Aria AI — Supply Chain
Collection
Operations-research decision-support demos for inventory, routing, warehousing, and sourcing. • 18 items • Updated
Dataset Preview
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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'week_index', 'date', 'demand'}) and 11 missing columns ({'shortage_cost', 'min_order', 'lead_time', 'demand_pattern', 'name', 'unit_cost', 'service_class', 'category', 'pack_size', 'initial_stock', 'holding_cost_rate'}).
This happened while the csv dataset builder was generating data using
hf://datasets/AriaAICompany/aria-stock-scenarios/data/demand.csv (at revision 116d8e9dc4d0e68a06c4146c96ca54402facf76a), ['hf://datasets/AriaAICompany/aria-stock-scenarios@116d8e9dc4d0e68a06c4146c96ca54402facf76a/data/catalog.csv', 'hf://datasets/AriaAICompany/aria-stock-scenarios@116d8e9dc4d0e68a06c4146c96ca54402facf76a/data/demand.csv', 'hf://datasets/AriaAICompany/aria-stock-scenarios@116d8e9dc4d0e68a06c4146c96ca54402facf76a/data/open_orders.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
sku_id: string
date: string
demand: double
week_index: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 730
to
{'sku_id': Value('string'), 'name': Value('string'), 'category': Value('string'), 'unit_cost': Value('float64'), 'pack_size': Value('int64'), 'min_order': Value('int64'), 'lead_time': Value('int64'), 'holding_cost_rate': Value('float64'), 'shortage_cost': Value('float64'), 'initial_stock': Value('float64'), 'service_class': Value('string'), 'demand_pattern': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'week_index', 'date', 'demand'}) and 11 missing columns ({'shortage_cost', 'min_order', 'lead_time', 'demand_pattern', 'name', 'unit_cost', 'service_class', 'category', 'pack_size', 'initial_stock', 'holding_cost_rate'}).
This happened while the csv dataset builder was generating data using
hf://datasets/AriaAICompany/aria-stock-scenarios/data/demand.csv (at revision 116d8e9dc4d0e68a06c4146c96ca54402facf76a), ['hf://datasets/AriaAICompany/aria-stock-scenarios@116d8e9dc4d0e68a06c4146c96ca54402facf76a/data/catalog.csv', 'hf://datasets/AriaAICompany/aria-stock-scenarios@116d8e9dc4d0e68a06c4146c96ca54402facf76a/data/demand.csv', 'hf://datasets/AriaAICompany/aria-stock-scenarios@116d8e9dc4d0e68a06c4146c96ca54402facf76a/data/open_orders.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
sku_id string | name string | category string | unit_cost float64 | pack_size int64 | min_order int64 | lead_time int64 | holding_cost_rate float64 | shortage_cost float64 | initial_stock float64 | service_class string | demand_pattern string |
|---|---|---|---|---|---|---|---|---|---|---|---|
BRG-1000 | Bearing 1 industrial | Bearing | 19.29 | 10 | 10 | 2 | 0.0072 | 46.3 | 16 | A | seasonal |
FLT-1001 | Filter 1 industrial | Filter | 6.88 | 20 | 20 | 1 | 0.0085 | 12.38 | 70 | B | regular |
SEAL-1002 | Seal 1 industrial | Seal | 5.99 | 5 | 5 | 2 | 0.0047 | 8.09 | 7 | C | intermittent |
MTR-1003 | Motor 1 industrial | Motor | 270.18 | 1 | 1 | 3 | 0.0097 | 648.44 | 3 | A | regular |
