Warehouse Picking Efficiency Metrics
This dataset provides granular metrics on warehouse picking efficiency, including picker performance, item details, location, timing, and error tracking for each picking event. It enables logistics teams to analyze labor productivity, identify bottlenecks, and optimize warehouse operations for cost reduction and improved order fulfillment accuracy.
Sample rows
preview · 8 of 200 rows · all 19 columns| pick_idstring | error_typestring | pick_accuracyfloat | errors_flaggedboolean | location_zonestring | picker_idstring | picker_namestring | pick_datedate | pick_start_timedatetime | pick_end_timedatetime | item_idstring | item_namestring | skustring | quantity_pickedinteger | location_idstring | pick_duration_secondsfloat | order_idstring | supervisor_idstring | shiftstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PICK0001 | none | 99.5 | false | Zone A | EMP101 | Ava Thompson | 2024-06-03 | 2024-06-03T07:05:23 | 2024-06-03T07:07:11 | ITM1001 | Widget Pro Small | WPRO-SM | 2 | LOC-A01 | 108 | ORD201 | SUP01 | morning |
| PICK0002 | none | 100 | false | Zone B | EMP102 | Mason Lee | 2024-06-03 | 2024-06-03T08:22:55 | 2024-06-03T08:23:35 | ITM1002 | Widget Standard | WSTD-01 | 1 | LOC-B12 | 40 | ORD202 | SUP02 | morning |
| PICK0003 | none | 98.9 | false | Zone C | EMP103 | Sophia Patel | 2024-06-03 | 2024-06-03T09:15:10 | 2024-06-03T09:16:45 | ITM1003 | Toolset Compact | TCMP-02 | 3 | LOC-C05 | 95 | ORD203 | SUP03 | morning |
| PICK0004 | none | 99.2 | false | Zone D | EMP104 | Jackson Miller | 2024-06-03 | 2024-06-03T10:42:11 | 2024-06-03T10:43:09 | ITM1004 | Toolset Large | TLRG-04 | 1 | LOC-D23 | 58 | ORD204 | SUP04 | morning |
| PICK0005 | none | 98.7 | false | Zone E | EMP105 | Amelia Chen | 2024-06-03 | 2024-06-03T11:18:32 | 2024-06-03T11:21:21 | ITM1005 | Widget Pro Medium | WPRO-MD | 5 | LOC-E14 | 169 | ORD205 | SUP05 | morning |
| PICK0006 | none | 99.8 | false | Zone F | EMP106 | Lucas Davis | 2024-06-03 | 2024-06-03T12:07:10 | 2024-06-03T12:09:25 | ITM1006 | Widget XL | WXL-07 | 2 | LOC-F22 | 135 | ORD206 | SUP02 | afternoon |
| PICK0007 | none | 99.4 | false | Zone G | EMP107 | Ella Garcia | 2024-06-03 | 2024-06-03T13:22:30 | 2024-06-03T13:23:52 | ITM1007 | Toolset Mini | TMIN-11 | 2 | LOC-G09 | 82 | ORD207 | SUP03 | afternoon |
| PICK0008 | none | 98.6 | false | Zone H | EMP108 | blank | 2024-06-03 | 2024-06-03T13:48:05 | 2024-06-03T13:48:25 | ITM1008 | Widget Micro | blank | 1 | LOC-H03 | 20 | ORD208 | SUP01 | afternoon |
| PICK0009 | none | 99.9 | false | Zone I | EMP109 | Benjamin Hall | 2024-06-03 | 2024-06-03T14:15:42 | 2024-06-03T14:28:05 | ITM1009 | Bulk Screws | BSCR-1000 | 1000 | LOC-I30 | 743 | ORD209 | SUP05 | afternoon |
| PICK0010 | none | 99.7 | false | Zone J | EMP110 | Mia Nelson | 2024-06-03 | 2024-06-03T14:45:10 | 2024-06-03T14:46:32 | ITM1010 | Bulk Washers | BWAS-500 | 500 | LOC-J38 | 82 | ORD210 | SUP04 | afternoon |
| PICK0011 | mis-pick | 0 | true | Zone K | EMP111 | Henry Young | 2024-06-03 | 2024-06-03T15:30:22 | 2024-06-03T15:30:22 | ITM1011 | Widget Pro Small | WPRO-SM | 1 | LOC-K11 | 0 | blank | blank | afternoon |
| PICK0012 | short-pick | 84.7 | true | Zone L | EMP112 | William King | 2024-06-03 | 2024-06-03T16:07:41 | 2024-06-03T16:08:11 | ITM1012 | Toolset Compact | TCMP-02 | 1 | LOC-L04 | 30 | ORD211 | blank | afternoon |
| PICK0013 | location-error | 89.2 | true | blank | EMP113 | blank | 2024-06-03 | 2024-06-03T16:24:11 | 2024-06-03T16:25:03 | ITM1013 | Widget Standard | blank | 1 | LOC-M15 | 52 | blank | SUP02 | afternoon |
| PICK0014 | none | 98.5 | false | Zone N | EMP114 | James Wright | 2024-06-03 | 2024-06-03T17:01:20 | 2024-06-03T17:04:55 | ITM1014 | Toolset Large | TLRG-04 | 7 | LOC-N21 | 215 | ORD212 | SUP05 | afternoon |
| PICK0015 | none | 99.6 | false | Zone O | EMP115 | Grace Lopez | 2024-06-03 | 2024-06-03T18:12:09 | 2024-06-03T18:15:11 | ITM1015 | Bulk Screws | BSCR-1000 | 1000 | LOC-O08 | 182 | ORD213 | SUP01 | night |
| PICK0016 | none | 99.5 | false | Zone P | EMP116 | Elijah Smith | 2024-06-03 | 2024-06-03T19:28:49 | 2024-06-03T19:32:15 | ITM1016 | Bulk Washers | BWAS-500 | 500 | LOC-P25 | 206 | blank | blank | night |
