Logistics
22 columns
120 rows
8 formats

Intermodal Shipment Anomaly Logs

This dataset provides detailed logs of shipment anomalies detected by IoT sensors in intermodal logistics, including event types, severity, location, and resolution actions. It enables supply chain managers to monitor, analyze, and respond to operational risks and inefficiencies in real time, supporting advanced analytics and AI-driven anomaly detection for improved shipment integrity.

last updated 12 Jan 2026
by GoMask

The brief that made it

Real-time supply chain risk monitoring and mitigation

Sample rows

preview · 8 of 120 rows · all 22 columns

anomaly_idstring	anomaly_typestring	event_valuefloat	resolvedboolean	shipment_destination_countrystring	shipment_idstring	container_idstring	sensor_idstring	anomaly_severitystring	anomaly_timestampdatetime	location_latitudefloat	location_longitudefloat	unit_of_measurementstring	transport_modestring	shipment_origin_citystring	shipment_origin_statestring	shipment_origin_countrystring	shipment_destination_citystring	shipment_destination_statestring	resolution_timestampdatetime	resolution_actionstring	notesstring
ANOM-0001	temperature	36.7	true	USA	SHIP-1234	CONT-001A	SENS-A123	high	2024-06-03T10:20:15Z	37.7749	-122.4194	Celsius	truck	San Francisco	CA	USA	Chicago	IL	2024-06-03T11:40:12Z	Cooling unit activated and container sealed.	Unexpected summer temperature spike during unloading in Chicago.
ANOM-0002	humidity	98.2	false	Netherlands	SHIP-1235	CONT-002B	SENS-B234	critical	2024-05-29T16:15:48Z	51.5074	-0.1278	%RH	ship	London	England	UK	Rotterdam	South Holland	blank	blank	Humidity peaked offshore near English Channel boundary.
ANOM-0003	temperature	9.2	true	USA	SHIP-1234	CONT-001A	SENS-A123	medium	2024-06-03T14:22:35Z	41.8781	-87.6298	Celsius	truck	San Francisco	CA	USA	Chicago	IL	2024-06-03T15:30:06Z	Container repacked and insulated.	Low temperature detected following early morning delivery.
ANOM-0004	vibration	13.8	false	Sweden	SHIP-1236	blank	SENS-C341	critical	2024-05-27T08:12:40Z	35.6895	139.6917	g-force	ship	Tokyo	Tokyo	Japan	Stockholm	Stockholm	blank	blank	Heavy vibration detected during rough sea crossing.
ANOM-0005	shock	16.7	true	Germany	SHIP-1237	CONT-003C	SENS-D456	high	2024-04-15T19:40:07Z	55.7558	37.6173	g-force	rail	Moscow	Moscow	Russia	Berlin	Berlin	2024-04-15T21:12:50Z	Packaging reinforced at transit station.	Sharp turn at Moscow station caused high shock event.
ANOM-0006	location_deviation	185.3	true	Spain	SHIP-1238	CONT-004D	SENS-E567	critical	2024-05-17T05:23:15Z	34.0522	-118.2437	km	air	Los Angeles	CA	USA	Madrid	Madrid	2024-05-17T06:22:13Z	Security notified and flight rerouted.	Flight deviated from planned route near LAX.
ANOM-0007	unauthorized_access	1	false	UK	SHIP-1239	blank	SENS-F678	medium	2024-06-01T12:17:30Z	40.6413	-73.7781	event	air	New York	NY	USA	London	England	blank	blank	Access detected in loading bay at JFK airport.
ANOM-0008	other	9999.9	false	Antarctica	SHIP-1240	blank	SENS-G789	critical	2024-03-11T23:41:12Z	-90	180	custom_units	other	McMurdo Station	Antarctica	Antarctica	South Pole	Antarctica	blank	blank	System overflow detected at polar boundary.