VLV-1004 | Valve 1 industrial | Valve | 43.33 | 2 | 2 | 2 | 0.0042 | 78 | 5 | B | intermittent |
BELT-1005 | Belt 1 industrial | Belt | 14.21 | 5 | 5 | 1 | 0.0043 | 25.57 | 10 | B | seasonal |
SNS-1006 | Sensor 1 industrial | Sensor | 50.93 | 1 | 1 | 3 | 0.0054 | 122.23 | 4 | A | intermittent |
HYD-1007 | Hose 1 industrial | Hose | 10.52 | 10 | 10 | 1 | 0.0097 | 14.2 | 4 | C | regular |
CPL-1008 | Coupling 1 industrial | Coupling | 14.83 | 5 | 5 | 2 | 0.0097 | 26.69 | 33 | B | seasonal |
GASK-1009 | Gasket 1 industrial | Gasket | 2.28 | 25 | 25 | 1 | 0.0077 | 3.07 | 31 | C | intermittent |
BRG-1010 | Bearing 2 industrial | Bearing | 25.11 | 10 | 10 | 2 | 0.0088 | 60.27 | 51 | A | seasonal |
FLT-1011 | Filter 2 industrial | Filter | 7.94 | 20 | 20 | 1 | 0.0055 | 14.29 | 58 | B | regular |
SEAL-1012 | Seal 2 industrial | Seal | 5.02 | 5 | 5 | 2 | 0.0106 | 6.77 | 14 | C | intermittent |
MTR-1013 | Motor 2 industrial | Motor | 240.58 | 1 | 1 | 3 | 0.0056 | 577.39 | 2 | A | regular |
VLV-1014 | Valve 2 industrial | Valve | 40.45 | 2 | 2 | 2 | 0.0057 | 72.82 | 7 | B | intermittent |
BELT-1015 | Belt 2 industrial | Belt | 16.33 | 5 | 5 | 1 | 0.0065 | 29.39 | 6 | B | seasonal |
SNS-1016 | Sensor 2 industrial | Sensor | 49.32 | 1 | 1 | 3 | 0.0048 | 118.38 | 7 | A | intermittent |
HYD-1017 | Hose 2 industrial | Hose | 12.82 | 10 | 10 | 1 | 0.0095 | 17.31 | 40 | C | regular |
CPL-1018 | Coupling 2 industrial | Coupling | 21.03 | 5 | 5 | 2 | 0.0059 | 37.85 | 19 | B | seasonal |
GASK-1019 | Gasket 2 industrial | Gasket | 3.01 | 25 | 25 | 1 | 0.006 | 4.07 | 83 | C | intermittent |
BRG-1020 | Bearing 3 industrial | Bearing | 22.11 | 10 | 10 | 2 | 0.0108 | 53.06 | 14 | A | seasonal |
FLT-1021 | Filter 3 industrial | Filter | 8.12 | 20 | 20 | 1 | 0.011 | 14.62 | 70 | B | regular |
SEAL-1022 | Seal 3 industrial | Seal | 5.77 | 5 | 5 | 2 | 0.0063 | 7.79 | 14 | C | intermittent |
MTR-1023 | Motor 3 industrial | Motor | 245.41 | 1 | 1 | 3 | 0.0098 | 588.99 | 4 | A | regular |
VLV-1024 | Valve 3 industrial | Valve | 36.38 | 2 | 2 | 2 | 0.0057 | 65.48 | 1 | B | intermittent |
BELT-1025 | Belt 3 industrial | Belt | 14.31 | 5 | 5 | 1 | 0.0095 | 25.77 | 35 | B | seasonal |
SNS-1026 | Sensor 3 industrial | Sensor | 70.85 | 1 | 1 | 3 | 0.0112 | 170.04 | 0 | A | intermittent |
HYD-1027 | Hose 3 industrial | Hose | 10.7 | 10 | 10 | 1 | 0.0047 | 14.45 | 36 | C | regular |
CPL-1028 | Coupling 3 industrial | Coupling | 22.35 | 5 | 5 | 2 | 0.0076 | 40.23 | 10 | B | seasonal |
GASK-1029 | Gasket 3 industrial | Gasket | 2.62 | 25 | 25 | 1 | 0.0101 | 3.54 | 120 | C | intermittent |
BRG-1030 | Bearing 4 industrial | Bearing | 26 | 10 | 10 | 2 | 0.0072 | 62.4 | 34 | A | seasonal |
FLT-1031 | Filter 4 industrial | Filter | 8.18 | 20 | 20 | 1 | 0.0064 | 14.73 | 16 | B | regular |
SEAL-1032 | Seal 4 industrial | Seal | 6.55 | 5 | 5 | 2 | 0.0052 | 8.84 | 19 | C | intermittent |
MTR-1033 | Motor 4 industrial | Motor | 330.1 | 1 | 1 | 3 | 0.0083 | 792.25 | 1 | A | regular |
VLV-1034 | Valve 4 industrial | Valve | 48.97 | 2 | 2 | 2 | 0.0088 | 88.14 | 6 | B | intermittent |
