Logistics
29 columns
200 rows
8 formats

Supply Chain Delivery Delay Logs

This dataset provides detailed logs of global supply chain delivery events, including shipment details, scheduled and actual delivery dates, delay durations, reasons for delays, and anomalies encountered. It is ideal for machine learning applications in delay prediction, risk assessment, and supply chain optimization, offering granular insights into operational bottlenecks and logistics performance.

opened 15 times
last updated 20 Aug 2025
by GoMask

The brief that made it

Machine learning for delivery delay prediction

Sample rows

preview · 8 of 200 rows · all 29 columns

delivery_event_idstring	transport_modestring	shipment_volume_m3float	anomaly_detectedboolean	origin_statestring	shipment_idstring	order_idstring	carrier_namestring	origin_street_addressstring	origin_citystring	origin_postal_codestring	origin_countrystring	destination_street_addressstring	destination_citystring	destination_statestring	destination_postal_codestring	destination_countrystring	scheduled_delivery_datedate	actual_delivery_datedate	delay_duration_hoursfloat	delay_reasonstring	delay_severitystring	anomaly_typestring	reported_bystring	event_created_datetimedatetime	shipment_weight_kgfloat	temperature_controlledboolean	high_value_goodsboolean	customs_clearance_requiredboolean
DEL-00000001	air	6.2	false	HE	SHIP-0101	ORD-1234	DHL Express	11 Hauptstrasse	Frankfurt	60311	Germany	200 Queen St	Toronto	ON	M5V2B6	Canada	2024-04-07	2024-04-08	24	weather	minor	none	blank	2024-04-07T10:23:00	450.3	false	true	true
DEL-00000002	road	0.022	false	blank	SHIP-0202	blank	FedEx	25 Rue Lafayette	Paris	75009	France	18 Oxford St	London	London	W1D1BS	UK	2024-04-08	2024-04-08	0	blank	none	none	blank	2024-04-08T08:19:00	3.55	false	false	false
DEL-00000003	sea	200.7	false	blank	SHIP-0303	ORD-3456	Maersk	88 Zhongshan Rd	Shanghai	200001	China	100 Pier 70 Blvd	San Francisco	CA	94107	USA	2024-04-10	2024-04-14	96	customs	critical	none	Customs Agent SF	2024-04-10T14:37:00	18000.1	false	true	true
DEL-00000004	road	0.009	false	Merseyside	SHIP-0202	blank	Royal Mail	15 Abbey Rd	Liverpool	L189AB	UK	33 Market St	Manchester	Manchester	M11DN	UK	2024-04-06	2024-04-06	0	blank	none	none	blank	2024-04-06T09:13:00	0.12	false	false	false
DEL-00000005	rail	67.2	true	blank	SHIP-0404	ORD-5678	China Rail	100 Beijing Rd	Beijing	100000	China	7 Tverskaya St	Moscow	blank	101000	Russia	2024-04-13	2024-04-14	24	mechanical_failure	minor	damaged_goods	Station Moscow	2024-04-13T15:03:00	8000.5	false	true	true
DEL-00000006	road	0.03	false	IL	SHIP-0505	ORD-9012	Amazon Logistics	502 Market St	Chicago	60607	USA	1010 Elm St	St Louis	MO	63101	USA	2024-04-09	2024-04-09	0	blank	none	none	blank	2024-04-09T11:27:00	1.25	false	false	false
DEL-00000007	road	0.007	false	MH	SHIP-0606	blank	Blue Dart	19 MG Road	Mumbai	400001	India	2 Residency Rd	Bangalore	KA	560025	India	2024-04-11	blank	blank	blank	none	none	blank	2024-04-11T12:44:00	0.62	false	false	false
