Warehouse Throughput and Efficiency Stats
This dataset provides daily operational performance statistics for warehouses, including throughput, downtime, and key efficiency metrics such as order fulfillment rate and equipment utilization. It enables granular analysis of logistics operations, identification of bottlenecks, and optimization of warehouse processes for improved supply chain performance.
Sample rows
preview · 8 of 200 rows · all 15 columns| warehouse_idstring | shiftstring | order_fulfillment_ratefloat | downtime_reason_primarystring | warehouse_namestring | datedate | total_throughput_unitsinteger | orders_processedinteger | operational_hoursfloat | downtime_minutesinteger | labor_hoursfloat | error_ratefloat | average_pick_time_secondsfloat | equipment_utilization_ratefloat | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| WH101 | morning | 99.2 | blank | RoboSite International DC | 2024-03-01 | 15800 | 4100 | 9 | 0 | 62.5 | 0.2 | 12.8 | 97.2 | blank |
| WH102 | afternoon | 97.8 | equipment maintenance | Central Distribution Hub | 2024-03-01 | 11100 | 2330 | 8.5 | 12 | 42 | 0.6 | 18.1 | 85.6 | blank |
| WH103 | night | 80.3 | staffing | Express MiniHub South | 2024-03-01 | 280 | 56 | 2.5 | 22 | 7 | 7.2 | 65.5 | 22.9 | Low staff on shift |
| WH104 | afternoon | 98.9 | blank | Mega North Warehouse | 2024-03-02 | 24800 | 6200 | 14.5 | 0 | 110.7 | 0.1 | 11.3 | 98.1 | Peak volume |
| WH105 | morning | 78.5 | equipment failure | Outbound Returns Facility | 2024-03-02 | 105 | 18 | 2 | 30 | 3.8 | 13 | 120 | 18.3 | Returns backlog |
| WH106 | night | 0 | power outage | Satellite Depot East | 2024-03-02 | 0 | 0 | 0 | 105 | 0 | 0 | blank | 0 | No operations possible |
| WH107 | afternoon | 95.6 | blank | Bulk Facility West | 2024-03-03 | 8200 | 1820 | 7.5 | 0 | 36.6 | 1.1 | 20.5 | 78.2 | blank |
| WH108 | morning | 99 | system outage | Regional Mega DC | 2024-03-03 | 19200 | 5050 | 10 | 8 | 54.5 | 0.3 | 13.6 | 95.9 | Brief system check |
| WH109 | night | 0 | planned shutdown | Temp Storage Overflow | 2024-03-04 | 0 | 0 | 1.5 | 55 | 2 | 0 | blank | 0 | Planned shutdown |
| WH110 | morning | 96.1 | blank | Outbound Distribution Hub | 2024-03-04 | 7200 | 1980 | 8 | 0 | 33.5 | 1.7 | 24.2 | 74 | blank |
| WH111 | afternoon | 82.6 | equipment failure | Express MiniHub North | 2024-03-04 | 320 | 64 | 3 | 10 | 10 | 8.7 | 58 | 28.5 | blank |
| WH112 | morning | 99.5 | blank | Westside Regional DC | 2024-03-05 | 21200 | 5400 | 12 | 0 | 98 | 0 | 10.7 | 98.9 | Smooth operations |
| WH113 | night | 96.3 | staffing | Metro International Hub | 2024-03-05 | 14200 | 3700 | 10.5 | 5 | 49.1 | 0.5 | 13.2 | 93.5 | Minor staff shortage |
| WH114 | afternoon | 98.2 | blank | Central City Bulk Facility | 2024-03-06 | 10100 | 2350 | 8.5 | 0 | 38.7 | 1 | 19.9 | 80.7 | blank |
| WH115 | morning | 79.5 | system outage | Returns Processing Center | 2024-03-06 | 450 | 90 | 4.5 | 39 | 8.2 | 12.7 | 104.1 | 15.7 | Delayed returns batch |
| WH116 | morning | 99.8 | blank | Seasonal Overflow Depot | 2024-03-07 | 19600 | 5200 | 9.5 | 0 | 56.1 | 0 | 12.2 | 98.6 | Holiday rush |
| WH117 | afternoon | 95.2 | equipment maintenance | Eastside Satellite Hub | 2024-03-07 | 6370 | 1460 | 7 | 14 | 27.6 | 2 | 26.7 | 72.8 | blank |
| WH118 | night | 78.9 | equipment failure | Express MiniHub Central | 2024-03-07 | 110 | 28 | 1 | 15 | 2.7 | 10.4 | 98.7 | 11.2 | Low staff |
| WH119 | morning | 100 | blank | Metro International Hub | 2024-03-08 | 22200 | 5520 | 12.5 | 0 | 101 | 0 | 10.8 | 99.1 | Record throughput |
| WH120 | afternoon | 97.6 | blank | Northwest Distribution Depot | 2024-03-08 | 9980 | 2100 | 8.5 | 0 | 40.2 | 1.9 | 22.5 | 80.2 | blank |
What the 200 rows show
from the 200-row sample- 95.3median order_
fulfillment_ rate - 7,890median total_
throughput_ units - 1,952median orders_
processed - 8.1median operational_
hours - 5median downtime_
minutes - 35.6median labor_
hours
Median 95.3, from 0.0 to 100.0.