ANOM-0009	vibration	6.3	true	Iran	SHIP-1241	CONT-005E	SENS-H890	medium	2024-04-22T06:05:21Z	35.6895	139.6917	g-force	rail	Tokyo	Tokyo	Japan	Tehran	Tehran	2024-04-22T07:03:15Z	Car replaced at regional station.	Motion sensor flagged vibration on eastbound train.
ANOM-0010	temperature	-50	false	Canada	SHIP-1242	blank	SENS-I901	critical	2024-02-01T02:44:50Z	90	0	Celsius	air	Longyearbyen	Svalbard	Norway	Alert	Nunavut	blank	blank	Extreme cold detected during polar flight.
ANOM-0011	humidity	89.5	true	UK	SHIP-1243	CONT-006F	SENS-J012	medium	2024-05-12T11:14:19Z	59.3293	18.0686	%RH	ship	Stockholm	Stockholm	Sweden	London	England	2024-05-12T12:58:43Z	Waterproofing applied at port.	Humidity rose sharply offshore near Baltic Sea.
ANOM-0012	shock	7.2	true	Russia	SHIP-1244	CONT-007G	SENS-K123	low	2024-04-19T17:22:11Z	52.52	13.405	g-force	rail	Berlin	Berlin	Germany	Moscow	Moscow	2024-04-19T18:17:18Z	Packaging reinforced at Berlin station.	Shock event detected at platform during loading.
ANOM-0013	location_deviation	112.8	true	USA	SHIP-1245	blank	SENS-L234	high	2024-05-10T15:31:55Z	48.8566	2.3522	km	air	Paris	Île-de-France	France	New York	NY	2024-05-10T16:22:17Z	Route deviation verified and corrected.	Flight deviated over Atlantic, rerouted to NY.
ANOM-0014	temperature	39.2	true	USA	SHIP-1246	CONT-008H	SENS-M345	high	2024-06-05T13:09:33Z	34.0522	-118.2437	Celsius	truck	Los Angeles	CA	USA	Phoenix	AZ	2024-06-05T14:51:02Z	Cooling applied at unloading.	Summer heat spike during highway transit.
ANOM-0015	other	0.5	false	American Samoa	SHIP-1247	blank	SENS-N456	critical	2024-01-28T23:59:59Z	blank	blank	custom_units	other	Apia	Upolu	Samoa	Pago Pago	Tutuila	blank	blank	Operational minimum reached near Date Line.
ANOM-0016	humidity	79.8	false	China	SHIP-1248	CONT-009I	SENS-O567	high	2024-05-30T02:18:41Z	35.6895	139.6917	%RH	ship	Tokyo	Tokyo	Japan	Shanghai	Shanghai	blank	blank	High humidity event detected in container bay.
ANOM-0017	vibration	2.9	true	Iran	SHIP-1249	CONT-010J	SENS-P678	medium	2024-04-15T22:50:10Z	52.52	13.405	g-force	rail	Berlin	Berlin	Germany	Tehran	Tehran	2024-04-15T23:36:21Z	Car replaced at Berlin station.	Consistent vibration detected at departure.
ANOM-0018	unauthorized_access	1	true	Japan	SHIP-1250	blank	SENS-Q789	high	2024-06-02T18:32:45Z	40.6413	-73.7781	event	air	New York	NY	USA	Tokyo	Tokyo	2024-06-02T19:09:14Z	Access verified by security staff.	Unscheduled door open detected during loading.
ANOM-0019	temperature	38.1	false	USA	SHIP-1251	CONT-011K	SENS-R890	medium	2024-06-06T16:45:30Z	41.8781	-87.6298	Celsius	truck	Chicago	IL	USA	Dallas	TX	blank	blank	High temperature recorded during southern route.
ANOM-0020	vibration	9.7	true	Germany	SHIP-1252	CONT-012L	SENS-S901	high	2024-04-16T10:11:22Z	55.7558	37.6173	g-force	rail	Moscow	Moscow	Russia	Berlin	Berlin	2024-04-16T11:22:40Z	Motion sensor recalibrated at Moscow station.	High vibration at departure platform.

What the 120 rows show

from the 120-row sample

Other (anomaly type) stands out: mean event_value is 5,135, against 38.0 for the rest.