DEL-00000008	sea	190.1	false	blank	SHIP-0404	blank	COSCO	33 Xiamen Port	Xiamen	361000	China	99 Yonge St	Toronto	ON	M5E1J3	Canada	2024-04-12	2024-04-15	72	customs	severe	none	Customs Toronto	2024-04-12T17:08:00	17500.9	true	true	true
DEL-00000009	road	0.016	false	blank	SHIP-0707	ORD-1122	DHL Express	501 Gran Vía	Madrid	28013	Spain	8 Champs-Élysées	Paris	blank	75008	France	2024-04-08	2024-04-08	0	blank	none	none	blank	2024-04-08T13:15:00	2.5	false	false	false
DEL-00000010	rail	42.9	false	BE	SHIP-0808	ORD-3344	DB Cargo	16 Wilhelmstr.	Berlin	10117	Germany	17 Bahnhofstr.	Zurich	blank	8001	Switzerland	2024-04-15	2024-04-16	24	mechanical_failure	minor	none	Rail Zurich	2024-04-15T09:20:00	12000	false	false	true
DEL-00000011	air	0.011	false	blank	SHIP-0909	blank	Japan Post	1 Chiyoda	Tokyo	100-0001	Japan	12 Orchard Rd	Singapore	blank	238863	Singapore	2024-04-12	blank	blank	blank	none	none	blank	2024-04-12T10:33:00	0.98	false	false	true
DEL-00000012	road	0.12	false	GA	SHIP-1010	ORD-5567	UPS	400 Peachtree St	Atlanta	30308	USA	802 W 12th St	Austin	TX	78701	USA	2024-04-10	2024-04-11	24	traffic	moderate	none	Austin Ops	2024-04-10T13:29:00	8.2	false	false	false
DEL-00000013	road	0.022	false	blank	SHIP-1111	blank	GLS	32 Nieuwezijds Voorburgwal	Amsterdam	1012SA	Netherlands	22 Kurfürstendamm	Berlin	BE	10719	Germany	2024-04-13	2024-04-13	0	blank	none	none	blank	2024-04-13T08:49:00	1.82	false	false	false
DEL-00000014	air	10.3	true	blank	SHIP-1212	ORD-6677	Transaero	18 Nevsky Prospekt	St Petersburg	191186	Russia	210 Collins St	Melbourne	VIC	3000	Australia	2024-04-14	2024-04-15	24	weather	minor	damaged_goods	Melbourne Agent	2024-04-14T11:18:00	1050.6	true	true	true
DEL-00000015	rail	0.09	false	Cambridgeshire	SHIP-1313	blank	Stobart Rail	40 Station Rd	Cambridge	CB11JZ	UK	14 King St	Glasgow	Glasgow	G11AA	UK	2024-04-12	2024-04-12	0	blank	none	none	blank	2024-04-12T08:14:00	12.8	false	false	false
DEL-00000016	sea	185	false	blank	SHIP-1414	ORD-2233	MSC	17 Keppel Rd	Singapore	089058	Singapore	88 Harbour St	Sydney	NSW	2000	Australia	2024-04-08	2024-04-10	48	weather	severe	none	Sydney Port	2024-04-08T16:24:00	16200	false	true	true
DEL-00000017	road	0.019	false	blank	SHIP-1515	blank	GLS	23 Rue du Rhône	Geneva	1204	Switzerland	16 Via Roma	Milan	MI	20121	Italy	2024-04-10	2024-04-10	0	blank	none	none	blank	2024-04-10T09:57:00	1.34	false	false	true
DEL-00000018	road	0.025	false	CA	SHIP-1616	ORD-7788	FedEx	2 Wilshire Blvd	Los Angeles	90017	USA	15 5th Ave	New York	NY	10011	USA	2024-04-09	2024-04-09	0	blank	none	none	blank	2024-04-09T13:44:00	4.2	false	false	false
DEL-00000019	sea	210.5	false	blank	SHIP-1717	ORD-9988	Maersk	120 Changi Rd	Singapore	419718	Singapore	60 Port Dr	Cape Town	WC	8001	South Africa	2024-04-11	2024-04-14	72	weather	severe	none	Cape Town Port	2024-04-11T14:09:00	15850	true	true	true
DEL-00000020	road	0.02	false	BE	SHIP-1818	ORD-6677	DHL Express	7 Alexanderplatz	Berlin	10178	Germany	22 Rue de Rivoli	Paris	blank	75001	France	2024-04-13	2024-04-13	0	blank	none	none	blank	2024-04-13T10:10:00	3.1	false	false	true

