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On 20 Februari 2026 17.48.53 UTC, Gravatar Dermot Kerr:

  • Updated description of Manufacturing Operations Dataset from

    # **Manufacturing Operations Dataset Documentation** ## **Overview** This package contains anonymised manufacturing operations datasets extracted from an MES (Manufacturing Execution System). These files support the analysis of **cycle times**, **routing/operation durations**, **stoppage/downtime**, and **order due-date performance**. ## **Files Summary** | File | Rows | Columns | Approx Size (MB) | | :---- | :---- | :---- | :---- | | df\_unit\_level\_cycle\_downtime\_anonymised.csv | 165,384 | 6 | 6.34 | | order\_analysis\_anonymised.csv | 53,164 | 9 | 3.83 | | product\_cycle\_anonymised.csv | 952,730 | 17 | 156.75 | | stoppage\_all\_anonymised.csv | 10,891 | 14 | 1.93 | ## **Anonymisation & Standards** * **Anonymisation:** Company names are replaced with Company\_XX. Identifiers such as orderId, unitId, stockId, operationId, eventId, and stoppage id are anonymised tokens. No personally identifying information (PII) is present. * **Timezones:** Timestamp fields include a UTC offset (e.g., \+00:00). When loading into Pandas, it is recommended to use: pd.to\_datetime(col, utc=True, errors='coerce'). * **Negative Values:** Some duration fields contain negative values due to system clock issues. These should be filtered or investigated during the cleaning phase. ## **Data Quality Notes** * **Missing Data:** Many records in df\_unit\_level\_cycle\_downtime\_anonymised.csv have nulls for downtime. This typically indicates zero downtime events occurred for that unit. * **Open Orders:** A high percentage (81%) of orders are currently open. Analysis of these should focus on open\_days\_late rather than completedDate. * **Negatives:** Negative durations in product\_cycle\_anonymised.csv (specifically in actual duration columns) should be treated as data quality errors. **Provenance:** Please record the extract date, source system version, and any applied filters (date range, plant, or operation types) externally to ensure benchmarking is reproducible.
    to
    # **Manufacturing Operations Dataset Documentation** ## **Overview** This package contains anonymised manufacturing operations datasets extracted from an MES (Manufacturing Execution System). These files support the analysis of **cycle times**, **routing/operation durations**, **stoppage/downtime**, and **order due-date performance**. ## **Anonymisation & Standards** * **Anonymisation:** Company names are replaced with Company\_XX. Identifiers such as orderId, unitId, stockId, operationId, eventId, and stoppage id are anonymised tokens. No personally identifying information (PII) is present. * **Timezones:** Timestamp fields include a UTC offset (e.g., \+00:00). When loading into Pandas, it is recommended to use: pd.to\_datetime(col, utc=True, errors='coerce'). * **Negative Values:** Some duration fields contain negative values due to system clock issues. These should be filtered or investigated during the cleaning phase. ## **Data Quality Notes** * **Missing Data:** Many records in df\_unit\_level\_cycle\_downtime\_anonymised.csv have nulls for downtime. This typically indicates zero downtime events occurred for that unit. * **Open Orders:** A high percentage (81%) of orders are currently open. Analysis of these should focus on open\_days\_late rather than completedDate. * **Negatives:** Negative durations in product\_cycle\_anonymised.csv (specifically in actual duration columns) should be treated as data quality errors. **Provenance:** Please record the extract date, source system version, and any applied filters (date range, plant, or operation types) externally to ensure benchmarking is reproducible.