Supply Chain Disruption Simulations
This dataset provides detailed synthetic scenarios of supply chain disruptions, including impacts on logistics network nodes, delivery times, inventory levels, rerouting events, and financial consequences. It enables robust analysis and modeling of supply chain resilience strategies for risk mitigation and operational planning.
Sample rows
preview · 8 of 200 rows · all 18 columns| scenario_idstring | disruption_typestring | inventory_level_afterfloat | rerouting_event_occurredboolean | network_node_typestring | scenario_namestring | disruption_start_datedate | disruption_end_datedate | affected_regionstring | network_node_idstring | inventory_level_beforefloat | average_delivery_time_beforefloat | average_delivery_time_afterfloat | financial_impactfloat | resilience_scorefloat | created_atdatetime | scenario_descriptionstring | rerouting_strategystring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SCN001 | natural_disaster | 7921 | true | supplier | Flooding at Supplier Hub | 2023-03-14 | 2023-03-28 | Mississippi Delta | SUP-MSD-001 | 27432 | 21.2 | 81.7 | 2084500 | 0.22 | 2023-03-01T10:31:12 | Severe flooding impacted the main supplier hub, halting inbound shipments and disrupting regional supply chains. | Alternate supplier sourcing from Tennessee region |
| SCN002 | equipment_failure | 14890 | false | warehouse | Equipment Malfunction Warehouse East | 2022-10-05 | 2022-10-07 | Newark, NJ | WARE-NWK-002 | 15320 | 13.6 | 17.8 | 37000 | 0.98 | 2022-09-28T15:22:17 | Unexpected conveyor belt failure at the eastern warehouse caused minor delays in outbound shipments. | blank |
| SCN003 | labor_strike | 272230 | true | port | Port Labor Strike - Los Angeles | 2023-07-11 | 2023-07-21 | Los Angeles, CA | PORT-LA-003 | 286540 | 32.5 | 64.3 | 1080000 | 0.43 | 2023-07-01T08:05:32 | A labor strike at the Los Angeles port led to long delays and rerouting of maritime shipments. | Temporary rerouting to Seattle port with expedited handling |
| SCN004 | cyber_attack | 6542 | true | retailer | Cyber Attack on Retail Node | 2023-11-02 | 2023-11-09 | Houston, TX | RTL-HOU-004 | 9811 | 8.2 | 41.3 | 2140000 | 0.11 | 2023-10-27T18:44:09 | Retail operations were disrupted by ransomware, severely impacting order fulfillment and inventory management. | Manual processing and use of expedited third-party shippers |
| SCN005 | transport_delay | 563850 | false | distribution_center | Road Closure Delays Distribution Center | 2022-08-16 | 2022-08-19 | Chicago, IL | DC-CHI-005 | 581200 | 18.9 | 25.6 | 78000 | 0.85 | 2022-08-11T09:22:33 | Major highway closure led to delays at the Chicago distribution center, impacting regional deliveries. | blank |
| SCN006 | natural_disaster | 12045 | true | supplier | Supplier Region Tornado | 2023-04-04 | 2023-04-20 | Little Rock, AR | SUP-LRK-006 | 31800 | 25.3 | 98.1 | 3125000 | 0.18 | 2023-03-28T14:13:45 | A tornado swept through the Arkansas supplier region, halting production and causing severe shortages. | Emergency sourcing from Oklahoma facilities |
| SCN007 | equipment_failure | 473680 | false | distribution_center | Distribution Center Power Outage | 2022-12-01 | 2022-12-02 | Denver, CO | DC-DEN-007 | 480930 | 19.4 | 25.1 | 14500 | 0.99 | 2022-11-25T11:35:01 | Power outage at Denver DC temporarily delayed outbound shipments to retailers. | blank |
| SCN008 | cyber_attack | 6045 | true | retailer | Retailer Payment System Breach | 2023-08-15 | 2023-08-22 | Brooklyn, NY | RTL-BKN-008 | 10041 | 6.7 | 65.2 | 4700000 | 0.07 | 2023-08-10T14:39:20 | Cyber breach at a New York retailer delayed order processing and required manual intervention. | Manual fulfillment and delivery via alternate carriers |
| SCN009 | natural_disaster | 13350 | true | supplier | Supplier Fire Damage | 2022-11-12 | 2022-11-30 | Fresno, CA | SUP-FRE-009 | 29864 | 19.8 | 72.4 | 1438000 | 0.27 | 2022-11-01T08:14:55 | A fire at the primary supplier resulted in inventory shortages and rerouting to secondary suppliers. | Procurement from Phoenix supplier network |
