3,99.90000000000002,0.646
+MFG-LINE-PLT-002-03,WC-010,2,94.155,0.6374
+MFG-LINE-PLT-006-02,WC-005,2,99.9,0.61195
+MFG-LINE-PLT-006-02,WC-012,2,99.39,0.56705
+MFG-LINE-PLT-006-02,WC-013,1,99.9,0.7049
+MFG-LINE-PLT-006-01,WC-009,1,99.9,0.5643
+MFG-LINE-PLT-006-02,WC-001,1,99.9,0.4648

downtime_pareto.csv ADDED Viewed

	@@ -0,0 +1,7 @@

+category,total_minutes,cumulative_pct
+downtime_category_mechanical,91668.1,49.483000695810084
+downtime_category_electrical,35032.7,68.39386629110557
+downtime_category_tooling,23179.5,80.9063020744209
+downtime_category_quality_hold,14965.900000000001,88.98498637259469
+downtime_category_material,13654.2,96.35560699308023
+downtime_category_operator,6751.3,100.0

mfg005_synthetic_line_performance.csv ADDED Viewed

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oee_summary_by_line.csv ADDED Viewed

	@@ -0,0 +1,37 @@

+line_id,product_family,automation_level,oee_mean,oee_std,availability_mean,performance_mean,quality_mean,shifts
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+MFG-LINE-PLT-005-06,automotive_body,highly_automated,0.5779662921348314,0.1473849288765956,0.7793741573033708,0.742094382022472,0.9946988764044944,89
+MFG-LINE-PLT-006-01,food_beverage,semi_automated,0.6398558441558442,0.15433696260004268,0.8102259740259741,0.7840545454545454,0.9968805194805195,77
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throughput_vs_takt.csv ADDED Viewed

The diff for this file is too large to render. See raw diff