Logistics
21 columns
86 rows
8 formats

AI-Driven Shipping Route Adjustments

This dataset provides detailed records of real-time shipping route adjustments influenced by AI, capturing vessel, route, port, cargo, and adjustment rationale alongside sustainability, weather, and cost metrics. It enables supply chain planners and ESG analysts to evaluate the impact of route changes on emissions, operational efficiency, and logistics costs, supporting data-driven optimization for greener and more cost-effective shipping.

last updated 25 Dec 2025
by GoMask

The brief that made it

Supply chain optimization for reduced emissions and improved efficiency

Sample rows

preview · 8 of 86 rows · all 21 columns

adjustment_idstring	reason_for_adjustmentstring	sustainability_scorefloat	origin_port_codestring	timestampdatetime	vessel_idstring	route_idstring	destination_port_codestring	previous_routestring	adjusted_routestring	weather_conditionstring	emissions_estimate_kgco2float	previous_emissions_kgco2float	cost_estimate_usdfloat	previous_cost_usdfloat	distance_nmfloat	previous_distance_nmfloat	previous_sustainability_scorefloat	cargo_typestring	cargo_weight_tonsfloat	adjustment_made_bystring
SH-RT-0021A3F	sustainability	92.3	LAX	2024-05-08T09:17:32Z	VES-10981	RT-4521	SHA	PacificDirect4521	EcoPacific4521A	blank	125000.56	143210	89000.5	96000	6180.7	6352.9	79.1	Electronics	450.8	AI-OptiRoute-5
SH-RT-0021A40	weather	85.6	NYC	2024-05-08T10:05:44Z	VES-20021	RT-3350	LIV	NorthAtlantic3350	GreenAtlantic3350B	Heavy fog, low visibility	112000.34	117500.8	104300	108000	3190.2	3250.1	81.2	Pharmaceuticals	120.5	AI-WeatherSense-2
SH-RT-0021A41	cost	78.1	RTM	2024-05-08T11:22:19Z	VES-51230	RT-1025	HAM	EuroLoop1025	EcoEuro1025C	blank	61000.25	61730.4	48200	49500	425.4	440.2	75.2	Machinery	225	HumanOperator-7
SH-RT-0021A42	sustainability	90.4	DXB	2024-05-08T12:38:55Z	VES-33490	RT-6392	SIN	MidEastAsia6392	EcoAsia6392B	blank	117900	121200	75200.75	77600	3860.8	3895.3	82.7	Textiles	310.3	AI-OptiRoute-5
SH-RT-0021A43	regulatory	81	HKG	2024-05-08T13:13:20Z	VES-12890	RT-2238	SYD	AsiaPacific2238	EcoPacific2238A	blank	104500	104500	93750	93750	4475	4475	81	Agricultural	800.2	AI-Compliance-1
SH-RT-0021A44	weather	88.9	JPN	2024-05-08T14:59:02Z	VES-41111	RT-7708	VAN	AsiaNAmerica7708	EcoNAmerica7708B	Typhoon warning	119200	123000	98600	102300	4702.7	4975.5	84.2	Automobiles	320	AI-WeatherSense-2
SH-RT-0021A45	sustainability	94.1	SFO	2024-05-09T08:23:10Z	VES-23879	RT-1010	YOK	PacificLoop1010	EcoPacific1010D	blank	98000	105200	83000	86000	4962	5200	80.6	Consumer Goods	520.4	AI-OptiRoute-5
SH-RT-0021A46	cost	76.2	RTM	2024-05-09T09:15:18Z	VES-30177	RT-4025	NYC	EuroAmerica4025	GreenAtlantic4025A	blank	85000	88000	63000	66000	3580.6	3700	72.9	Furniture	311	HumanOperator-4
SH-RT-0021A47	sustainability	95	LAX	2024-05-09T10:01:30Z	VES-10981	RT-4521	SHA	PacificDirect4521	EcoPacific4521B	blank	127500	140000	87200	93000	6132.4	6300	82	Electronics	475	AI-OptiRoute-5
SH-RT-0021A48	cost	79.1	RTM	2024-05-09T11:46:59Z	VES-51230	RT-1025	HAM	EuroLoop1025	EcoEuro1025B	blank	61200	62700	48000	50000	430	445	76	Machinery	228.5	HumanOperator-7
SH-RT-0021A49	sustainability	91.3	DXB	2024-05-09T13:14:23Z	VES-33490	RT-6392	SIN	MidEastAsia6392	EcoAsia6392C	blank	114350	120000	74500	78500	3820	3950	85	Textiles	314	AI-OptiRoute-5
SH-RT-0021A4A	regulatory	81	HKG	2024-05-09T14:57:32Z	VES-12890	RT-2238	SYD	AsiaPacific2238	EcoPacific2238B	blank	104500	104500	93750	93750	4475	4475	81	Agricultural	795.5	AI-Compliance-1
SH-RT-0021A4B	weather	89.2	JPN	2024-05-09T16:33:12Z	VES-41111	RT-7708	VAN	AsiaNAmerica7708	EcoNAmerica7708C	High winds, moderate seas	121000	126100	98400	104000	4800	5060	83	Automobiles	318.1	AI-WeatherSense-2
SH-RT-0021A4C	cost	91	SFO	2024-05-09T18:14:07Z	VES-23879	RT-1010	YOK	PacificLoop1010	EcoPacific1010E	blank	99000	106000	82500	86000	4980	5200	80.6	Consumer Goods	522	HumanOperator-2
SH-RT-0021A4D	sustainability	77.1	RTM	2024-05-10T08:22:03Z	VES-30177	RT-4025	NYC	EuroAmerica4025	GreenAtlantic4025B	blank	84800	89200	62500	66000	3550	3700	70.9	Furniture	312.5	AI-OptiRoute-5
SH-RT-0021A4E	cost	90	LAX	2024-05-10T09:31:05Z	VES-10981	RT-4521	SHA	PacificDirect4521	EcoPacific4521C	blank	129700	142500	87600	95000	6170	6350	81	Electronics	479.7	HumanOperator-3
SH-RT-0021A4F	regulatory	78.1	RTM	2024-05-10T11:15:19Z	VES-51230	RT-1025	HAM	EuroLoop1025	EcoEuro1025D	blank	61400	61730	48400	49500	420	440	78.1	Machinery	215.2	AI-Compliance-1
SH-RT-0021A50	sustainability	94	DXB	2024-05-10T13:21:47Z	VES-33490	RT-6392	SIN	MidEastAsia6392	EcoAsia6392D	blank	116400	121200	76000	77600	3900	3895	85	Textiles	311.9	AI-OptiRoute-6
SH-RT-0021A51	other	81	HKG	2024-05-10T14:32:16Z	VES-12890	RT-2238	SYD	AsiaPacific2238	EcoPacific2238C	blank	104500	104500	93750	93750	4475	4475	81	Agricultural	801	HumanOperator-1
SH-RT-0021A52	weather	90.2	JPN	2024-05-10T16:44:59Z	VES-41111	RT-7708	VAN	AsiaNAmerica7708	EcoNAmerica7708D	Dense fog, slight drizzle	124000	126100	99000	102300	4882	4975	88	Automobiles	322.2	AI-WeatherSense-3