| SCN010 | transport_delay | 223980 | false | port | East Coast Port Fog | 2023-01-13 | 2023-01-14 | Baltimore, MD | PORT-BAL-010 | 225100 | 20.3 | 23.1 | 12000 | 0.97 | 2023-01-08T09:51:36 | Heavy fog delayed operations at the Baltimore port, causing minor shipment delays. | blank |
| SCN011 | labor_strike | 181480 | true | distribution_center | Distribution Center Worker Protest | 2022-06-07 | 2022-06-15 | Miami, FL | DC-MIA-011 | 185600 | 14.7 | 39.2 | 650000 | 0.38 | 2022-05-29T12:16:27 | Worker protest at Miami DC caused delays and required alternate labor arrangements. | Temporary labor and alternate DC support |
| SCN012 | natural_disaster | 11580 | true | supplier | Supplier Drought Impact | 2023-06-04 | 2023-06-29 | Amarillo, TX | SUP-AMR-012 | 37200 | 29.6 | 96.3 | 4378000 | 0.16 | 2023-05-29T08:13:14 | Extended drought in the Texas supplier region caused inventory depletion and major delays. | Alternate supplier in Oklahoma region |
| SCN013 | equipment_failure | 7600 | false | retailer | Retailer Blackout - Seattle | 2022-09-17 | 2022-09-18 | Seattle, WA | RTL-SEA-013 | 8021 | 9.1 | 13.5 | 6000 | 0.97 | 2022-09-10T15:17:18 | Retail blackout in Seattle due to grid failure led to manual processing and shipment rerouting. | blank |
| SCN014 | other | 201800 | false | distribution_center | Distribution Center Temporary Closure | 2022-11-25 | 2022-11-26 | San Diego, CA | DC-SDG-014 | 205000 | 16.2 | 18.7 | 900 | 0.99 | 2022-11-20T06:32:45 | Temporary closure of San Diego DC for maintenance led to minor supply chain delays. | blank |
| SCN015 | transport_delay | 192230 | false | port | Port Customs Slowdown | 2022-05-19 | 2022-05-22 | Savannah, GA | PORT-SAV-015 | 196450 | 16.9 | 22.5 | 28000 | 0.91 | 2022-05-14T10:47:22 | Customs processing slowdown at the Savannah port led to moderate delays and limited rerouting. | blank |
| SCN016 | natural_disaster | 3400 | true | supplier | Supplier Flooding - Memphis | 2022-03-03 | 2022-03-20 | Memphis, TN | SUP-MEM-016 | 21400 | 32.5 | 82.4 | 1548000 | 0.23 | 2022-02-22T14:22:09 | Flooding in the Memphis supplier region caused substantial inventory losses and delays. | Emergency procurement from Dallas region |
| SCN017 | labor_strike | 488150 | true | distribution_center | Distribution Center Labor Dispute | 2023-09-08 | 2023-09-19 | Atlanta, GA | DC-ATL-017 | 498000 | 23.1 | 56.3 | 920000 | 0.41 | 2023-08-30T09:01:19 | Labor dispute at Atlanta DC led to significant delays and alternate labor arrangements. | Temporary labor contracts and alternate DC support |
| SCN018 | equipment_failure | 8900 | false | retailer | Retailer Outage - Dallas | 2023-06-14 | 2023-06-15 | Dallas, TX | RTL-DAL-018 | 9080 | 7.7 | 10.8 | 5200 | 0.99 | 2023-06-08T11:42:13 | Retail outage in Dallas caused order processing delays due to technical issues. | blank |
| SCN019 | natural_disaster | 8400 | true | supplier | Supplier Heatwave Effect | 2022-07-22 | 2022-08-01 | Las Vegas, NV | SUP-LV-019 | 22800 | 18.7 | 57.9 | 1090000 | 0.29 | 2022-07-16T16:22:29 | A prolonged heatwave in Nevada disrupted supplier operations and logistics. | Alternate supplier arrangement with Salt Lake City |
| SCN020 | equipment_failure | 7550 | false | retailer | Retailer Systems Outage | 2023-02-16 | 2023-02-17 | Minneapolis, MN | RTL-MPL-020 | 7730 | 7.8 | 10.6 | 4800 | 0.99 | 2023-02-10T09:32:07 | System outage at Minneapolis retailer caused delays in customer delivery times. | blank |
What the 200 rows show
from the 200-row sampleLabor_
- 62%rerouting_
event_ occurred = true - 13,415median inventory_
level_ after - 30,832median inventory_
level_ before - 16.8median average_
delivery_ time_ before - 35.3median average_
delivery_ time_ after - 520,000median financial_
impact
Median 13,415, from 0.0 to 563,850.
