Supply Chain Disruption Patterns
This dataset provides detailed synthetic records of supply chain disruption events, including node information, disruption types, impact severity, financial losses, and mitigation actions. It is designed to support resilience analytics, risk mitigation planning, and supply chain optimization for organizations seeking to understand and manage supply chain vulnerabilities.
Sample rows
preview · 8 of 200 rows · all 18 columns| disruption_idstring | node_typestring | estimated_loss_usdfloat | resolvedboolean | mitigation_actionstring | node_idstring | node_namestring | node_countrystring | node_citystring | disruption_typestring | disruption_start_datedate | disruption_end_datedate | duration_daysfloat | impact_severityinteger | affected_productsstring | reported_bystring | report_datedatetime | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| D-00001 | supplier | 320000 | true | alternate supplier | SUP-001 | Global Steel Supplies | China | Shanghai | natural_disaster | 2023-02-11 | 2023-02-22 | 11 | 5 | RM-STEEL-12,RM-IRON-04,RM-ALUM-03 | sysops23 | 2023-02-11T08:16:01 | Factory flooded after heavy rainfall, shipments delayed. |
| D-00002 | manufacturer | 15500 | true | on-site repair | MF-002 | Prime Auto Fabricators | Germany | Stuttgart | equipment_failure | 2023-03-06 | 2023-03-07 | 1 | 2 | CAR-AXLE-21 | j.keller | 2023-03-06T09:45:12 | Press malfunction halted axle production for 8 hours. |
| D-00003 | warehouse | 87000 | false | blank | WH-003 | West Coast Storage Hub | USA | Los Angeles | labor_strike | 2023-04-10 | blank | blank | 4 | ELEC-GEN-09,ELEC-CAP-02 | admin12 | 2023-04-10T07:22:49 | Strike ongoing, warehouse operations interrupted. |
| D-00004 | distribution_center | 6400 | true | rerouting | DC-004 | Amazonia DC | Brazil | Manaus | transportation_delay | 2023-05-01 | 2023-05-05 | 4 | 3 | FRT-RICE-88 | logistics_team | 2023-05-01T11:02:10 | River port strike caused delayed shipments. |
| D-00005 | retailer | 3200 | true | system patch | RET-005 | QuickMart Central | Canada | Toronto | cyber_attack | 2023-06-14 | 2023-06-15 | 1 | 3 | POS-1002,POS-1004 | secmon | 2023-06-14T13:22:36 | Ransomware attack disabled checkout terminals. |
| D-00006 | supplier | 198000 | true | inventory buffer | SUP-006 | Norway Timber Exports | Norway | Bergen | natural_disaster | 2023-01-17 | 2023-01-27 | 10 | 4 | RM-TMBR-07,RM-PLYWD-15 | r.svensen | 2023-01-17T10:36:03 | Heavy snowstorm blocked access to timber yard. |
| D-00007 | manufacturer | 0 | true | on-site repair | MF-007 | Estonia CircuitWorks | Estonia | Tallinn | equipment_failure | 2023-03-14 | 2023-03-14 | 0 | 2 | CHIP-344 | m.kask | 2023-03-14T08:01:50 | Quick fix required for soldering station. |
| D-00008 | warehouse | 17000 | true | expedited transport | WH-008 | Madrid Goods Storage | Spain | Madrid | labor_strike | 2023-07-03 | 2023-07-09 | 6 | 3 | FRT-TOM-34 | s.valencia | 2023-07-03T10:47:29 | Union protest halted warehouse loading temporarily. |
| D-00009 | distribution_center | 9500 | false | blank | DC-009 | UAE Regional DC | UAE | Dubai | transportation_delay | 2023-08-19 | blank | blank | 3 | FRT-DATES-27 | logistics_uae | 2023-08-19T07:13:41 | Holiday congestion delaying inbound shipments. |
| D-00010 | retailer | 32000 | true | alternate supplier | RET-010 | Estrella Supermarket | Mexico | Guadalajara | labor_strike | 2023-09-02 | 2023-09-12 | 10 | 4 | FRT-AVOC-11,FRT-LIME-77 | j.moreno | 2023-09-02T09:02:56 | Retail staff walkout impacted produce sections. |
| D-00011 | supplier | 440000 | false | blank | SUP-011 | Sydney Chemical Co. | Australia | Sydney | natural_disaster | 2023-10-20 | blank | blank | 5 | RM-CHM-05,RM-CHM-22 | syschem | 2023-10-20T06:15:27 | Flooding disrupted chemical production lines. |
| D-00012 | manufacturer | 28000 | true | inventory buffer | MF-012 | Poland Plastics Group | Poland | Warsaw | equipment_failure | 2023-11-13 | 2023-11-14 | 1 | 3 | PLST-BOTT-03,PLST-CAP-08 | opspoland | 2023-11-13T08:13:09 | Injection molder breakdown, short-term production loss. |
| D-00013 | warehouse | 110000 | true | expedited transport | WH-013 | Mumbai Cold Storage | India | Mumbai | labor_strike | 2022-12-01 | 2022-12-13 | 12 | 5 | FRT-MANGO-56,FRT-BANANA-44 | s.storage | 2022-12-01T10:21:15 | Prolonged strike reduced cold storage operations. |
| D-00014 | distribution_center | 5400 | true | rerouting | DC-014 | UAE Express Center | UAE | Abu Dhabi | transportation_delay | 2022-11-27 | 2022-11-29 | 2 | 2 | COSM-LIP-02 | dispatch_abu | 2022-11-27T07:44:55 | Truck breakdown delayed express delivery. |
| D-00015 | retailer | 1250 | true | manual override | RET-015 | FashionLine Paris | France | Paris | cyber_attack | 2022-10-16 | 2022-10-17 | 1 | 2 | POS-2106 | it.france | 2022-10-16T13:34:40 | POS system slowed by malware, resolved quickly. |
