Warehouse Picking Path Records
This dataset provides granular records of warehouse picking paths, detailing order information, picker identity, timing, distance, efficiency, and errors encountered. It enables deep analysis of picker performance and route optimization, supporting both human and robotic operations for logistics efficiency and robotics simulation. The flat structure ensures compatibility with analytics platforms and easy integration into warehouse management systems.
Sample rows
preview · 8 of 200 rows · all 15 columns| picking_path_idstring | picker_typestring | errors_encounteredinteger | notesstring | order_idstring | picker_idstring | start_datetimedatetime | end_datetimedatetime | total_distance_metersfloat | total_time_secondsfloat | warehouse_idstring | zone_sequencestring | item_countinteger | pick_sequencestring | path_efficiency_scorefloat |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PPATH0001 | human | 0 | blank | ORD10001 | PICKER01 | 2024-03-12T08:13:20Z | 2024-03-12T08:17:55Z | 95.2 | 275 | WAREA | A1,B2 | 7 | A1-005,B2-021,B2-019,A1-002,B2-015,A1-009,B2-004 | 0.77 |
| PPATH0002 | robot | 0 | blank | ORD10002 | RB-102 | 2024-03-12T09:03:08Z | 2024-03-12T09:05:18Z | 110.5 | 130 | ROBOT-1 | Z3,Z7 | 5 | Z3-101,Z7-212,Z7-200,Z7-199,Z3-110 | 0.93 |
| PPATH0003 | cobot | 2 | Manual restart required after error | ORD10001 | COB-11 | 2024-03-12T10:35:10Z | 2024-03-12T10:53:41Z | 280.7 | 1111 | WH-SPECIAL | E4,E5,E6,E7 | 13 | E4-025,E4-028,E4-030,E5-011,E5-014,E6-022,E7-001,E7-012,E7-019,E7-021,E7-024,E6-033,E5-020 | 0.64 |
| PPATH0004 | human | 0 | Quick pick | ORD10234 | PICKER07 | 2024-03-12T11:02:12Z | 2024-03-12T11:05:54Z | 35.7 | 222 | WAREB | B3 | 2 | B3-007,B3-013 | 0.81 |
| PPATH0005 | robot | 0 | blank | ORD10235 | RB-107 | 2024-03-12T12:18:00Z | 2024-03-12T12:25:17Z | 230.8 | 437 | ROBOT-2 | Z1,Z8,Z2 | 9 | Z1-099,Z8-110,Z2-140,Z1-101,Z8-112,Z8-121,Z2-148,Z2-152,Z8-119 | 0.89 |
| PPATH0006 | cobot | 3 | Overnight run | ORD10236 | COB-02 | 2024-03-12T13:56:10Z | 2024-03-12T15:13:26Z | 410 | 4636 | WH-STARTUP | E2,E3,E4,E5,E6 | 18 | E2-002,E2-009,E2-011,E3-007,E3-014,E3-019,E4-020,E4-023,E5-004,E5-010,E5-017,E6-001,E6-005,E6-011,E6-012,E6-017,E6-021,E6-027 | 0.51 |
| PPATH0007 | human | 0 | Shortest possible path | ORD10237 | PICKER17 | 2024-03-12T15:24:28Z | 2024-03-12T15:25:41Z | 17.9 | 73 | WAREC | C1 | 1 | C1-005 | 1 |
| PPATH0008 | robot | 0 | First run | ORD10238 | RB-115 | 2024-03-12T16:37:00Z | 2024-03-12T16:39:30Z | 27.5 | 150 | ROBOT-3 | Z5 | 3 | Z5-203,Z5-212,Z5-215 | 0.97 |
| PPATH0009 | cobot | 2 | Overnight run | ORD10239 | COB-13 | 2024-03-12T17:05:00Z | 2024-03-12T18:07:31Z | 330.1 | 3751 | WH-SPECIAL | E4,E9,E5 | 15 | E4-020,E4-023,E9-018,E9-025,E5-010,E5-015,E9-030,E9-032,E5-020,E4-017,E5-025,E9-035,E9-036,E4-029,E5-019 | 0.6 |
| PPATH0010 | human | 1 | Lost time in aisle | ORD10240 | PICKER09 | 2024-03-13T07:21:02Z | 2024-03-13T07:32:24Z | 158.4 | 682 | WARED | D1,D2,D3 | 8 | D1-014,D2-005,D2-007,D3-008,D3-012,D3-013,D2-011,D1-022 | 0.69 |
| PPATH0011 | robot | 0 | blank | ORD10241 | RB-104 | 2024-03-13T09:00:00Z | 2024-03-13T09:08:49Z | 362.3 | 529 | ROBOT-1 | Z2,Z4,Z6 | 10 | Z2-099,Z4-102,Z6-205,Z2-101,Z2-110,Z4-115,Z4-116,Z4-121,Z6-208,Z2-120 | 0.91 |
| PPATH0012 | cobot | 0 | Scheduled but not started | ORD10242 | COB-05 | 2024-03-13T12:00:00Z | 2024-03-13T12:00:00Z | 0 | 0 | WAREE | blank | 1 | blank | 0 |
| PPATH0013 | human | 0 | blank | ORD10243 | PICKER14 | 2024-03-13T13:33:12Z | 2024-03-13T13:37:33Z | 82.3 | 261 | WH-02 | B2,B3 | 4 | B2-001,B2-013,B3-007,B3-009 | 0.83 |
| PPATH0014 | robot | 0 | blank | ORD10244 | RB-121 | 2024-03-13T14:22:44Z | 2024-03-13T14:26:38Z | 75.1 | 234 | ROBOT-2 | Z1 | 2 | Z1-105,Z1-110 | 0.98 |
| PPATH0015 | cobot | 0 | blank | ORD10245 | COB-07 | 2024-03-13T15:00:00Z | 2024-03-13T15:02:06Z | 25 | 126 | WH-01 | E6 | 2 | E6-001,E6-002 | 0.86 |
