IoT Inventory Tracking Events

This dataset provides granular, real-time records of inventory movement and anomalies in educational institutions, captured via IoT sensors. It includes detailed event, item, location, and facility information, enabling facility managers and edtech startups to analyze inventory flow, detect anomalies, and optimize supply chain efficiency for resource planning and operational improvement.

  • last updated 16 Jan 2026
  • by GoMask
The brief that made it

Inventory flow analysis and optimization in educational facilities

Sample rows

preview · 8 of 78 rows · all 22 columns
event_idstringevent_typestringquantityintegeranomaly_flagbooleanlocation_namestringtimestampdatetimesensor_idstringsensor_typestringinventory_item_idstringinventory_item_namestringinventory_item_categorystringlocation_idstringlocation_street_addressstringlocation_citystringlocation_statestringlocation_postal_codestringlocation_countrystringfacility_idstringfacility_namestringoperator_idstringoperator_namestringanomaly_descriptionstring
EVT-001-AQXZcheck-in5falseRoom 1012024-05-14T08:45:32SEN-101-RFIDRFIDINV-301-LAPTOPDell Latitude 5410 LaptopelectronicsLOC-201-RM101123 Oak StreetSpringfieldIL62704USAFAC-001-EDUSpringfield STEM AcademyOP-12Jessica Leeblank
EVT-002-BQWScheck-out1falseLibrary Storage2024-05-14T09:15:22SEN-102-BARbarcodeINV-302-BOOKCalculus TextbookbooksLOC-202-LIBST501 Maple AvenueSpringfieldIL62705USAFAC-001-EDUSpringfield STEM AcademyOP-15Michael Torresblank
EVT-003-CRTYmovement10falseRoom 2022024-05-14T09:30:13SEN-103-WGTweightINV-303-MARKERExpo Whiteboard MarkersuppliesLOC-203-RM202123 Oak StreetSpringfieldIL62704USAFAC-001-EDUSpringfield STEM Academyblankblankblank
EVT-004-DXVBcheck-in4falseRoom 1032024-05-14T09:49:07SEN-104-INFinfraredINV-304-USBKingston 32GB USB DriveelectronicsLOC-204-RM103123 Oak StreetSpringfieldIL62704USAFAC-001-EDUSpringfield STEM AcademyOP-08Samantha Owensblank
EVT-005-EYGTanomaly1trueAV Storage2024-05-14T10:02:56SEN-105-RFIDRFIDINV-305-PROJEpson Projector EB-E20electronicsLOC-205-AVST501 Maple AvenueSpringfieldIL62705USAFAC-001-EDUSpringfield STEM AcademyOP-20Daniel PriceProjector not detected during scheduled audit
EVT-006-FWQZaudit200falseRoom 1042024-05-14T10:27:41SEN-106-BARbarcodeINV-306-PAPERA4 Printer PapersuppliesLOC-206-RM104123 Oak StreetSpringfieldIL62704USAFAC-001-EDUSpringfield STEM AcademyOP-05Linda Chenblank
EVT-007-GLTBmovement15falseRoom 2022024-05-14T10:31:25SEN-107-WGTweightINV-307-GLUEElmer's Glue SticksuppliesLOC-207-RM202123 Oak StreetSpringfieldIL62704USAFAC-001-EDUSpringfield STEM Academyblankblankblank
EVT-008-HXRNcheck-out2falseRoom 1052024-05-14T10:48:59SEN-108-INFinfraredINV-308-TABLETSamsung Galaxy Tab A7electronicsLOC-208-RM105123 Oak StreetSpringfieldIL62704USAFAC-001-EDUSpringfield STEM AcademyOP-17Ryan Patelblank

What the 78 rows show

from the 78-row sample

Audit (event type) stands out: mean quantity is 66.7, against 4.2 for the rest.

  • 12%anomaly_flag = true
  • 2median quantity
  • 3inventory item categories
  • 3location states
  • 4location cities
  • 4facilities
Mean quantity by event_type78 rows
040803.0check-…22 rows1.7check-…18 rows10.6moveme…16 rows1.0anomaly9 rows66.7audit13 rows
quantity78 rows, in bands of 20
03570683011211010100200quantity →

Median 2, from 1 to 200.

location_name78 rows · top 10 of 15 values
  1. IT Room11
  2. Library Storage7
  3. Room 2027
  4. AV Storage7
  5. Room 1047
  6. Library Main7
  7. Supply Closet7
  8. Science Lab6
  9. Media Room4
  10. Math Room4
22 columns by typefrom the column list below
  • string 19
  • integer 1
  • datetime 1
  • boolean 1

Columns

22 columns in four groups
blueprint · 22 columns
columntypedescriptionexample
Text 19 columns
event_idstringUnique identifier for each IoT inventory tracking eventuniqueEVT-001-AQXZ
sensor_idstringUnique identifier for the IoT sensor reporting the eventSEN-101-RFID
sensor_typestringType of IoT sensor (e.g., RFID, barcode, weight sensor)RFID · barcode · weight · infrared · otherRFID
inventory_item_idstringUnique identifier for the inventory item being trackedINV-301-LAPTOP
inventory_item_namestringHuman-readable name of the inventory itemCalculus Textbook
inventory_item_categorystringCategory or type of inventory item (e.g., electronics, books, supplies)3 categorieselectronics
event_typestringType of inventory event (e.g., check-in, check-out, movement, anomaly)check-in · check-out · movement · anomaly · auditcheck-in
location_idstringUnique identifier for the location where the event occurredLOC-201-RM101
location_namestringHuman-readable name of the location (e.g., Room 101, Library Storage)Room 101
location_street_addressstringStreet address of the location8 addresses · optional123 Oak Street
location_citystringCity where the location is situated4 cities · optionalSpringfield
location_statestringState or province of the location3 states · optionalIL
location_postal_codestringPostal code for the location6 codes · optional62704
location_countrystringCountry of the locationoptionalUSA
facility_idstringUnique identifier for the educational facility4 facilitiesFAC-001-EDU
facility_namestringHuman-readable name of the educational facility4 namesSpringfield STEM Academy
anomaly_descriptionstringDescription of the anomaly detected (if any)optionalProjector not detected du…
operator_idstringIdentifier for the staff or operator who validated or triggered the eventoptionalOP-12
operator_namestringName of the staff or operator involved in the eventoptionalJessica Lee
Numbers 1 column
quantityintegerNumber of items involved in the event1 or more5
Dates and times 1 column
timestampdatetimeDate and time when the event was recorded by the IoT sensor2024-05-14T08:45:32
True or false 1 column
anomaly_flagbooleanIndicates if the event is flagged as an anomalyfalse

Use it for

  • anomaly flag12%9 of 78 rowsmean quantity by even…3.0chec…1.7chec…10.6move…1.0anom…

    An education dashboard

    The anomaly_flag rate, quantity by event_type and a breakdown of location_name. Excel, Power BI or Tableau.

  • Why do the 13 audit rows have a mean quantity of 66.7?

    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 events with timestamp, sensor_id and sensor_type to fill a screen in front of a buyer.

Not quite right?

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This dataset78 rows22 columns
Yours1,000 rows22 columnslocation_id: UK only

blueprint · iot-inventory-tracking-events

Behind this dataset

Same schema. As many rows as you need.

These 78 rows came out of a blueprint — 22 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 event includes a unique sensor ID and timestamp.
  • Inventory item ID must link to a standardized catalog.
  • Events capture movement type: in, out, relocated, or audit.
  • Anomalies (e.g., missing items) are flagged with a status code.
  • Entries include location zone within the institution.
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
iot-inventory-tracking-events

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