57%resolved = true
26.6median event_value
4anomaly severities
5transport modes
6unit of measurements
30shipment origin countries

Mean event_value by anomaly_type120 rows

event_value120 rows, in bands of 2,000

Median 26.6, from -50.0 to 10,000.

shipment_destination_country120 rows · top 10 of 35 values

USA30
Germany16
UK8
Spain5
Russia5
Netherlands4
Sweden4
Iran4
Canada4
China4

22 columns by typefrom the column list below

string 16
float 3
datetime 2
boolean 1

Columns

22 columns in four groups

blueprint · 22 columns
column	type	description	example
Text 16 columns
`anomaly_id`	string	Unique identifier for each anomaly event detected by IoT sensors.unique	ANOM-0001
`shipment_id`	string	Unique identifier for the shipment associated with the anomaly.	SHIP-1234
`container_id`	string	Unique identifier for the intermodal container involved in the shipment.optional	CONT-001A
`sensor_id`	string	Unique identifier for the IoT sensor that detected the anomaly.	SENS-A123
`anomaly_type`	string	Type of anomaly detected (e.g., temperature spike, vibration, unauthorized access, humidity, shock).7 values	temperature
`anomaly_severity`	string	Severity level of the anomaly (e.g., low, medium, high, critical).low · medium · high · critical	high
`unit_of_measurement`	string	Unit of measurement for the event_value (e.g., Celsius, %RH, g-force).6 measurements · optional	Celsius
`transport_mode`	string	Mode of transportation at the time of anomaly (e.g., rail, truck, ship, air).rail · truck · ship · air · other · optional	truck
`shipment_origin_city`	string	City where the shipment originated.optional	San Francisco
`shipment_origin_state`	string	State or region where the shipment originated.optional	CA
`shipment_origin_country`	string	Country where the shipment originated.optional	USA
`shipment_destination_city`	string	Destination city for the shipment.optional	Chicago
`shipment_destination_state`	string	Destination state or region for the shipment.optional	IL
`shipment_destination_country`	string	Destination country for the shipment.optional	USA
`resolution_action`	string	Description of the action taken to resolve the anomaly.optional	Cooling unit activated an…
`notes`	string	Additional notes or comments regarding the anomaly event.optional	Unexpected summer tempera…
Numbers 3 columns
`location_latitude`	float	Latitude coordinate where the anomaly was detected.-90 to 90 · optional	37.7749
`location_longitude`	float	Longitude coordinate where the anomaly was detected.-180 to 180 · optional	-122.4194
`event_value`	float	Measured value associated with the anomaly (e.g., temperature in Celsius, vibration magnitude).optional	36.7
Dates and times 2 columns
`anomaly_timestamp`	datetime	Date and time when the anomaly was detected.	2024-06-03T10:20:15Z
`resolution_timestamp`	datetime	Date and time when the anomaly was resolved (if applicable).optional	2024-06-03T11:40:12Z
True or false 1 column
`resolved`	boolean	Indicates whether the anomaly has been resolved.	true

Use it for

A logistics dashboard
The resolved rate, event_value by anomaly_type and a breakdown of shipment_destination_country. Excel, Power BI or Tableau.
Why do the 12 other rows have a mean event_value of 5,135?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Anomalies120
ANOM-000136.7temperat…
ANOM-000298.2humidity
ANOM-00039.2temperat…
A software demo
Believable anomalies with shipment_id, container_id and sensor_id to fill a screen in front of a buyer.

This dataset120 rows22 columns

Yours1,000 rows22 columnslocation_latitude: UK only

blueprint · intermodal-shipment-anomaly-logs

Behind this dataset

Same schema. As many rows as you need.

These 120 rows came out of a blueprint — 22 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

Each row represents a single shipment event flagged by IoT sensors.
Include anomaly type, detected timestamp, shipment mode (rail, truck, ship), and severity score.
Geo-coordinates and weather conditions logged for each anomaly occurrence.
Anomaly cause must be classified (e.g., delay, temperature breach, shock, unauthorized access).
Only shipments with at least one anomaly within the last 48 hours are included.
All data points anonymized and compliant with transportation privacy standards.

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

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: intermodal-shipment-anomaly-logs

What should your data show?

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Sample rows

What the 120 rows show

Columns

Use it for

A logistics dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Logistics

What should your data show?