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| license: cc-by-4.0 |
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| # Temporal Logistics & Inventory Movements Dataset |
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| ## Dataset Overview |
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| 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. |
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| 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 |
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| The dataset includes several complementary **date and time fields**, enabling both coarse-grained and fine-grained temporal studies: |
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| - **Daily / periodic analysis** |
| Using movement, production, expiration, and document dates to analyze trends, seasonality, and workload distribution. |
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| - **Event sequencing and process mining** |
| By ordering records via timestamps, it is possible to reconstruct operational pipelines (e.g., inbound → storage → transfer → outbound). |
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| - **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 |
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| - **Traceability over time** |
| Temporal alignment of lots, articles, documents, and shipments enables end-to-end traceability across the supply chain. |
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| - **Anomaly and exception detection** |
| Time gaps, overlaps, or unexpected sequences can highlight bottlenecks, inefficiencies, or data inconsistencies. |
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| ## Analytical Use Cases |
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| 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 |
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| 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** |
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| It is not limited to static inventory snapshots, but instead provides a **dynamic, event-driven view** of logistics operations over time. |
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| # Dataset Structure Documentation |
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| 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 |
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| ### 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. |
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| #### 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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