Manufacturing SPC (Cpk / Xbar-R)
5 processes, 450 subgroups, 2,250 measurements, where process capability and out-of-control detection come from a real Xbar-R control chart -- including one process with an engineered tool-wear drift that actually trips Western Electric rules where it happens.
A measurement column of independently random floats can't sit at a declared Cp/Cpk, can't split into the right within/between variance an Xbar-R chart actually estimates from, and can't trigger a Western Electric Rule violation at a believable rate. Here, sigma is estimated from Rbar/d2 (the real Xbar-R estimator, not pooled standard deviation), control limits are grand_mean +/- A2 x Rbar set during a Phase I baseline, and one process is built off-center on purpose so Cp measurably exceeds Cpk. A fifth process carries a genuine engineered tool-wear drift over its last third of subgroups, tripping Rule 1 and Rule 4 on 100% of its final subgroups and 0% before the drift starts -- the actual signature these rules exist to catch.
- manufacturing
- SPC
- Cpk
- Six Sigma
- control charts
- quality control
Explore
Every table, profiled
Each column's type, spread, empties and most common values, measured from the CSVs in the download. Switch to the first rows to see the data exactly as it sits in the file.
4 tables
measurements.csv
2,250 rows · 5 columns · 2 foreign keys
Keys
Every join resolves
3 foreign keys, 4,950 references checked against the table each one points at. None points at a row that does not exist.
measurements
- subgroup_idsubgroups.subgroup_id2,250 rows checked0 orphans
- process_idprocesses.process_id2,250 rows checked0 orphans
subgroups
- process_idprocesses.process_id450 rows checked0 orphans
Checks
What we checked
- measured Cpk reconciles to the declared target within sampling tolerance, on all 5 processes
- Cp >= Cpk on every process; 'Housing Bolt Torque' shows Cp 1.94 vs Cpk 1.41 (built off-center)
- 'Valve Seal Thickness' measures Cpk 0.83, below the 1.33 capability minimum -- a real failing example
- stable processes trip a Western Electric rule on 3.3% of subgroups or fewer (a low false-alarm rate)
- 'Bearing Race Width' (tool-wear drift): 100% of its final subgroups trip a rule vs 0% before the drift started
- every subgroup's xbar and r reconcile exactly to its own 5 raw measurements
The same checks ship in the zip as INTEGRITY.txt, so you can rerun them in any SQL engine. How we verify
Questions
Worth asking it
- 1Does the measured Cpk (from Rbar/d2) actually reconcile with the declared target?
- 2How much does an off-center mean cost a process in Cpk vs its potential Cp?
- 3Can a control-chart rule catch a gradual tool-wear drift before it becomes a defect?
- 4What does a genuinely failing process (below the 1.33 capability minimum) look like in real subgroup data?
Full version · $15
This sample, or the full SPC for Precision Machining
Six months of control charts with the ground truth underneath. Try its free preview before you decide.
| This sample | SPC for Precision Machining | |
|---|---|---|
| Rows | 2,710 | 515,442 |
| Tables | 4 | 15 |
| Columns | 32 | 166 |
| Formats | CSV | CSV |
| Audit | Keys and the checks listed above | 101 checks, all listed |
| Licence | CC0, free | Commercial use, $15 once |
| Built for | manufacturing, SPC, Cpk, Six Sigma | Control charts and capability, Run-rule and anomaly detection with labels, Root cause analysis, Tool wear and predictive maintenance, Measurement system analysis, Teaching and dashboards |
Use it
Take it, or make one shaped like yours
Rebuild it byte for byte
pip install misata curl -O https://raw.githubusercontent.com/rasinmuhammed/misata/main/examples/manufacturing_spc.py python -c "from manufacturing_spc import build, verify; t = build(seed=19); verify(t)"
This dataset solves each process's underlying sigma from a declared target Cpk, estimates it back out via a Phase I Xbar-R baseline, and engineers a tool-wear drift into one process's final subgroups -- none of which a per-column declared schema can express, so it ships as a runnable script rather than a schema.yaml a bare misata generate could regenerate honestly.
Your own tables, your own numbers
Describe the data you need in Studio and get connected tables with the totals you state, profiled like this and checked before you download.
The full version, and what goes with it
All premiumAn X-bar chart with the truth underneath: flatness, characteristic 139
3 plants, 36 CNC machines, six months
Six months of control charts with the ground truth underneath
Answer key
- active_cause_id
- true_cause_shift_sigma
- true_tool_wear_sigma
- +6 more
- Rows
- 515,442
- Tables
- 15
- Checks
- 101 of 101
Free previewProfile and previewVibration through a life that ends in failure
240 machines at 4 plants, two years
Two years of sensor readings, failures, repairs and costs for 240 machines
Answer key
- failure_mode
- true_life_days
- true_failure_at
- +6 more
- Rows
- 179,038
- Tables
- 6
- Checks
- 67 of 67
Free previewProfile and previewQA review of manufacturing batch records, by month
One oral-solids plant, five systems, two years
Two years of a pharmaceutical plant's records, from process order to QP release
Answer key
- root_cause
- batch_disposition_outcome
- conclusion
- +6 more
- Rows
- 661,497
- Tables
- 65
- Checks
- 133 of 133
Free previewProfile and preview
Related free datasets
All 12- Retail star schemaA dimensional model you can actually practise joins on: one fact table, four dimensions, 47,051 rows, every join resolving and every total known.47,051 rows · 5 tables
- Machine degradation100 machines monitored from commissioning to failure, with an exact remaining-useful-life label on every one of 23,118 readings.46,336 rows · 3 tables
- Ecommerce storefrontA year of orders across five joined tables, with best sellers, heavy buyers, a real Q4 peak and totals that reconcile to the cent.49,493 rows · 5 tables
- B2B SaaS subscription analyticsAccounts, seats, MRR, churn and support load, where company size actually drives the plan.42,240 rows · 5 tables
- Credit risk portfolio3,000 borrowers and 4,200 commercial loans, where a credit rating actually predicts whether the loan defaults, at the rate S&P's own data says it should.7,200 rows · 2 tables
- Network intrusion / netflow500 hosts and 20,000 labeled flows, where a port scan, a DDoS flood, and a brute-force attempt each carry the real statistical signature their MITRE ATT&CK technique actually leaves in traffic.20,500 rows · 2 tables
Fully synthetic. No real person, company or transaction is represented, and no production data was read to make it. CC0: use it anywhere, no attribution needed.