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toolathon123/manufacturing-cost-optimization-2026Q3

Manufacturing Quarterly Cost Optimization Dataset (2026Q3) Unified quarterly cost-analysis dataset for the manufacturing group, merged from the China / Japan / India factory datasets hosted on Hugging Face. Contents 11,100 records (>= 10,000) covering 9 plants across 3 regions. Source datasets: toolathon123/manufacturing-cn-energy-2026Q3 — China energy & raw material (4,200 rows) toolathon123/manufacturing-jp-maintenance-2026Q3 — Japan maintenance & downtime (3… See the full description on the dataset page: https://huggingface.co/datasets/toolathon123/manufacturing-cost-optimization-2026Q3.

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Dataset Card

Manufacturing Quarterly Cost Optimization Dataset (2026Q3)

Unified quarterly cost-analysis dataset for the manufacturing group, merged from the China / Japan / India factory datasets hosted on Hugging Face.

—11,100 records (>= 10,000) covering 9 plants across 3 regions.
—Source datasets:
—toolathon123/manufacturing-cn-energy-2026Q3 — China energy & raw material (4,200 rows)
—toolathon123/manufacturing-jp-maintenance-2026Q3 — Japan maintenance & downtime (3,100 rows)
—toolathon123/manufacturing-in-labor-2026Q3 — India labor & output (3,800 rows)

Columns

Column	Type	Description
`timestamp`	datetime	Record timestamp (UTC)
`plant_id`	string	Plant identifier, e.g. `CN-SH`, `JP-TK`, `IN-MU`
`region`	string	`China`, `Japan` or `India`
`quarter`	string	Reporting quarter, `2026Q3`
`shift`	string	India: `Morning` / `Evening` / `Night`
`machine_id`	string	Japan: machine identifier
`energy_kwh`	float	China: energy consumption (kWh)
`raw_material_kg`	float	China: raw material consumption (kg)
`maintenance_cost_usd`	float	Japan: maintenance cost (USD)
`labor_cost_usd`	float	India: labor cost (USD)
`downtime_min`	float	Japan: downtime (minutes)
`labor_hours`	float	India: labor hours
`output_units`	float	Produced units
`energy_cost`	float	Derived: `energy_kwh * 0.12`
`maintenance_cost`	float	Derived: `maintenance_cost_usd`
`labor_cost`	float	Derived: `labor_cost_usd`
`total_cost`	float	Derived: energy + maintenance + labor
`unit_cost`	float	Derived: `total_cost / output_units`

Cleaning & aggregation rules

—timestamp parsed as datetime.
—Missing values in cost-related columns filled with 0.
—Per-row costs derived then aggregated per plant_id (see plant_cost_summary.csv).

Files

—data/train-00000-of-00001.parquet — unified cleaned dataset (11,100 rows)
—plant_cost_summary.csv — quarterly total cost & unit cost per plant
—cost_report_2026Q3.md — markdown cost report
—README.md — this card

Usage

python

from datasets import load_dataset

ds = load_dataset("toolathon123/manufacturing-cost-optimization-2026Q3", split="train")
df = ds.to_pandas()
print(df.head())