What the 200 rows show

from the 200-row sample

Sea (transport mode) stands out: mean shipment_volume_m3 is 213.1, against 16.8 for the rest.

12%anomaly_detected = true
0.08median shipment_volume_m3
5delay severities
5anomaly types
6delay reasons
16carrier names

Mean shipment_volume_m3 by transport_mode200 rows

shipment_volume_m3200 rows, in bands of 50

Median 0.08, from 0.00 to 259.8.

origin_state89 rows with a value · 111 left blank

CA9
Greater London8
MH5
NSW5
HE4
BE4
Berlin4
Merseyside3
IL3
NY3

29 columns by typefrom the column list below

string 19
float 3
date 2
datetime 1
boolean 4

Columns

29 columns in four groups

blueprint · 29 columns
column	type	description	example
Text 19 columns
`delivery_event_id`	string	Unique identifier for each delivery event in the supply chain.unique	DEL-00000001
`shipment_id`	string	Identifier for the shipment associated with the delivery event.	SHIP-0101
`order_id`	string	Identifier for the customer order linked to the shipment.optional	ORD-1234
`carrier_name`	string	Name of the logistics carrier responsible for the delivery.	DHL Express
`origin_street_address`	string	Street address of the shipment origin.	11 Hauptstrasse
`origin_city`	string	City of the shipment origin.	Frankfurt
`origin_state`	string	State or region of the shipment origin.optional	HE
`origin_postal_code`	string	Postal code of the shipment origin.optional	60311
`origin_country`	string	Country of the shipment origin.	Germany
`destination_street_address`	string	Street address of the shipment destination.	200 Queen St
`destination_city`	string	City of the shipment destination.	Toronto
`destination_state`	string	State or region of the shipment destination.optional	ON
`destination_postal_code`	string	Postal code of the shipment destination.optional	M5V2B6
`destination_country`	string	Country of the shipment destination.	Canada
`delay_reason`	string	Primary reason for the delivery delay (e.g., weather, customs, traffic, mechanical failure).7 values · optional	weather
`delay_severity`	string	Severity level of the delay.none · minor · moderate · severe · critical · optional	minor
`anomaly_type`	string	Type of anomaly detected (if any).6 values · optional	none
`reported_by`	string	Name or identifier of the person or system reporting the delay/anomaly.optional	Customs Agent SF
`transport_mode`	string	Mode of transportation used for the shipment.air · sea · road · rail · multimodal	air
Numbers 3 columns
`delay_duration_hours`	float	Number of hours the delivery was delayed compared to the scheduled date.0 or more · optional	24
`shipment_weight_kg`	float	Weight of the shipment in kilograms.0 or more · optional	450.3
`shipment_volume_m3`	float	Volume of the shipment in cubic meters.0 or more · optional	6.2
Dates and times 3 columns
`scheduled_delivery_date`	date	Planned date for delivery to the destination.	2024-04-07
`actual_delivery_date`	date	Actual date when the delivery was completed.optional	2024-04-08
`event_created_datetime`	datetime	Timestamp when the delivery event was logged in the system.	2024-04-07T10:23:00
True or false 4 columns
`anomaly_detected`	boolean	Indicates if an anomaly was detected during the delivery event.	false
`temperature_controlled`	boolean	Indicates if the shipment required temperature control.optional	false
`high_value_goods`	boolean	Indicates if the shipment contains high-value goods.optional	true
`customs_clearance_required`	boolean	Indicates if customs clearance was required for the shipment.optional	true

15 SQL practice questions for the Supply Chain Delivery Delay Logs

Written for this data and checked against it: 12 SQL and 3 Power BI exercises from beginner to advanced. Try the first three free; the practice kit has all 15, a starter .sql file and an answer key with every expected result.

01BeginnerFreeSELECT · WHERE · ORDER BY
Severe and critical delays
Return delivery_event_id, carrier_name, delay_duration_hours and delay_severity for every event whose delay_severity is 'severe' or 'critical'. Order the results by delay_duration_hours from highest to lowest, then by delivery_event_id ascending.
Tables: supply_chain_delivery_delay_logs · your result: 4 columns, 29 rows
02BeginnerFreeDISTINCT · WHERE · NULL
Distinct delay reasons
Return each distinct delay_reason that is not null, in a single column named delay_reason, sorted alphabetically.
Tables: supply_chain_delivery_delay_logs · your result: 1 columns, 6 rows
03BeginnerFreeGROUP BY · COUNT
Events per transport mode
Count the events for each transport_mode. Return transport_mode and event_count, ordered by event_count from highest to lowest, then by transport_mode alphabetically.
Tables: supply_chain_delivery_delay_logs · your result: 2 columns, 4 rows

04Average delay by carrierBeginner
05Power BI: events by anomaly typePower BI
06Reasons with many delaysIntermediate
07Weight class bucketsIntermediate
08Carriers slower than overallIntermediate
09Monthly delayed eventsIntermediate
10Power BI: delay rate measurePower BI
11Rank carriers by delayAdvanced
12Gap to previous shipment eventAdvanced
13Running delay hours by monthAdvanced
14Share of delay hours by reasonAdvanced
15Power BI: top event per carrierPower BI

Get the practice kit: the full dataset, all 15 exercises, a starter .sql and the answer key. One-time $3.99.

Get the practice kit · $3.99

Use it for

A logistics dashboard
The anomaly_detected rate, shipment_volume_m3 by transport_mode and a breakdown of origin_state. Excel, Power BI or Tableau.
Why do the 36 sea rows have a mean shipment_volume_m3 of 213.1?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Delivery events200
DEL-000000016.2air
DEL-0000000567.2rail
DEL-0000001410.3air
A software demo
Believable delivery events with shipment_id, order_id and carrier_name to fill a screen in front of a buyer.

This dataset200 rows29 columns

Yours1,000 rows29 columnsorigin_street_address: UK only

blueprint · supply-chain-delivery-delay-logs

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 29 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

Track origin and destination nodes
Label delivery events with delay reason
Include real-world disruption patterns
Multi-modal carrier simulation
Temporal features for holiday/peak effects

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: supply-chain-delivery-delay-logs

What should your data show?

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Sample rows

What the 200 rows show

Columns

Severe and critical delays

Distinct delay reasons

Events per transport mode

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?