| D-00016 | supplier | 270000 | true | alternate supplier | SUP-016 | Estonian Minerals Ltd. | Estonia | Narva | natural_disaster | 2022-08-22 | 2022-09-02 | 11 | 4 | RM-SAND-14,RM-LIME-35,RM-CLAY-21 | miningops | 2022-08-22T08:03:27 | Flood rendered mine access roads impassable. |
| D-00017 | manufacturer | 0 | true | on-site repair | MF-017 | UAE Industrial Plastics | UAE | Sharjah | equipment_failure | 2022-07-09 | 2022-07-09 | 0 | 2 | PLST-PACK-17 | prod_uae | 2022-07-09T09:07:11 | Short cooling unit outage, minimal disruption. |
| D-00018 | warehouse | 38000 | false | blank | WH-018 | Polska Food Depot | Poland | Gdansk | labor_strike | 2022-06-15 | blank | blank | 4 | FRT-APPLE-17,FRT-PEAR-33 | gdansk_mgr | 2022-06-15T10:12:05 | Union walkout, ongoing negotiations. |
| D-00019 | distribution_center | 8300 | true | expedited transport | DC-019 | Houston Freight DC | USA | Houston | transportation_delay | 2022-05-21 | 2022-05-26 | 5 | 3 | FRT-CORN-19 | hou_dc | 2022-05-21T11:20:33 | Holiday congestion at port delayed shipments. |
| D-00020 | retailer | 9200 | true | reboot | RET-020 | SuperMart Sweden | Sweden | Stockholm | cyber_attack | 2022-04-07 | 2022-04-08 | 1 | 4 | POS-5120,POS-5108,POS-5105 | sw_itops | 2022-04-07T14:09:42 | POS outage due to targeted ransomware. |
What the 200 rows show
from the 200-row sampleSupplier (node type) stands out: mean estimated_
- 84%resolved = true
- 10,750median estimated_
loss_ usd - 6disruption types
- 17node countries
- 3median duration_
days - 3median impact_
severity
Median 10,750, from 0 to 1,040,000.
- string 11
- integer 1
- float 2
- date 2
- datetime 1
- boolean 1
Columns
18 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 11 columns | |||
disruption_id | string | Unique identifier for each supply chain disruption eventunique | D-00001 |
node_id | string | Unique identifier for the supply chain node affected by the disruption | SUP-001 |
node_type | string | Type of supply chain node (e.g., supplier, manufacturer, warehouse, distribution center, retailer)5 values | supplier |
node_name | string | Name of the supply chain node | Global Steel Supplies |
node_country | string | Country where the supply chain node is located | China |
node_city | string | City where the supply chain node is locatedoptional | Shanghai |
disruption_type | string | Type of disruption (e.g., natural disaster, labor strike, cyber attack, equipment failure, transportation delay)6 values | natural_disaster |
affected_products | string | Comma-separated list of product SKUs or names impacted by the disruptionoptional | CAR-AXLE-21 |
mitigation_action | string | Primary mitigation action taken (e.g., rerouting, alternate supplier, inventory buffer)8 actions · optional | alternate supplier |
reported_by | string | Name or identifier of the person or system reporting the disruptionoptional | sysops23 |
notes | string | Additional notes or comments about the disruption eventoptional | Factory flooded after hea… |
| Numbers 3 columns | |||
duration_days | float | Duration of the disruption in days0 or more · optional | 11 |
impact_severity | integer | Severity of the disruption on a scale from 1 (low) to 5 (high)1 to 5 | 5 |
estimated_loss_usd | float | Estimated financial loss due to the disruption in USD0 or more · optional | 320000 |
| Dates and times 3 columns | |||
disruption_start_date | date | Date when the disruption started | 2023-02-11 |
disruption_end_date | date | Date when the disruption endedoptional | 2023-02-22 |
report_date | datetime | Timestamp when the disruption was reported | 2023-02-11T08:16:01 |
| True or false 1 column | |||
resolved | boolean | Indicates whether the disruption has been resolved | true |
Use it for
A logistics dashboard
The resolved rate, estimated_
loss_ usd by node_ type and a breakdown of mitigation_ action. Excel, Power BI or Tableau. Why do the 43 supplier rows have a mean estimated_
loss_ usd of 398,337? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Disruptions200D-00001320000supplierD-0000215500manufact…D-0000387000warehouse
A software demo
Believable disruptions with node_
id, node_ type and node_ name to fill a screen in front of a buyer.
This dataset200 rows18 columns
Yours1,000 rows18 columnsnode_country: UK only
blueprint · supply-chain-disruption-patterns
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 18 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Disruption type and cause logged
- Duration of impact recorded
- Affected product categories tagged
- Recovery actions documented
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- supply-chain-disruption-patterns