| PPATH0016 | human | 5 | Unusually high item count | ORD10246 | PICKER22 | 2024-03-13T16:40:00Z | 2024-03-13T17:15:12Z | 440.2 | 2112 | WAREA | A1,A2,B1,B2,B3 | 22 | A1-005,A2-002,B1-001,B1-002,B2-008,B2-014,B3-020,B3-021,B3-022,A2-006,A2-007,B2-009,B1-005,B3-025,B2-016,B1-009,A1-008,A1-009,B2-015,B3-028,A2-010,B1-011 | 0.54 |
| PPATH0017 | robot | 0 | blank | ORD10247 | RB-108 | 2024-03-13T18:00:00Z | 2024-03-13T18:13:41Z | 182 | 821 | ROBOT-1 | Z2,Z3 | 6 | Z2-120,Z2-122,Z3-205,Z3-210,Z3-211,Z2-130 | 0.87 |
| PPATH0018 | cobot | 0 | blank | ORD10248 | COB-21 | 2024-03-13T19:19:08Z | 2024-03-13T19:21:52Z | 34.2 | 164 | WH-03 | E5 | 2 | E5-012,E5-013 | 0.88 |
| PPATH0019 | human | 0 | blank | ORD10249 | PICKER03 | 2024-03-13T20:02:00Z | 2024-03-13T20:04:44Z | 27.6 | 164 | WAREB | B2 | 1 | B2-008 | 0.99 |
| PPATH0020 | robot | 0 | Auto-recharge | ORD10250 | RB-132 | 2024-03-14T07:04:00Z | 2024-03-14T07:10:19Z | 94.9 | 379 | ROBOT-4 | Z7,Z8 | 4 | Z7-215,Z8-109,Z8-112,Z7-218 | 0.95 |
What the 200 rows show
from the 200-row sampleRobot (picker type) stands out: mean errors_
- 0median errors_
encountered - 50warehouses
- 122.3median total_
distance_ meters - 854median total_
time_ seconds - 5median item_
count - 0.80median path_
efficiency_ score
Median 0, from 0 to 5.
- string 8
- integer 2
- float 3
- datetime 2
Columns
15 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
picking_path_id | string | Unique identifier for each recorded picking path instance.unique | PPATH0001 |
order_id | string | Unique identifier for the picking order associated with the path. | ORD10001 |
picker_id | string | Unique identifier for the warehouse worker or robot who performed the picking. | PICKER01 |
warehouse_id | string | Identifier for the warehouse where the picking occurred. | WAREA |
zone_sequence | string | Comma-separated list of warehouse zones traversed during the picking path, in order.optional | A1,B2 |
pick_sequence | string | Comma-separated list of item location codes in the order they were picked.optional | B3-007,B3-013 |
picker_type | string | Type of picker (e.g., human, robot, cobot).human · robot · cobot | human |
notes | string | Additional notes or comments about the picking path.optional | Quick pick |
| Numbers 5 columns | |||
total_distance_meters | float | Total distance traveled during the picking path, in meters.0 or more | 95.2 |
total_time_seconds | float | Total time taken to complete the picking path, in seconds.0 or more | 275 |
item_count | integer | Total number of items picked during the path.1 or more | 7 |
path_efficiency_score | float | Calculated efficiency score for the picking path (0-1 scale, higher is better).0 to 1 · optional | 0.77 |
errors_encountered | integer | Number of errors (missed picks, wrong items, etc.) encountered during the path.0 or more · optional | 0 |
| Dates and times 2 columns | |||
start_datetime | datetime | Timestamp when the picking path started. | 2024-03-12T08:13:20Z |
end_datetime | datetime | Timestamp when the picking path ended. | 2024-03-12T08:17:55Z |
Use it for
A logistics dashboard
Errors_
encountered by picker_ type and a breakdown of notes. Excel, Power BI or Tableau. Why do the 60 robot rows have a mean errors_
encountered of 0.03? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Picking paths200PPATH00010humanPPATH00020robotPPATH00032cobot
A software demo
Believable picking paths with order_
id, picker_ id and start_ datetime to fill a screen in front of a buyer.
This dataset200 rows15 columns
Yours1,000 rows15 columns
blueprint · warehouse-picking-path-records
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.
Rules it was built with
- Include order ID and item locations
- Sequence of picker actions tracked
- Capture time per pick and total path length
- No real employee info
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-path-records