Logistics
26 columns
200 rows
8 formats

Cross-Border Logistics Cost Analysis

This dataset provides a detailed breakdown of international shipment costs, including customs duties, tariffs, carrier and route information, and cost components such as freight, insurance, and handling. It enables granular analysis of cost drivers and operational factors in cross-border logistics, supporting optimization and benchmarking efforts for global supply chains.

opened 5 times
last updated 12 Jul 2025
by GoMask

The brief that made it

Identifying cost-saving opportunities in international shipping

Sample rows

preview · 8 of 200 rows · all 26 columns

shipment_idstring	carrier_typestring	shipment_weight_kgfloat	delayedboolean	origin_countrystring	origin_citystring	destination_countrystring	destination_citystring	departure_datedate	arrival_datedate	carrier_namestring	route_codestring	incotermstring	shipment_volume_cbmfloat	goods_descriptionstring	hs_codestring	customs_duty_usdfloat	tariff_rate_percentfloat	other_customs_fees_usdfloat	freight_cost_usdfloat	insurance_cost_usdfloat	handling_fees_usdfloat	total_cost_usdfloat	currencystring	transit_time_daysinteger	delay_reasonstring
SHP000001	ocean	21000.5	false	China	Shanghai	United States	Los Angeles	2024-01-10	2024-01-30	Maersk Line	CNUS2401A	CIF	120.3	Consumer electronics	85171200	3400.25	5.2	340	4100.75	400	90	8331	USD	20	blank
SHP000002	ocean	18000.2	false	Germany	Hamburg	Canada	Toronto	2024-02-15	2024-02-29	Hapag-Lloyd	DECA2402B	FOB	95.1	Automotive components	87089999	1200.5	3.8	290	3670	320	120	5600.5	CAD	14	blank
SHP000003	ocean	16000.7	true	Japan	Osaka	Australia	Sydney	2024-03-02	2024-03-20	NYK Line	JPAS2403C	DPU	89.6	Heavy machinery	84295100	2600	4.1	350	4220.5	380	135	7685.5	AUD	18	Port congestion at Sydney
SHP000004	ocean	22500	false	India	Mumbai	United Kingdom	London	2024-02-05	2024-02-23	MSC Mediterranean	INUK2402D	CIF	132	Textile garments	61091000	1100	6	150	5080	470	95	6895	GBP	18	blank
SHP000005	ocean	12000.6	false	France	Marseille	Morocco	Casablanca	2024-03-03	2024-03-10	CMA CGM	FRMA2403E	FOB	60.3	Wine products	22042100	700.8	2.7	70	2050.8	160	80	3061.6	MAD	7	blank
SHP000006	ocean	19500.2	true	Brazil	Santos	South Africa	Durban	2024-01-12	2024-02-10	Hamburg Süd	BRSA2401F	CIF	104.7	Coffee beans	09011100	950	1.9	120	3870	310	105	5355	ZAR	29	Adverse weather conditions
SHP000007	ocean	17500.8	false	South Korea	Busan	Russia	Vladivostok	2024-01-26	2024-02-10	Hyundai Merchant	KRRU2401G	CIF	99.2	Plastic resins	39012000	1100	2.4	210	3120	220	75	4725	RUB	15	blank
SHP000008	ocean	22000	false	Italy	Genoa	United States	New York	2024-02-20	2024-03-10	Evergreen Marine	ITUS2402H	CIF	122.5	Ceramic tiles	69072190	2100	4.8	180	3950	260	110	6600	USD	19	blank
SHP000009	ocean	19500	false	Vietnam	Ho Chi Minh City	Germany	Hamburg	2024-01-18	2024-02-13	COSCO Shipping	VNDE2401I	FOB	102.7	Footwear	64039900	890	3.8	80	3400	310	95	4775	EUR	26	blank
SHP000010	ocean	14000.8	false	Turkey	Istanbul	Egypt	Alexandria	2024-01-06	2024-01-23	ZIM Integrated	TRAE2401J	CIF	78.9	Household appliances	85094010	1000	2.2	90	2900	195	65	4250	EGP	17	blank
SHP000011	air	1500.2	false	Japan	Tokyo	United States	San Francisco	2024-04-01	2024-04-09	ANA Cargo	JPUS2404K	CIP	4.8	Pharmaceuticals	30049099	250	2.1	50	2100	75	70	2545	USD	8	blank
SHP000012	air	720.9	false	Switzerland	Zurich	United Arab Emirates	Dubai	2024-03-12	2024-03-17	Emirates SkyCargo	CHAE2403L	CPT	2.5	Luxury watches	91021200	600	4.5	40	1500	60	45	2245	AED	5	blank
SHP000013	air	2100.7	false	Singapore	Singapore	Australia	Melbourne	2024-01-10	2024-01-16	Singapore Airlines	SGAU2401M	CIP	8.6	Medical equipment	90189090	210	2.9	30	1700	60	30	2030	AUD	6	blank
SHP000014	air	3800.4	false	United States	Chicago	Brazil	Sao Paulo	2024-02-07	2024-02-13	United Cargo	USBR2402N	CPT	9.7	Telecom equipment	85176200	410	3.5	60	1900	80	40	2490	BRL	6	blank
SHP000015	air	860.8	false	India	Delhi	Singapore	Singapore	2024-03-08	2024-03-13	IndiGo Cargo	INSG2403O	DDP	2.6	Garments samples	62034290	150	2.7	20	950	40	25	1185	SGD	5	blank
SHP000016	air	2900	false	Hong Kong	Hong Kong	France	Paris	2024-01-15	2024-01-22	Cathay Pacific Cargo	HKFR2401P	CFR	4.9	Luxury handbags	42022100	420	4.9	35	1850	50	35	2390	EUR	7	blank
SHP000017	rail	10500.5	false	Netherlands	Rotterdam	Poland	Warsaw	2024-02-14	2024-02-16	DB Cargo	NLPL2402Q	DAP	52.7	Industrial parts	84818099	780	2.4	60	1120	110	60	2130	EUR	2	blank
SHP000018	rail	15300.2	true	Russia	Moscow	China	Harbin	2024-04-15	2024-04-21	RZD Logistics	RUCN2404R	DAP	82.5	Paper reels	48102930	410	4.2	30	1760	75	45	2320	CNY	6	Technical issue at border
SHP000019	rail	3900.1	false	Poland	Gdansk	Czech Republic	Prague	2024-03-05	2024-03-08	PKP Cargo	PLCZ2403S	DAP	19.5	Engine blocks	84099999	350	2.9	25	810	60	30	1275	CZK	3	blank
SHP000020	rail	11000.6	false	Hungary	Budapest	Austria	Vienna	2024-02-17	2024-02-18	Rail Cargo Austria	HUAT2402T	CPT	58.1	Steel coils	72082710	540	3.2	20	900	55	40	1555	EUR	1	blank

