Supply Chain Shipment Tracking

This dataset provides detailed, event-level tracking of global supply chain shipments, including timestamps, locations, carrier information, transport modes, and delay causes. It enables comprehensive logistics analysis, predictive ETD/ETA modeling, and root-cause identification for shipment delays, supporting supply chain optimization and transparency.

  • opened 10 times
  • last updated 22 Jul 2025
  • by GoMask
The brief that made it

End-to-end shipment visibility and real-time tracking

Sample rows

preview · 8 of 200 rows · all 26 columns
shipment_idstringmode_of_transportstringdelay_duration_hoursfloatdestination_countrystringevent_idstringevent_typestringevent_timestampdatetimelocation_namestringlocation_countrystringlocation_citystringlocation_statestringcarrier_namestringcontainer_idstringorigin_countrystringorigin_citystringdestination_citystringestimated_departure_datedateactual_departure_datedateestimated_arrival_datedateactual_arrival_datedatedelay_reasonstringshipment_statusstringgoods_descriptionstringgoods_weight_kgfloatgoods_volume_cbmfloatincotermstring
SHIP2024010001seablankUSAEVT2024010001pickup2024-01-03T09:23:00ZShanghai WarehouseChinaShanghaiShanghaiCOSCO ShippingCNU0012345AChinaShanghaiLos Angeles2024-01-042024-01-042024-01-19blankblankin_transitConsumer electronics973.55.8FOB
SHIP2024010002seablankBrazilEVT2024010002departure2024-01-10T13:30:00ZRotterdam PortNetherlandsRotterdamSouth HollandMaersk LineNLRTD89321BNetherlandsRotterdamSantos2024-01-102024-01-102024-01-29blankblankin_transitFrozen vegetables215012.4CIF
SHIP2024010003railblankPolandEVT2024010003pickup2024-01-05T07:45:00ZHamburg WarehouseGermanyHamburgHamburgDB SchenkerDEHAM83219CGermanyHamburgWarsaw2024-01-062024-01-062024-01-08blankblankin_transitAuto parts1200.47.3DAP
SHIP2024010004seablankBrazilEVT2024010004arrival2024-01-11T15:50:00ZSantos PortBrazilSantosSão PauloMaersk LineNLRTD89321BNetherlandsRotterdamSantos2024-01-102024-01-102024-01-292024-01-29blankdeliveredFrozen vegetables215012.4CIF
SHIP2024010005seablankUSAEVT2024010005customs_clearance2024-01-22T12:16:00ZPort of Los AngelesUSALos AngelesCaliforniaCOSCO ShippingCNU0012345AChinaShanghaiLos Angeles2024-01-042024-01-042024-01-19blankblankin_transitConsumer electronics973.55.8FOB
SHIP2024010006seablankUSAEVT2024010006in_transit2024-01-13T10:00:00ZPacific Ocean RouteInternational WatersblankblankCOSCO ShippingCNU0012345AChinaShanghaiLos Angeles2024-01-042024-01-042024-01-19blankblankin_transitConsumer electronics973.55.8FOB
SHIP2024010007railblankPolandEVT2024010007arrival2024-01-08T08:15:00ZWarsaw TerminalPolandWarsawMazowieckieDB SchenkerDEHAM83219CGermanyHamburgWarsaw2024-01-062024-01-062024-01-082024-01-08blankdeliveredAuto parts1200.47.3DAP
SHIP2024010008sea27.5BrazilEVT2024010008delayed2024-01-14T19:40:00ZAtlantic OceanInternational WatersblankblankMaersk LineNLRTD89321BNetherlandsRotterdamSantos2024-01-102024-01-102024-01-29blankSevere weatherdelayedFrozen vegetables215012.4CIF