What the 86 rows show

from the 86-row sample

Regulatory (reason for adjustment) stands out: mean sustainability_score is 67.9, against 82.0 for the rest.

81.0median sustainability_score
56,260median emissions_estimate_kgco2
56,700median previous_emissions_kgco2
62,300median cost_estimate_usd
66,000median previous_cost_usd
3,591median distance_nm

Mean sustainability_score by reason_for_adjustment86 rows

sustainability_score86 rows, in bands of 10

Median 81.0, from 51.7 to 99.0.

origin_port_code86 rows · top 10 of 35 values

RTM12
LAX7
DXB7
HKG6
JPN5
SFO5
NYC3
SIN3
AMS3
LHR3

21 columns by typefrom the column list below

string 11
float 9
datetime 1

Columns

21 columns in three groups

blueprint · 21 columns
column	type	description	example
Text 11 columns
`adjustment_id`	string	Unique identifier for each route adjustment eventunique	SH-RT-0021A3F
`vessel_id`	string	Unique identifier for the vessel making the adjustment	VES-10981
`route_id`	string	Unique identifier for the shipping route being adjusted	RT-4521
`origin_port_code`	string	Standard code for the origin port	LAX
`destination_port_code`	string	Standard code for the destination port	SHA
`previous_route`	string	Description or code of the route prior to adjustment	PacificDirect4521
`adjusted_route`	string	Description or code of the route after adjustment	EcoPacific4521A
`reason_for_adjustment`	string	Primary reason for the route adjustment (e.g., weather, sustainability, cost)weather · sustainability · cost · regulatory · other	sustainability
`weather_condition`	string	Summary of weather conditions at time of adjustmentoptional	Typhoon warning
`cargo_type`	string	Type of cargo being shippedoptional	Electronics
`adjustment_made_by`	string	Identifier for the AI system or human operator making the adjustment	AI-OptiRoute-5
Numbers 9 columns
`emissions_estimate_kgco2`	float	Estimated CO2 emissions (kg) for the adjusted route0 or more	125000.56
`previous_emissions_kgco2`	float	Estimated CO2 emissions (kg) for the previous route0 or more	143210
`cost_estimate_usd`	float	Estimated shipping cost (USD) for the adjusted route0 or more	89000.5
`previous_cost_usd`	float	Estimated shipping cost (USD) for the previous route0 or more	96000
`distance_nm`	float	Distance of the adjusted route in nautical miles0 or more	6180.7
`previous_distance_nm`	float	Distance of the previous route in nautical miles0 or more	6352.9
`sustainability_score`	float	Score representing sustainability of the adjusted route (0-100)0 to 100	92.3
`previous_sustainability_score`	float	Score representing sustainability of the previous route (0-100)0 to 100	79.1
`cargo_weight_tons`	float	Weight of cargo in metric tons0 or more · optional	450.8
Dates and times 1 column
`timestamp`	datetime	Date and time when the route adjustment was made	2024-05-08T09:17:32Z

Use it for

A logistics dashboard
Sustainability_score by reason_for_adjustment and a breakdown of origin_port_code. Excel, Power BI or Tableau.
Why do the 11 regulatory rows have a mean sustainability_score of 67.9?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Adjustments86
SH-RT-0021A3F92.3sustaina…
SH-RT-0021A4085.6weather
SH-RT-0021A4178.1cost
A software demo
Believable adjustments with timestamp, vessel_id and route_id to fill a screen in front of a buyer.

This dataset86 rows21 columns

Yours1,000 rows21 columns

blueprint · ai-driven-shipping-route-adjustments

Behind this dataset

Same schema. As many rows as you need.

These 86 rows came out of a blueprint — 21 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 record represents a shipping route change event with before-and-after details.
Adjustments must consider at least two factors: emissions, cost, weather, or delivery urgency.
ESG compliance score calculated for each adjustment based on emissions reduction.
Routes flagged if they result in delivery delays exceeding preset thresholds.

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: ai-driven-shipping-route-adjustments

What should your data show?

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Sample rows

What the 86 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?