What the 200 rows show

from the 200-row sample

Ocean (carrier type) stands out: mean shipment_weight_kg is 16,879, against 3,690 for the rest.

10%delayed = true
10,200median shipment_weight_kg
9incoterms
36currencies
55destination countries
53.1median shipment_volume_cbm

Mean shipment_weight_kg by carrier_type200 rows

shipment_weight_kg200 rows, in bands of 2,500

Median 10,200, from 0.0 to 23,000.

origin_country200 rows · top 10 of 37 values

China25
USA18
United States15
Germany11
India10
Japan9
France8
Singapore8
South Korea7
Spain7

26 columns by typefrom the column list below

string 13
integer 1
float 9
date 2
boolean 1

Columns

26 columns in four groups

blueprint · 26 columns
column	type	description	example
Text 13 columns
`shipment_id`	string	Unique identifier for each international shipmentunique	SHP000001
`origin_country`	string	Country where the shipment originates	China
`origin_city`	string	City where the shipment originates	Shanghai
`destination_country`	string	Country where the shipment is delivered	United States
`destination_city`	string	City where the shipment is delivered	Los Angeles
`carrier_name`	string	Name of the logistics carrier responsible for the shipment	Maersk Line
`carrier_type`	string	Type of carrier (e.g., ocean, air, road, rail)ocean · air · road · rail	ocean
`route_code`	string	Unique code representing the route taken by the shipment	CNUS2401A
`incoterm`	string	International commercial term defining responsibilities (e.g., FOB, CIF, DDP)11 values	CIF
`goods_description`	string	Brief description of the goods being shipped	Consumer electronics
`hs_code`	string	Harmonized System code for the goods	85171200
`currency`	string	Currency used for cost calculations (ISO 4217 code)	USD
`delay_reason`	string	Reason for delay, if anyoptional	Customs inspection delay
Numbers 10 columns
`shipment_weight_kg`	float	Total weight of the shipment in kilograms0 or more	21000.5
`shipment_volume_cbm`	float	Total volume of the shipment in cubic meters0 or more	120.3
`customs_duty_usd`	float	Customs duty paid for the shipment in USD0 or more	3400.25
`tariff_rate_percent`	float	Tariff rate applied to the shipment as a percentage0 to 100	5.2
`other_customs_fees_usd`	float	Other customs-related fees (e.g., brokerage, inspection) in USD0 or more · optional	340
`freight_cost_usd`	float	Freight cost charged by the carrier in USD0 or more	4100.75
`insurance_cost_usd`	float	Insurance cost for the shipment in USD0 or more · optional	400
`handling_fees_usd`	float	Handling fees incurred during shipment in USD0 or more · optional	90
`total_cost_usd`	float	Total cost of the shipment including all components in USD0 or more	8331
`transit_time_days`	integer	Total transit time from origin to destination in days0 or more	20
Dates and times 2 columns
`departure_date`	date	Date when the shipment departs from the origin	2024-01-10
`arrival_date`	date	Date when the shipment arrives at the destination	2024-01-30
True or false 1 column
`delayed`	boolean	Indicates if the shipment was delayed beyond scheduled arrival	false

Use it for

A logistics dashboard
The delayed rate, shipment_weight_kg by carrier_type and a breakdown of origin_country. Excel, Power BI or Tableau.
Why do the 95 ocean rows have a mean shipment_weight_kg of 16,879?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Shipments200
SHP00000121000.5ocean
SHP00000316000.7ocean
SHP00000619500.2ocean
A software demo
Believable shipments with origin_country, origin_city and destination_country to fill a screen in front of a buyer.

This dataset200 rows26 columns

Yours1,000 rows26 columnsorigin_country: UK only

blueprint · cross-border-logistics-cost-analysis

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

Costs itemized by customs and carrier fees
Routes with over 3 stops detailed
Shipments under $100 excluded
All currency in USD

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: cross-border-logistics-cost-analysis

What should your data show?

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Sample rows

What the 200 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?