| PICK0017 | none | 98.8 | false | Zone Q | EMP117 | Victoria Martinez | 2024-06-03 | 2024-06-03T20:17:08 | 2024-06-03T20:19:10 | ITM1017 | Widget Pro Medium | WPRO-MD | 5 | LOC-Q31 | 122 | ORD214 | blank | night |
| PICK0018 | none | 97.5 | false | blank | EMP118 | Alexander Scott | 2024-06-03 | 2024-06-03T21:22:01 | 2024-06-03T21:24:10 | ITM1018 | Toolset Mini | TMIN-11 | 2 | LOC-R18 | 129 | blank | blank | night |
| PICK0019 | none | 97.3 | false | blank | EMP119 | Harper Rivera | 2024-06-03 | 2024-06-03T22:16:41 | 2024-06-03T22:18:11 | ITM1019 | Widget XL | WXL-07 | 2 | LOC-S55 | 90 | blank | blank | night |
| PICK0020 | none | 99 | false | Zone T | EMP120 | Ethan Clark | 2024-06-03 | 2024-06-03T23:03:20 | 2024-06-03T23:09:10 | ITM1020 | Toolset Large | TLRG-04 | 8 | LOC-T37 | 350 | ORD215 | blank | night |
What the 200 rows show
from the 200-row sampleNone (error type) stands out: mean pick_
- 16%errors_
flagged = true - 99.0median pick_
accuracy - 3shifts
- 36supervisors
- 2median quantity_
picked - 121median pick_
duration_ seconds
Median 99.0, from 0.0 to 100.0.
- string 12
- integer 1
- float 2
- date 1
- datetime 2
- boolean 1
Columns
19 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 12 columns | |||
pick_id | string | Unique identifier for each picking event or transaction.unique | PICK0001 |
picker_id | string | Unique identifier for the warehouse employee performing the pick. | EMP101 |
picker_name | string | Full name of the picker.optional | Ava Thompson |
item_id | string | Unique identifier for the item being picked. | ITM1001 |
item_name | string | Name or description of the item being picked.optional | Widget Pro Small |
sku | string | Stock Keeping Unit code for the item.optional | WPRO-SM |
location_id | string | Unique identifier for the warehouse location where the item was picked. | LOC-A01 |
location_zone | string | Zone or area within the warehouse where the item was picked.optional | Zone A |
error_type | string | Type of error encountered during picking (if any).6 values · optional | none |
order_id | string | Unique identifier for the customer order associated with the pick.optional | ORD201 |
supervisor_id | string | Unique identifier for the supervisor overseeing the picking event.optional | SUP01 |
shift | string | Warehouse shift during which the pick occurred (e.g., morning, afternoon, night).morning · afternoon · night · optional | morning |
| Numbers 3 columns | |||
quantity_picked | integer | Number of units picked in this event.1 or more | 2 |
pick_duration_seconds | float | Total time taken to pick the item, measured in seconds.0 or more | 108 |
pick_accuracy | float | Accuracy of the pick, expressed as a percentage (0-100).0 to 100 · optional | 99.5 |
| Dates and times 3 columns | |||
pick_date | date | Date when the picking event occurred. | 2024-06-03 |
pick_start_time | datetime | Timestamp when the picking process started. | 2024-06-03T07:05:23 |
pick_end_time | datetime | Timestamp when the picking process ended. | 2024-06-03T07:07:11 |
| True or false 1 column | |||
errors_flagged | boolean | Indicates if any errors were flagged during the picking event. | false |
Use it for
A logistics dashboard
The errors_
flagged rate, pick_ accuracy by error_ type and a breakdown of location_ zone. Excel, Power BI or Tableau. Why do the 169 none rows have a mean pick_
accuracy of 99.2? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Picks200PICK000199.5nonePICK00110mis-pickPICK001284.7short-pi…
A software demo
Believable picks with picker_
id, picker_ name and pick_ date to fill a screen in front of a buyer.
This dataset200 rows19 columns
Yours1,000 rows19 columnslocation_id: UK only
blueprint · warehouse-picking-efficiency-metrics
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 19 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
- Record time spent per pick
- Segment by picker and item type
- Flag mis-picks and errors
- Include workload distribution
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
- warehouse-picking-efficiency-metrics