BELT-1035 | Belt 4 industrial | Belt | 16.18 | 5 | 5 | 1 | 0.0068 | 29.13 | 0 | B | seasonal |
SNS-1036 | Sensor 4 industrial | Sensor | 63.43 | 1 | 1 | 3 | 0.0067 | 152.23 | 6 | A | intermittent |
HYD-1037 | Hose 4 industrial | Hose | 12.13 | 10 | 10 | 1 | 0.0093 | 16.37 | 74 | C | regular |
CPL-1038 | Coupling 4 industrial | Coupling | 23.24 | 5 | 5 | 2 | 0.0111 | 41.83 | 19 | B | seasonal |
GASK-1039 | Gasket 4 industrial | Gasket | 3.14 | 25 | 25 | 1 | 0.0059 | 4.24 | 52 | C | intermittent |
BRG-1040 | Bearing 5 industrial | Bearing | 26.95 | 10 | 10 | 2 | 0.0109 | 64.68 | 48 | A | seasonal |
FLT-1041 | Filter 5 industrial | Filter | 8.67 | 20 | 20 | 1 | 0.0078 | 15.6 | 110 | B | regular |
SEAL-1042 | Seal 5 industrial | Seal | 6.99 | 5 | 5 | 2 | 0.0042 | 9.44 | 22 | C | intermittent |
MTR-1043 | Motor 5 industrial | Motor | 287.31 | 1 | 1 | 3 | 0.0053 | 689.56 | 1 | A | regular |
VLV-1044 | Valve 5 industrial | Valve | 56.83 | 2 | 2 | 2 | 0.0109 | 102.29 | 7 | B | intermittent |
BELT-1045 | Belt 5 industrial | Belt | 14.91 | 5 | 5 | 1 | 0.007 | 26.84 | 21 | B | seasonal |
SNS-1046 | Sensor 5 industrial | Sensor | 59.84 | 1 | 1 | 3 | 0.0088 | 143.61 | 6 | A | intermittent |
HYD-1047 | Hose 5 industrial | Hose | 16 | 10 | 10 | 1 | 0.0067 | 21.59 | 0 | C | regular |
CPL-1048 | Coupling 5 industrial | Coupling | 19.05 | 5 | 5 | 2 | 0.0054 | 34.28 | 30 | B | seasonal |
GASK-1049 | Gasket 5 industrial | Gasket | 3.04 | 25 | 25 | 1 | 0.0074 | 4.1 | 61 | C | intermittent |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
BRG-1000 | null | null | null | null | null | null | null | null | null | null | null |
End of preview.
AriaStock inventory scenarios
Synthetic one-warehouse catalog, 52 weeks of weekly demand, and open purchase orders used by AriaStock.
This is generated data (seed 24). It is for reproducible demos and tests, not a customer extract.
Files
| File | Grain | Notes |
|---|---|---|
data/catalog.csv |
50 SKUs | Cost, pack, MOQ, lead time, holding rate, shortage cost, opening stock |
data/demand.csv |
SKU × week | 52 history weeks ending 2026-09-14 |
data/open_orders.csv |
inbound PO | arrive_week is 1..4 on the planning horizon |
data/sample_preview.json |
one solved run | Precomputed KPI snapshot; labeled as a sample |
Schema
catalog.csv: sku_id, name, category, unit_cost, pack_size, min_order, lead_time, holding_cost_rate, initial_stock, shortage_cost, service_class, demand_pattern
demand.csv: sku_id, date, demand, week_index
open_orders.csv: sku_id, arrive_week, qty
Demand patterns: seasonal, regular, intermittent. Intermittent series are Croston-friendly (many zeros). Recorded zeros are true zero demand in this generator — they are not censored stockouts.
Split
Use weeks week_index < 0 as history. The demo forecasts the next four weeks. Any backtest must use past-only features (see the Space tests).
Generator
projects/aria-stock/src/generator.py in the Space repo, seed 24.
License
CC-BY-4.0. Please keep the synthetic-data label if you reuse the tables.
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