- string 8
- float 6
- date 2
- datetime 1
- boolean 1
Columns
18 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
scenario_id | string | Unique identifier for each disruption simulation scenariounique | SCN001 |
scenario_name | string | Descriptive name of the disruption scenario | Flooding at Supplier Hub |
scenario_description | string | Detailed description of the disruption scenario, including context and objectivesoptional | Severe flooding impacted … |
disruption_type | string | Type of disruption (e.g., natural disaster, labor strike, equipment failure)6 values | natural_disaster |
affected_region | string | Geographic region or location impacted by the disruption | Mississippi Delta |
network_node_id | string | Identifier for the affected logistics network node (e.g., warehouse, port, distribution center) | SUP-MSD-001 |
network_node_type | string | Type of logistics network node (e.g., warehouse, port, distribution_center, supplier)6 values | supplier |
rerouting_strategy | string | Description of rerouting strategy used (e.g., alternate route, expedited shipping, supplier change)optional | Alternate supplier sourci… |
| Numbers 6 columns | |||
inventory_level_before | float | Inventory level at the network node before the disruption (in units)0 or more | 27432 |
inventory_level_after | float | Inventory level at the network node after the disruption (in units)0 or more | 7921 |
average_delivery_time_before | float | Average delivery time to customers before the disruption (in hours)0 or more | 21.2 |
average_delivery_time_after | float | Average delivery time to customers after the disruption (in hours)0 or more | 81.7 |
financial_impact | float | Estimated financial impact of the disruption (in USD)0 or more · optional | 2084500 |
resilience_score | float | Calculated resilience score for the scenario (0-1 scale, higher is better)0 to 1 · optional | 0.22 |
| Dates and times 3 columns | |||
disruption_start_date | date | Date when the disruption begins | 2023-03-14 |
disruption_end_date | date | Date when the disruption endsoptional | 2023-03-28 |
created_at | datetime | Timestamp when the simulation scenario was created | 2023-03-01T10:31:12 |
| True or false 1 column | |||
rerouting_event_occurred | boolean | Indicates if rerouting was required due to the disruption | true |
Use it for
A logistics dashboard
The rerouting_
event_ occurred rate, inventory_ level_ after by disruption_ type and a breakdown of network_ node_ type. Excel, Power BI or Tableau. Why do the 24 labor_
strike rows have a mean inventory_ level_ after of 244,441? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Scenarios200SCN0017921natural_…SCN00214890equipmen…SCN003272230labor_st…
A software demo
Believable scenarios with scenario_
name, scenario_ description and disruption_ type to fill a screen in front of a buyer.
This dataset200 rows18 columns
Yours1,000 rows18 columnsaffected_region: UK only
blueprint · supply-chain-disruption-simulations
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
- Region, route, and carrier specified
- Disruption type and duration detailed
- Inventory delays calculated
- Reroute events and affected shipments logged
- Seasonal spikes incorporated
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-simulations