Pine Assembly Workshop: Yield and scrap
Yield and scrap for Pine Assembly Workshop. Yield by production order. Every order yields 2 good units for 2 ordered, so finished-goods yield is 100.00 percent on all four, while BUILD-1 scrapped 2 legs worth 6.00 USD at component level. The two measures are in separate columns on purpose: component scrap that is replaced before assembly does not reduce finished-goods yield, and reporting one as the other is the mistake this file exists to catch.
csv
text/csv
- Document Set
- manufacturing
- Industry
- manufacturing
- Source Kit
- inventory-manufacturing
- Entity
- Pine Assembly Workshop
- Synthetic
- true
- As Of
- 2026-09-08
Binary csv: no in-browser preview. Download it above to open in a compatible application.
Specifications
- Document Set
- manufacturing
- Industry
- manufacturing
- Source Kit
- inventory-manufacturing
- Entity
- Pine Assembly Workshop
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 4
- Finished Goods Yield Percent
- 100.00
- Component Scrap Units
- 2
- Component Scrap Value
- 6.00
Testing contract
Expected to pass- Scenario
- Compute yield from quantity_good over quantity_ordered, then look at the scrap columns separately.
- Expected result
- Yield is 100.00 percent on all 4 orders because no finished unit was lost. Component scrap is 2 LEG worth 6.00 USD on BUILD-1 only, and the scrap_part_id column says it is a component and not a DESK.
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
How to use this file for testing
“Pine Assembly Workshop: Yield and scrap” is a deterministic Novus Examples fixture for Data import, CSV parsing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 4 rows. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import pandas as pd
df = pd.read_csv("yield-and-scrap.csv")
print(df.head())
print(df.dtypes)Related files
- csvPine Assembly Workshop: Bill of materials exploded for a production orderBill of materials exploded for a production order for Pine Assembly Workshop. The same structure exploded for BUILD-1, an order for 2 DESK. Extended quantity is quantity per parent times parent quantity on every row, and the leaf totals of 8 LEG, 2 PANEL and 24 SCREW equal the material requirements the inventory-manufacturing model already holds for that order, which the model_requirement column repeats on each leaf row for comparison.
- csvPine Assembly Workshop: Certificate of conformance registerCertificate of conformance register for Pine Assembly Workshop. Two certificates against the same lot and the same inspection report, one per production order. The first discloses 2 nonconformances and the second none, because the nonconforming legs were consumed by the replacement on the first order and the second order drew clean stock from the same lot.
- csvPine Assembly Workshop: Exploded bill of materials whose sub-assembly quantities do not roll upExploded bill of materials whose sub-assembly quantities do not roll up for Pine Assembly Workshop. The same 6 rows with one number changed: the SCREW line under BASE carries an extended quantity of 12 instead of 16. That row now says 8 per parent times 2 parents is 12, which is arithmetically false, and the SCREW leaf total falls to 20 against the 24 the model_requirement_component_total column on the same rows still declares. The shortfall is 4 screws, 0.40 USD of material, and it is enough to leave one BASE sub-assembly short at build time.
- csvPine Assembly Workshop: Indented bill of materialsIndented bill of materials for Pine Assembly Workshop. The DESK structure exploded to 2 levels in 6 rows, with an indent column so the tree is readable and an is_assembly flag so leaves can be told from sub-assemblies. Per finished unit the leaves are 4 LEG, 1 PANEL and 12 SCREW, which cost 25.20 USD. The 35.00 USD of sub-assembly rows is the same material counted again at a higher level and must not be added to it.
- csvPine Assembly Workshop: Inspection characteristics and tolerancesInspection characteristics and tolerances for Pine Assembly Workshop. The 5 characteristics taken from DRW-LEG-002 revision C, with the nominal, both tolerances and the resulting limits. Four are bilateral and one, surface roughness, is unilateral with a lower tolerance of zero, so its lower limit equals its nominal and a reader that assumes symmetry computes a limit that does not exist.
- csvPine Assembly Workshop: Inspection measurements with tolerancesInspection measurements with tolerances for Pine Assembly Workshop. 25 readings, 5 samples across 5 characteristics of the LEG part. Deviation is measured less nominal and carries an explicit sign, and result is pass only when the reading lies between both limits inclusive. 2 readings fail: CH-3 sample 3 measured 25.41, above the upper limit of 25.300 by 0.110; CH-5 sample 5 measured 1.58, below the lower limit of 1.600 by 0.020.
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