What the 200 rows show

from the 200-row sample
  • 12.0median delay_duration_hours
  • 4shipment statuses
  • 8event types
  • 11incoterms
  • 36origin countries
  • 48location countries
mode_of_transport194 rows by value
0408074sea37%38air19%33rail17%49road25%
delay_duration_hours35 rows, in bands of 10
0714131262110000050100delay_duration_hours →

Median 12.0, from 2.2 to 55.2.

destination_country200 rows · top 10 of 39 values
  1. USA25
  2. Singapore12
  3. Vietnam10
  4. New Zealand9
  5. France8
  6. Germany8
  7. UAE8
  8. India8
  9. Brazil7
  10. Canada6
26 columns by typefrom the column list below
  • string 18
  • float 3
  • date 4
  • datetime 1

Columns

26 columns in three groups
blueprint · 26 columns
columntypedescriptionexample
Text 18 columns
shipment_idstringUnique identifier for each shipment in the supply chain.uniqueSHIP2024010001
event_idstringUnique identifier for each shipment event.uniqueEVT2024010001
event_typestringType of event (e.g., pickup, departure, arrival, customs_clearance, in_transit, delivered, delayed, exception).8 valuespickup
location_namestringName of the location where the event occurred (e.g., port, warehouse, city).Shanghai Warehouse
location_countrystringCountry where the event occurred.China
location_citystringCity where the event occurred.optionalShanghai
location_statestringState or province where the event occurred.optionalShanghai
carrier_namestringName of the logistics carrier responsible for this shipment segment.optionalCOSCO Shipping
mode_of_transportstringMode of transport used for this shipment segment (e.g., sea, air, rail, road).sea · air · rail · road · optionalsea
container_idstringIdentifier for the shipping container (if applicable).optionalCNU0012345A
origin_countrystringCountry where the shipment originated.China
origin_citystringCity where the shipment originated.optionalShanghai
destination_countrystringCountry where the shipment is destined.USA
destination_citystringCity where the shipment is destined.optionalLos Angeles
delay_reasonstringReason for any delay or exception (if applicable).optionalSevere weather
shipment_statusstringCurrent status of the shipment (e.g., in_transit, delivered, delayed, exception, cancelled).in_transit · delivered · delayed · exception · cancelledin_transit
goods_descriptionstringBrief description of the goods being shipped.optionalConsumer electronics
incotermstringInternational commercial term (e.g., FOB, CIF, DDP) governing the shipment.11 incoterms · optionalFOB
Numbers 3 columns
delay_duration_hoursfloatDuration of delay in hours (if applicable).0 or more · optional27.5
goods_weight_kgfloatTotal weight of the shipment in kilograms.0 or more · optional973.5
goods_volume_cbmfloatTotal volume of the shipment in cubic meters.0 or more · optional5.8
Dates and times 5 columns
event_timestampdatetimeDate and time when the event occurred.2024-01-03T09:23:00Z
estimated_departure_datedateEstimated date of departure from the origin.optional2024-01-04
actual_departure_datedateActual date of departure from the origin.optional2024-01-04
estimated_arrival_datedateEstimated date of arrival at the destination.optional2024-01-19
actual_arrival_datedateActual date of arrival at the destination.optional2024-01-29

Use it for

  • median delay d…12.035 rowsmean delay duration h…25.3sea8.8air13.0rail8.5road

    A logistics dashboard

    Delay_duration_hours by mode_of_transport and a breakdown of destination_country. Excel, Power BI or Tableau.

  • What pattern can you find in 200 rows?

    A class exercise

    Hand out the rows and one question. Everyone works from the same 200 rows.

  • A software demo

    Believable shipments with event_id, event_type and event_timestamp to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 26 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows26 columns
Yours1,000 rows26 columnslocation_name: UK only

blueprint · supply-chain-shipment-tracking

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 26 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
  • Shipments have unique tracking IDs
  • Delays attributed to customs/weather
  • Carrier performance tracked over time
  • Shipment contents affect handling requirements
  • Multi-modal transit paths included
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
supply-chain-shipment-tracking

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