Perubahan

              
    

          
          
        
        

            
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  "author": "SMDH Data Science Team",
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  "author": "SMDH Data Science Team",
            
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  "author_email": "info@smdh.uk",
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  "author_email": "info@smdh.uk",
            
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  "license_id": "other-nc",
            
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  "license_title": "Other (Non-Commercial)",
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  "license_title": "Other (Non-Commercial)",
            
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  "maintainer": "Dermot Kerr",
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  "maintainer": "Dermot Kerr",
            
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  "maintainer_email": "d.kerr@smdh.uk",
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  "maintainer_email": "d.kerr@smdh.uk",
            
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  "metadata_created": "2026-02-20T17:42:37.166534",
            
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  "metadata_modified": "2026-02-20T17:48:52.748695",
            
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  "name": "ulster-university--manufacturing-operations-dataset",
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  "name": "ulster-university--manufacturing-operations-dataset",
            
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  "notes": "# **Manufacturing Operations Dataset 
16
  "notes": "# **Manufacturing Operations Dataset 
            
17
Documentation**\r\n\r\n## **Overview**\r\n\r\nThis package contains 
17
Documentation**\r\n\r\n## **Overview**\r\n\r\nThis package contains 
            
18
anonymised manufacturing operations datasets extracted from an MES 
18
anonymised manufacturing operations datasets extracted from an MES 
            
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(Manufacturing Execution System). These files support the analysis of 
19
(Manufacturing Execution System). These files support the analysis of 
            
20
**cycle times**, **routing/operation durations**, 
20
**cycle times**, **routing/operation durations**, 
            
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**stoppage/downtime**, and **order due-date performance**.\r\n\r\n## 
21
**stoppage/downtime**, and **order due-date performance**.\r\n\r\n## 
            
22
**Anonymisation & Standards**\r\n\r\n* **Anonymisation:** Company 
22
**Anonymisation & Standards**\r\n\r\n* **Anonymisation:** Company 
            
23
names are replaced with Company\\_XX. Identifiers such as orderId, 
23
names are replaced with Company\\_XX. Identifiers such as orderId, 
            
24
unitId, stockId, operationId, eventId, and stoppage id are anonymised 
24
unitId, stockId, operationId, eventId, and stoppage id are anonymised 
            
25
tokens. No personally identifying information (PII) is present.  \r\n* 
25
tokens. No personally identifying information (PII) is present.  \r\n* 
            
26
**Timezones:** Timestamp fields include a UTC offset (e.g., \\+00:00). 
26
**Timezones:** Timestamp fields include a UTC offset (e.g., \\+00:00). 
            
27
When loading into Pandas, it is recommended to use: 
27
When loading into Pandas, it is recommended to use: 
            
28
pd.to\\_datetime(col, utc=True, errors='coerce').  \r\n* **Negative 
28
pd.to\\_datetime(col, utc=True, errors='coerce').  \r\n* **Negative 
            
29
Values:** Some duration fields contain negative values due to system 
29
Values:** Some duration fields contain negative values due to system 
            
30
clock issues. These should be filtered or investigated during the 
30
clock issues. These should be filtered or investigated during the 
            
31
cleaning phase.\r\n\r\n\r\n## **Data Quality Notes**\r\n\r\n* 
31
cleaning phase.\r\n\r\n\r\n## **Data Quality Notes**\r\n\r\n* 
            
32
**Missing Data:** Many records in 
32
**Missing Data:** Many records in 
            
33
df\\_unit\\_level\\_cycle\\_downtime\\_anonymised.csv have nulls for 
33
df\\_unit\\_level\\_cycle\\_downtime\\_anonymised.csv have nulls for 
            
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downtime. This typically indicates zero downtime events occurred for 
34
downtime. This typically indicates zero downtime events occurred for 
            
35
that unit.  \r\n* **Open Orders:** A high percentage (81%) of orders 
35
that unit.  \r\n* **Open Orders:** A high percentage (81%) of orders 
            
36
are currently open. Analysis of these should focus on 
36
are currently open. Analysis of these should focus on 
            
37
open\\_days\\_late rather than completedDate.  \r\n* **Negatives:** 
37
open\\_days\\_late rather than completedDate.  \r\n* **Negatives:** 
            
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Negative durations in product\\_cycle\\_anonymised.csv (specifically 
38
Negative durations in product\\_cycle\\_anonymised.csv (specifically 
            
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in actual duration columns) should be treated as data quality 
39
in actual duration columns) should be treated as data quality 
            
40
errors.\r\n\r\n**Provenance:** Please record the extract date, source 
40
errors.\r\n\r\n**Provenance:** Please record the extract date, source 
            
41
system version, and any applied filters (date range, plant, or 
41
system version, and any applied filters (date range, plant, or 
            
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operation types) externally to ensure benchmarking is reproducible.",
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operation types) externally to ensure benchmarking is reproducible.",
            
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    "created": "2023-03-20T14:27:38.943709",
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    "created": "2023-03-20T14:27:38.943709",
            
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    "description": "Ulster University has a sustained track record in 
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    "description": "Ulster University has a sustained track record in 
            
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robotics, computer vision, and computational intelligence. Translating 
49
robotics, computer vision, and computational intelligence. Translating 
            
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research into technologies that catalyse innovation in small and large 
50
research into technologies that catalyse innovation in small and large 
            
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enterprises, we have been instrumental in attracting inward investment 
51
enterprises, we have been instrumental in attracting inward investment 
            
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and developing the knowledge economy. Ulster lead SMDH, lead the 
52
and developing the knowledge economy. Ulster lead SMDH, lead the 
            
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virtual manufacturing platform and contribute to data science, 
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virtual manufacturing platform and contribute to data science, 
            
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roll-out and marketing.",
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roll-out and marketing.",
            
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    "title": "Ulster University",
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    "title": "Ulster University",
            
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