Supply Chain Warehouse Temperature Logs

This dataset provides granular, hourly temperature and humidity logs from supply chain warehouses, including sensor metadata, location details, and compliance status. It is ideal for regulatory audits, anomaly detection, and benchmarking IoT sensor performance across diverse warehouse environments.

  • last updated 21 Aug 2025
  • by GoMask
The brief that made it

Regulatory compliance monitoring for warehouse temperature and humidity

Sample rows

preview · 8 of 200 rows · all 17 columns
log_idstringcompliance_statusstringhumidity_percentfloatis_anomalybooleanwarehouse_statestringwarehouse_idstringwarehouse_namestringwarehouse_street_addressstringwarehouse_citystringwarehouse_postal_codestringwarehouse_countrystringzone_idstringsensor_idstringsensor_typestringtimestampdatetimetemperature_celsiusfloatnotesstring
LOG0001compliant38.4falseILWH_A1Chicago Cold Storage4800 N Central AveChicago60630USACOLD_01SENS1001TempSense Pro V22024-06-01T08:00:00Z-18.2blank
LOG0002compliant21.6falseMOWH_B2St. Louis Pharma Depot150 S BroadwaySt Louis63102USAPHARMA_FRZSENS1002T-Guard RX2024-06-01T09:00:00Z-40.1blank
LOG0003compliant13.2falseBCWH_C3Vancouver Dry Warehouse821 Main StVancouverV5K2G8CanadaDRY_03SENS1003EnviroTrack Mini2024-06-01T10:00:00Z22.7blank
LOG0004compliant51.9falseHHWH_D4Hamburg Grain TerminalAm Hafen 2Hamburg20457GermanyGRAIN_04SENS1004ThermoX Basic2024-06-01T11:00:00Z39.7blank
LOG0005non-compliant10.4trueNSWWH_E5Sydney Bulk Facility17 Botany RdSydney2017AustraliaBULK_05SENS1005ThermoX Advanced2024-06-01T12:00:00Z55.1Extreme temperature spike detected.
LOG0006compliant37.2falseMHWH_F6Mumbai Food Park401 Marine DrMumbai400020IndiaDRY_06SENS1006EnviroTrack Mini2024-06-01T13:00:00Z27.8blank
LOG0007compliant99.1falseNOWH_G7Bergen Seafood StorageBryggen 1Bergen5003NorwaySEAFOOD_07SENS1007CoolCheck 5002024-06-01T14:00:00Z2.9blank
LOG0008compliant90.3falseIDFWH_H8Paris Dairy Distribution39 Rue de LyonParis75012FranceDAIRY_08SENS1008ThermoX Advanced2024-06-01T15:00:00Z6.2blank

What the 200 rows show

from the 200-row sample

Pending (compliance status) stands out: 27 of its 27 rows have is_anomaly = true, against 20 of 173 for the rest.

  • 24%is_anomaly = true
  • 38.8median humidity_percent
  • 6sensor types
  • 11warehouse countries
  • 43warehouse cities
  • 7.3median temperature_celsius
Is anomaly rate by compliance_statusis_anomaly = true
0%50%100%0%compliant0 of 153100%non-compliant20 of 20100%pending27 of 27

Pending (compliance status)'s mean humidity_percent is 17.4, against 59.3 for compliant and 11.4 for non-compliant.

humidity_percent196 rows, in bands of 10
035701862146121052760050100humidity_percent →

Median 38.8, from 2.1 to 100.0.

warehouse_state200 rows · top 10 of 39 values
  1. IL14
  2. IDF11
  3. WC10
  4. ENG10
  5. CO10
  6. ZH10
  7. HH9
  8. MO8
  9. NSW8
  10. BE8
17 columns by typefrom the column list below
  • string 13
  • float 2
  • datetime 1
  • boolean 1

Columns

17 columns in four groups
blueprint · 17 columns
columntypedescriptionexample
Text 13 columns
log_idstringUnique identifier for each temperature log entryuniqueLOG0001
warehouse_idstringUnique identifier for the warehouse where the log was recordedWH_A1
warehouse_namestringName of the warehouseoptionalChicago Cold Storage
warehouse_street_addressstringStreet address of the warehouseoptional4800 N Central Ave
warehouse_citystringCity where the warehouse is locatedoptionalChicago
warehouse_statestringState or region of the warehouse locationoptionalIL
warehouse_postal_codestringPostal code of the warehouseoptional60630
warehouse_countrystringCountry where the warehouse is located11 countries · optionalUSA
zone_idstringIdentifier for the specific zone or area within the warehouse (e.g., cold storage, dry goods)optionalCOLD_01
sensor_idstringUnique identifier for the IoT temperature sensorSENS1001
sensor_typestringType or model of the temperature sensor6 types · optionalTempSense Pro V2
compliance_statusstringCompliance status for the reading (e.g., compliant, non-compliant, pending)compliant · non-compliant · pending · optionalcompliant
notesstringAdditional notes or comments about the log entry (e.g., manual override, sensor maintenance)optionalExtreme temperature spike…
Numbers 2 columns
temperature_celsiusfloatRecorded temperature in degrees Celsius-50 to 100-18.2
humidity_percentfloatRecorded relative humidity as a percentage0 to 100 · optional38.4
Dates and times 1 column
timestampdatetimeDate and time when the temperature reading was taken (ISO 8601 format)2024-06-01T08:00:00Z
True or false 1 column
is_anomalybooleanFlag indicating whether the reading is considered an anomaly based on compliance rules or thresholdsoptionalfalse

Use it for

  • is anomaly24%47 of 200 rowsmean humidity percent…59.3compli…11.4non-co…17.4pending

    A logistics dashboard

    The is_anomaly rate, humidity_percent by compliance_status and a breakdown of warehouse_state. Excel, Power BI or Tableau.

  • Why do 47 of 200 rows have is_anomaly = true?

    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 logs with warehouse_id, warehouse_name and warehouse_street_address to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 17 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 dataset200 rows17 columns
Yours1,000 rows17 columnswarehouse_street_address: UK only

blueprint · supply-chain-warehouse-temperature-logs

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 17 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
  • Warehouse ID per record
  • Temperature range -10°C to 35°C
  • Anomaly events injected at 1.5%
  • Sensor calibration flag included
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-warehouse-temperature-logs

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