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license: cc-by-nc-sa-4.0
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# Temporal Logistics & Inventory Movements Dataset

## Dataset Overview

This dataset contains detailed, time-indexed records of logistics, warehouse, and inventory operations.  
Each row represents a **single operational event** (e.g., stock movement, transfer, shipment, or document line) enriched with **multiple temporal attributes**, spatial warehouse references, and administrative metadata.

The structure is particularly suited for **temporal analysis**, allowing the reconstruction and study of:
- Event sequences over time  
- Operational workflows and delays  
- Inventory life-cycles (production → storage → movement → shipment)  
- Temporal correlations between documents, movements, and logistics activities  

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## Temporal Dimension and Analysis Potential

The dataset includes several complementary **date and time fields**, enabling both coarse-grained and fine-grained temporal studies:

- **Daily / periodic analysis**  
  Using movement, production, expiration, and document dates to analyze trends, seasonality, and workload distribution.

- **Event sequencing and process mining**  
  By ordering records via timestamps, it is possible to reconstruct operational pipelines (e.g., inbound → storage → transfer → outbound).

- **Duration and latency measurement**  
  Comparing start/end dates and movement timestamps allows estimation of:
  - Storage time per lot or item  
  - Transfer and handling delays  
  - Time-to-shipment and time-to-completion  

- **Traceability over time**  
  Temporal alignment of lots, articles, documents, and shipments enables end-to-end traceability across the supply chain.

- **Anomaly and exception detection**  
  Time gaps, overlaps, or unexpected sequences can highlight bottlenecks, inefficiencies, or data inconsistencies.

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## Analytical Use Cases

Typical analyses supported by this dataset include:
- Time-series analysis of inventory movements  
- Lead-time and throughput evaluation  
- Lot aging and expiration risk monitoring  
- Operational performance monitoring by day, hour, or period  
- Historical reconstruction of warehouse and shipping activities  

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## Scope

This dataset is designed for:
- **Temporal analytics and forecasting**
- **Process mining and operational intelligence**
- **Logistics and warehouse optimization studies**
- **Auditability and historical analysis of inventory flows**

It is not limited to static inventory snapshots, but instead provides a **dynamic, event-driven view** of logistics operations over time.


# Dataset Structure Documentation

This dataset represents transactional and logistical records, likely related to warehouse management, shipping, inventory movements, and document tracking.  
Each row corresponds to a single operational record (e.g., shipment, movement, or document line).

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## Columns Description

### Identifiers and Core References
- **id**: Unique internal identifier of the record.
- **cont**: Container or batch identifier.
- **dtmo**: Movement date.
- **caus**: Causal code indicating the reason/type of movement.
- **segno**: Sign of the movement (e.g., debit/credit, in/out).
- **conf**: Configuration or confirmation flag.
- **udc**: Logistic unit or handling unit code.
- **lotto**: Lot or batch number.
- **extra**: Extra or custom flag/field.

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### Warehouse / Location Information
- **stab**: Plant or warehouse code.
- **maga**: Warehouse area or main storage identifier.
- **area**: Specific storage area.
- **cors**: Aisle identifier.
- **posi**: Position or bin location.
- **cell**: Cell or slot within the position.

#### Transferred Location Fields
(Values after a transfer or movement)
- **stab_trf**: Destination plant/warehouse.
- **maga_trf**: Destination warehouse area.
- **area_trf**: Destination area.
- **cors_trf**: Destination aisle.
- **posi_trf**: Destination position.
- **cell_trf**: Destination cell.

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### Article / Item Information
- **arti**: Main article or item code.
- **arti1 – arti6**: Additional or related article codes.
- **qt_pezzi**: Quantity in pieces.
- **qt_conf**: Quantity per package.
- **tipo_nomi**: Type/category of item naming.
- **nomi**: Item name or description reference.

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### Program and Processing
- **prog**: Program or process identifier.
- **prog_trf**: Program identifier for transfer operations.
- **ragg**: Aggregation or grouping code.
- **solo_per_host**: Flag indicating host-only processing.

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### Document and Administrative Data
- **tipo_docu**: Document type.
- **anno_docu**: Document year.
- **nume_docu**: Document number.
- **riga_docu**: Document line number.
- **anno_viag**: Travel/shipment year.
- **viaggio**: Travel or shipment identifier.
- **uten**: User or operator ID.
- **term**: Terminal or workstation identifier.

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### Dates and Time Fields
- **dtlo**: Lot date.
- **dt_scad**: Expiration date.
- **dt_iniz**: Start date.
- **dt_fine**: End date.
- **dt_prod**: Production date.
- **dt_movim_host**: Movement date recorded by host system.
- **d_rest**: Residual or remaining date.
- **oramo**: Time of the movement (HH:MM:SS).

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### Shipping and Logistics
- **tipo_sped**: Shipping type.
- **rest**: Residual quantity or status flag.
- **cpv_movim_cont**: Container movement reference code.
- **posi**: Physical position involved in shipping.

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## Notes
- Date fields may appear in localized formats (e.g., `09-gen-24`).
- Masked values (e.g., `XXXXXXXXXX`) indicate anonymized or sensitive information.
- Empty fields represent optional or non-applicable data for specific records.

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## Example Use Cases
- Warehouse movement tracking  
- Shipment and logistics analysis  
- Inventory and batch traceability  
- Document and operational auditing  

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