Logistics Exception Event Tracking

This dataset provides detailed tracking of logistics exception events such as shipment delays, damages, losses, and misroutes, including event specifics, location, customer impact, and resolution status. It enables comprehensive analysis to identify process bottlenecks, improve operational reliability, and enhance customer satisfaction in supply chain management.

  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Root cause analysis of logistics disruptions

Sample rows

preview · 8 of 200 rows · all 18 columns
exception_event_idstringdamage_severitystringaffected_items_countintegerlocation_statestringshipment_idstringevent_typestringevent_datetimedatetimereported_bystringlocation_citystringlocation_countrystringdelay_duration_minutesintegerresolution_statusstringresolution_datetimedatetimecarrier_namestringcustomer_idstringevent_descriptionstringcustomer_impactstringresolution_notesstring
EXC0001blank25ILSHP10001delay2024-05-14T09:23:00Zsystem_autoChicagoUS320resolved2024-05-15T13:58:00ZMidwest LogisticsCUS1001Shipment delayed due to severe weather conditions, impacting delivery timelines.Customer notified; delivery rescheduled, minor inconvenience reported.Shipment rerouted and expedited for delivery, customer compensated.
EXC0002moderate11ONSHP10002damage2024-04-27T15:44:00ZJames LiTorontoCAblankresolved2024-04-28T10:16:00ZNorthern FreightCUS1002Several parcels damaged during transit due to improper packaging.Customer received partial shipment; replacements dispatched.Replacements sent, packaging reviewed for future shipments.
EXC0003blank2blankSHP10003loss2024-06-02T11:32:00Zaudit_systemBerlinDEblankclosed2024-06-03T17:45:00ZEuroShip LogisticsCUS1003Missing package reported at distribution center in Berlin.Customer informed; refund processed for lost items.Investigation completed, items confirmed lost.
EXC0004blank6NHSHP10004misroute2024-03-21T07:14:00Zsystem_autoAmsterdamNLblankresolved2024-03-23T10:12:00ZDutch ExpressCUS1004Shipment sent to incorrect hub in Amsterdam, requiring redirection.Customer notified; shipment rerouted, delivery delayed by 2 days.Shipment redirected, new estimated delivery provided.
EXC0005blank14CASHP10005delay2024-05-06T17:25:00Zsystem_autoLos AngelesUS90openblankWest Coast FreightCUS1005Traffic congestion caused delivery delay for outbound shipment.Customer experienced moderate delay; delivery rescheduled.blank
EXC0006severe18MHSHP10006damage2024-04-08T14:09:00ZArjun S.MumbaiINblankresolved2024-04-10T09:11:00ZEastern ExpressCUS1006Boxes crushed during loading at Mumbai facility.Several items unusable; replacements expedited.Damaged items replaced, team retrained on handling procedures.
EXC0007blank22blankSHP10007delay2024-06-01T13:50:00Zsystem_autoParisFR480resolved2024-06-03T08:32:00ZGlobal FreightCUS1007Unexpected customs inspection delayed shipment in Paris.Delivery postponed; customer informed of customs delay.Customs cleared and shipment released.
EXC0008blank4LOSHP10008misroute2024-02-19T11:37:00Zsystem_autoMilanITblankin_progressblankItalia ShipCUS1008Shipment sent to wrong sorting center in Milan.Customer notified; delivery rescheduled for next available slot.blank

What the 200 rows show

from the 200-row sample

Severe (damage severity) stands out: mean affected_items_count is 37.1, against 9.8 for the rest.

  • 12median affected_items_count
  • 4resolution statuses
  • 5event types
  • 34location countries
  • 44reported_by values
  • 137.5median delay_duration_minutes
Mean affected_items_count by damage_severity63 rows
020406.5minor20 rows12.3moderate26 rows37.1severe17 rows
affected_items_count200 rows, in bands of 10
045908564346531011050100affected_items_count →

Median 12, from 1 to 100.

location_state109 rows with a value · 91 left blank
  1. CA11
  2. TX9
  3. ZH6
  4. NH5
  5. NSW5
  6. IL4
  7. ON4
  8. MH4
  9. KA4
  10. VIC4
18 columns by typefrom the column list below
  • string 14
  • integer 2
  • datetime 2

Columns

18 columns in three groups
blueprint · 18 columns
columntypedescriptionexample
Text 14 columns
exception_event_idstringUnique identifier for each logistics exception eventuniqueEXC0001
shipment_idstringIdentifier for the shipment associated with the exception eventSHP10001
event_typestringType of exception event (e.g., delay, damage, loss, misroute)delay · damage · loss · misroute · otherdelay
event_descriptionstringDetailed description of the exception eventoptionalShipment delayed due to s…
reported_bystringName or identifier of the person or system reporting the exceptionsystem_auto
location_citystringCity where the exception event occurredoptionalChicago
location_statestringState or region where the exception event occurredoptionalIL
location_countrystringCountry where the exception event occurredoptionalUS
damage_severitystringSeverity of damage (if applicable)minor · moderate · severe · optionalmoderate
customer_impactstringDescription of how the exception event impacted the customeroptionalCustomer notified; delive…
resolution_statusstringCurrent status of the exception event resolutionopen · in_progress · resolved · closedresolved
resolution_notesstringNotes or actions taken to resolve the exception eventoptionalCargo securing improved.
carrier_namestringName of the logistics carrier handling the shipmentoptionalMidwest Logistics
customer_idstringIdentifier for the customer affected by the exception eventoptionalCUS1001
Numbers 2 columns
affected_items_countintegerNumber of items affected by the exception event0 or more · optional25
delay_duration_minutesintegerDuration of delay in minutes (if applicable)0 or more · optional320
Dates and times 2 columns
event_datetimedatetimeTimestamp when the exception event occurred2024-05-14T09:23:00Z
resolution_datetimedatetimeTimestamp when the exception event was resolved (if applicable)optional2024-05-15T13:58:00Z

Use it for

  • median affecte…12200 rowsmean affected items c…6.5minor12.3modera…37.1severe

    A logistics dashboard

    Affected_items_count by damage_severity and a breakdown of location_state. Excel, Power BI or Tableau.

  • Why do the 17 severe rows have a mean affected_items_count of 37.1?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable exception events with shipment_id, event_type and event_description to fill a screen in front of a buyer.

Not quite right?

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This dataset200 rows18 columns
Yours1,000 rows18 columnslocation_city: UK only

blueprint · logistics-exception-event-tracking

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
  • Log event type and timestamp
  • Include shipment ID and route
  • Flag repeated exception risks
  • Aggregate by carrier
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
logistics-exception-event-tracking

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