- string 5
- integer 3
- float 6
- date 1
Columns
15 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 5 columns | |||
warehouse_id | string | Unique identifier for the warehouse location | WH101 |
warehouse_name | string | Descriptive name of the warehouse | Central Distribution Hub |
shift | string | Shift during which the data was collected (e.g., morning, afternoon, night)morning · afternoon · night · optional | morning |
downtime_reason_primary | string | Primary reason for downtime (e.g., equipment failure, staffing, system outage)9 primaries · optional | equipment maintenance |
notes | string | Additional notes or comments for the day's operationsoptional | Low staff on shift |
| Numbers 9 columns | |||
total_throughput_units | integer | Total number of units processed by the warehouse on the given date and shift0 or more | 15800 |
orders_processed | integer | Number of orders processed during the period0 or more | 4100 |
operational_hours | float | Total number of operational hours for the warehouse during the period0 to 24 | 9 |
downtime_minutes | integer | Total downtime in minutes experienced during the period0 or more | 0 |
labor_hours | float | Total labor hours worked during the period0 or more | 62.5 |
order_fulfillment_rate | float | Percentage of orders fulfilled on time (0-100)0 to 100 | 99.2 |
error_rate | float | Percentage of processed units with errors (0-100)0 to 100 | 0.2 |
average_pick_time_seconds | float | Average time in seconds to pick a unit/order0 or more · optional | 12.8 |
equipment_utilization_rate | float | Percentage of equipment utilization during operational hours (0-100)0 to 100 · optional | 97.2 |
| Dates and times 1 column | |||
date | date | Date for which the operational data is recorded | 2024-03-01 |
15 SQL practice questions for the Warehouse Throughput and Efficiency Stats
Written for this data and checked against it: 12 SQL and 3 Power BI exercises from beginner to advanced. Try the first three free; the practice kit has all 15, a starter .sql file and an answer key with every expected result.
Night shift rows with heavy downtime
Return warehouse_id, date and downtime_minutes for every row where shift is 'night' and downtime_minutes is greater than 60. Order the results by downtime_minutes descending, then warehouse_id ascending, then date ascending.
Tables: warehouse_throughput_and_efficiency_stats · your result: 3 columns, 9 rowsDistinct downtime reasons
Return each distinct non-null downtime_reason_primary as a single column named downtime_reason_primary, sorted alphabetically.
Tables: warehouse_throughput_and_efficiency_stats · your result: 1 columns, 9 rowsRows and units per shift
For each shift, return shift, the number of rows as row_count, and the sum of total_throughput_units as total_units. Order the results by shift.
Tables: warehouse_throughput_and_efficiency_stats · your result: 3 columns, 3 rows
- 04Average error rate by downtime reasonBeginner
- 05Total orders by shift chartPower BI
- 06Reasons with high total downtimeIntermediate
- 07Fulfillment performance bucketsIntermediate
- 08Monthly throughputIntermediate
- 09Shifts with below-average utilizationIntermediate
- 10Downtime per operational hour by shiftPower BI
- 11Top 3 throughput rows within each shiftAdvanced
- 12Running total of daily throughputAdvanced
- 13Day-over-day change in daily ordersAdvanced
- 14Downtime share by reasonAdvanced
- 15Error rates above double the shift averagePower BI
Get the practice kit: the full dataset, all 15 exercises, a starter .sql and the answer key. One-time $3.99.
Get the practice kit · $3.99Use it for
A logistics dashboard
Order_
fulfillment_ rate by shift and a breakdown of downtime_ reason_ primary. Excel, Power BI or Tableau. What pattern can you find in 200 rows?
A class exercise
Hand out the rows and one question. Everyone works from the same 200 rows.
- Warehouses200WH10199.2morningWH10297.8afternoonWH10380.3night
A software demo
Believable warehouses with warehouse_
name, date and shift to fill a screen in front of a buyer.
blueprint · warehouse-throughput-and-efficiency-stats
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 15 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.
- Each row: warehouse, date, throughput volume
- Downtime reason and duration logged
- Efficiency calculated from staffing and automation level
- Order type and fulfillment channel included
- Peak/off-peak indicators
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
- warehouse-throughput-and-efficiency-stats