Shipment Delay Root Causes

This dataset provides granular details on shipment delays within a logistics network, including root causes, delay durations, responsible parties, and SLA violations. It enables deep analysis of delay patterns, supports process optimization, and helps drive compliance with service level agreements. The dataset is ideal for logistics performance monitoring, root cause analysis, and continuous improvement initiatives.

  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Root cause analysis of shipment delays

Sample rows

preview · 8 of 110 rows · all 23 columns
shipment_idstringshipment_modestringshipment_valuefloatsla_violatedbooleanorigin_countrystringdelay_idstringshipment_datedateactual_shipment_datedateexpected_delivery_datedateactual_delivery_datedatedelay_duration_hoursfloatdelay_root_causestringdelay_sub_causestringresponsible_partystringorigin_citystringorigin_statestringdestination_citystringdestination_statestringdestination_countrystringcarrier_namestringsla_typestringcustomer_idstringdelay_descriptionstring
SHIP001Road11800falseGermanyDELAY00012022-02-142022-02-152022-02-202022-02-200Traffic CongestionblankCarrierFrankfurtblankZurichblankSwitzerlandEuroFreight LogisticsOn-Time DeliveryCUST001blank
SHIP002Road14200trueItalyDELAY00022023-03-012023-03-012023-03-072023-03-0948WeatherSnowstorm in AlpsCarrierMilanblankMunichblankGermanyNordic Haulage Co.GuaranteedCUST002Heavy snowfall caused road closures along the main route.
SHIP003Road18350truePolandDELAY00032024-01-182024-01-182024-01-202024-01-2124Traffic CongestionBorder crossing delaysCarrierWarsawblankPragueblankCzechiaTransPol RoadwaysOn-Time DeliveryCUST003Congestion at border checkpoint delayed customs processing.
SHIP004Road27900falseUSADELAY00042023-12-032023-12-042023-12-102023-12-100Documentation ErrorblankShipperNew YorkNYHoustonTXUSAAmeriTransportOn-Time DeliveryCUST004blank
SHIP005Road13600falseSpainDELAY00052024-03-212024-03-212024-03-272024-03-270OtherblankThird-PartyBarcelonablankParisblankFranceIberia Road ServicesOn-Time DeliveryCUST005blank
SHIP006Road24800trueCanadaDELAY00062023-09-122023-09-122023-09-192023-09-2024Documentation ErrorIncorrect invoice detailsShipperTorontoONChicagoILUSAMapleLeaf LogisticsGuaranteedCUST006Invoice discrepancies required additional review by customs.
SHIP007Road15700falseBelgiumDELAY00072022-07-112022-07-112022-07-182022-07-180WeatherblankCarrierBrusselsblankAmsterdamblankNetherlandsBenelux FreightwaysOn-Time DeliveryCUST007blank
SHIP008Road21100falseUSADELAY00082024-05-012024-05-022024-05-082024-05-080Documentation ErrorblankShipperLos AngelesCASeattleWAUSAWestCoast CarriersOn-Time DeliveryCUST008blank

What the 110 rows show

from the 110-row sample

Sea (shipment mode) stands out: mean shipment_value is 286,037, against 28,932 for the rest.

  • 39%sla_violated = true
  • 21,100median shipment_value
  • 3sla types
  • 5responsible parties
  • 9delay root causes
  • 0median delay_duration_hours
Mean shipment_value by shipment_mode107 rows
0200k400k97,711Air9 rows286kSea21 rows27,780Rail15 rows17,808Road57 rows35,400Multim…5 rows
shipment_value107 rows, in bands of 100k
045908411423210400k700kshipment_value →

Median 21,100, from 9,400 to 690,000.

origin_country110 rows · top 10 of 39 values
  1. USA12
  2. Germany8
  3. India8
  4. Canada6
  5. Spain5
  6. Australia5
  7. France5
  8. South Africa5
  9. Italy4
  10. Belgium4
23 columns by typefrom the column list below
  • string 16
  • float 2
  • date 4
  • boolean 1

Columns

23 columns in four groups
blueprint · 23 columns
columntypedescriptionexample
Text 16 columns
shipment_idstringUnique identifier for each shipmentuniqueSHIP001
delay_idstringUnique identifier for each delay eventuniqueDELAY0001
delay_root_causestringPrimary root cause of the shipment delay9 valuesTraffic Congestion
delay_sub_causestringMore specific sub-cause of the delay, if applicableoptionalSnowstorm in Alps
delay_descriptionstringDetailed description of the delay event and contributing factorsoptionalHeavy snowfall caused roa…
responsible_partystringParty responsible for the delay (e.g., Carrier, Shipper, Consignee, Customs, Third-Party)6 valuesCarrier
origin_citystringCity where the shipment originatedFrankfurt
origin_statestringState or province where the shipment originatedoptionalNY
origin_countrystringCountry where the shipment originatedGermany
destination_citystringCity where the shipment is to be deliveredZurich
destination_statestringState or province where the shipment is to be deliveredoptionalTX
destination_countrystringCountry where the shipment is to be deliveredSwitzerland
shipment_modestringMode of transportation used for the shipmentAir · Sea · Rail · Road · MultimodalRoad
carrier_namestringName of the carrier or logistics provider handling the shipmentoptionalEuroFreight Logistics
sla_typestringType of SLA impacted (e.g., On-Time Delivery, Expedited, Guaranteed, etc.)3 types · optionalOn-Time Delivery
customer_idstringUnique identifier for the customer associated with the shipmentoptionalCUST001
Numbers 2 columns
delay_duration_hoursfloatTotal delay duration in hours (actual_delivery_date - expected_delivery_date)0 or more0
shipment_valuefloatMonetary value of the shipment in local currency0 or more · optional11800
Dates and times 4 columns
shipment_datedateDate the shipment was originally scheduled to be shipped2022-02-14
actual_shipment_datedateDate the shipment actually left the origin locationoptional2022-02-15
expected_delivery_datedateDate the shipment was expected to be delivered according to SLA2022-02-20
actual_delivery_datedateDate the shipment was actually deliveredoptional2022-02-20
True or false 1 column
sla_violatedbooleanIndicates if the delay resulted in a Service Level Agreement (SLA) violationfalse

Use it for

  • sla violated39%43 of 110 rowsmean shipment value b…97.7kAir286kSea27.8kRail17.8kRoad

    A logistics dashboard

    The sla_violated rate, shipment_value by shipment_mode and a breakdown of origin_country. Excel, Power BI or Tableau.

  • Why do the 21 Sea rows have a mean shipment_value of 286,037?

    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 shipments with delay_id, shipment_date and actual_shipment_date to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 23 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset110 rows23 columns
Yours1,000 rows23 columnsorigin_city: UK only

blueprint · shipment-delay-root-causes

Behind this dataset

Same schema. As many rows as you need.

These 110 rows came out of a blueprint — 23 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 origin, destination, and carrier for each shipment
  • List delay reason from predefined categories
  • Include duration and impact of delay
  • Flag delays exceeding 24 hours
  • Aggregate delay frequency per carrier
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
shipment-delay-root-causes

More in Logistics

Browse all
Also in LogisticsGovernment Procurement Logistics TrackerCold Chain Logistics Temperature LogsPharmaceutical Inventory Supply ChainsAll 1,966 datasets

What should your data show?

Preview 20 rows free
No signup. No card.