Modifiche

              
    

          
          
        
        

            
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{
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{
            
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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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  "cloud_storage_key_segment": "manufacturing-operations-dataset",
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  "cloud_storage_key_segment": "manufacturing-operations-dataset",
            
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  "creator_user_id": "06d04fb0-fe0e-4a6a-b958-061fb38a9f9c",
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  "creator_user_id": "06d04fb0-fe0e-4a6a-b958-061fb38a9f9c",
            
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  "groups": [],
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  "groups": [],
            
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  "id": "8ddb1432-fa3a-4173-a46c-915fe230e08e",
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  "id": "8ddb1432-fa3a-4173-a46c-915fe230e08e",
            
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  "isopen": false,
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  "isopen": false,
            
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  "license_id": "other-nc",
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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_created": "2026-02-20T17:42:37.166534",
            
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  "metadata_modified": "2026-02-20T17:48:01.004764",
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  "metadata_modified": "2026-02-20T17:48:01.004764",
            
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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 
            
19
(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**, 
            
21
**stoppage/downtime**, and **order due-date performance**.\r\n\r\n## 
21
**stoppage/downtime**, and **order due-date performance**.\r\n\r\n## 
            
22
**Files Summary**\r\n\r\n| File | Rows | Columns | Approx Size (MB) 
22
**Files Summary**\r\n\r\n| File | Rows | Columns | Approx Size (MB) 
            
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|\r\n| :---- | :---- | :---- | :---- |\r\n| 
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|\r\n| :---- | :---- | :---- | :---- |\r\n| 
            
24
df\\_unit\\_level\\_cycle\\_downtime\\_anonymised.csv | 165,384 | 6 | 
24
df\\_unit\\_level\\_cycle\\_downtime\\_anonymised.csv | 165,384 | 6 | 
            
25
6.34 |\r\n| order\\_analysis\\_anonymised.csv | 53,164 | 9 | 3.83 
25
6.34 |\r\n| order\\_analysis\\_anonymised.csv | 53,164 | 9 | 3.83 
            
26
|\r\n| product\\_cycle\\_anonymised.csv | 952,730 | 17 | 156.75 |\r\n| 
26
|\r\n| product\\_cycle\\_anonymised.csv | 952,730 | 17 | 156.75 |\r\n| 
            
27
stoppage\\_all\\_anonymised.csv | 10,891 | 14 | 1.93 |\r\n\r\n## 
27
stoppage\\_all\\_anonymised.csv | 10,891 | 14 | 1.93 |\r\n\r\n## 
            
28
**Anonymisation & Standards**\r\n\r\n* **Anonymisation:** Company 
28
**Anonymisation & Standards**\r\n\r\n* **Anonymisation:** Company 
            
29
names are replaced with Company\\_XX. Identifiers such as orderId, 
29
names are replaced with Company\\_XX. Identifiers such as orderId, 
            
30
unitId, stockId, operationId, eventId, and stoppage id are anonymised 
30
unitId, stockId, operationId, eventId, and stoppage id are anonymised 
            
31
tokens. No personally identifying information (PII) is present.  \r\n* 
31
tokens. No personally identifying information (PII) is present.  \r\n* 
            
32
**Timezones:** Timestamp fields include a UTC offset (e.g., \\+00:00). 
32
**Timezones:** Timestamp fields include a UTC offset (e.g., \\+00:00). 
            
33
When loading into Pandas, it is recommended to use: 
33
When loading into Pandas, it is recommended to use: 
            
34
pd.to\\_datetime(col, utc=True, errors='coerce').  \r\n* **Negative 
34
pd.to\\_datetime(col, utc=True, errors='coerce').  \r\n* **Negative 
            
35
Values:** Some duration fields contain negative values due to system 
35
Values:** Some duration fields contain negative values due to system 
            
36
clock issues. These should be filtered or investigated during the 
36
clock issues. These should be filtered or investigated during the 
            
37
cleaning phase.\r\n\r\n\r\n## **Data Quality Notes**\r\n\r\n* 
37
cleaning phase.\r\n\r\n\r\n## **Data Quality Notes**\r\n\r\n* 
            
38
**Missing Data:** Many records in 
38
**Missing Data:** Many records in 
            
39
df\\_unit\\_level\\_cycle\\_downtime\\_anonymised.csv have nulls for 
39
df\\_unit\\_level\\_cycle\\_downtime\\_anonymised.csv have nulls for 
            
40
downtime. This typically indicates zero downtime events occurred for 
40
downtime. This typically indicates zero downtime events occurred for 
            
41
that unit.  \r\n* **Open Orders:** A high percentage (81%) of orders 
41
that unit.  \r\n* **Open Orders:** A high percentage (81%) of orders 
            
42
are currently open. Analysis of these should focus on 
42
are currently open. Analysis of these should focus on 
            
43
open\\_days\\_late rather than completedDate.  \r\n* **Negatives:** 
43
open\\_days\\_late rather than completedDate.  \r\n* **Negatives:** 
            
44
Negative durations in product\\_cycle\\_anonymised.csv (specifically 
44
Negative durations in product\\_cycle\\_anonymised.csv (specifically 
            
45
in actual duration columns) should be treated as data quality 
45
in actual duration columns) should be treated as data quality 
            
46
errors.\r\n\r\n**Provenance:** Please record the extract date, source 
46
errors.\r\n\r\n**Provenance:** Please record the extract date, source 
            
47
system version, and any applied filters (date range, plant, or 
47
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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  "organization": {
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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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 
54
    "description": "Ulster University has a sustained track record in 
            
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robotics, computer vision, and computational intelligence. Translating 
55
robotics, computer vision, and computational intelligence. Translating 
            
56
research into technologies that catalyse innovation in small and large 
56
research into technologies that catalyse innovation in small and large 
            
57
enterprises, we have been instrumental in attracting inward investment 
57
enterprises, we have been instrumental in attracting inward investment 
            
58
and developing the knowledge economy. Ulster lead SMDH, lead the 
58
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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    "image_url": "2023-05-02-104737.949700uu.png",
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    "image_url": "2023-05-02-104737.949700uu.png",
            
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    "is_organization": true,
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    "is_organization": true,
            
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    "name": "ulster-university",
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    "name": "ulster-university",
            
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    "state": "active",
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    "state": "active",
            
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    "title": "Ulster University",
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    "title": "Ulster University",
            
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    "type": "organization"
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    "type": "organization"
            
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