{ "format": "dataset-manifest/1", "slug": "general-stock-tracking-application", "version": 2, "seed": "general-stock-tracking-application:1", "spec": { "format": "dataset/1", "slug": "general-stock-tracking-application", "name": "Logistics Stock Insights", "tagline": "Stock levels, suppliers, products, and shipments data from the logistics industry.", "description": "Efficient stock tracking drives logistics operations to meet customer demands while minimizing excess inventory. In this environment, suppliers manage a variety of products that are consistently shipped to various storage locations. Order fulfillment hinges on accurately monitoring stock levels, which reflect the relationship between the items available, their movement through the supply chain, and the dynamic nature of customer needs. Popular items tend to cluster in stock levels due to consistent demand, while unique or seasonal products may appear less frequently, showcasing the diverse inventory landscape.\n\nThe primary table in this dataset is 'stock_levels', which captures key metrics related to the quantity of products held in various storage locations at any given time. Each row in this table represents a snapshot of a specific product's stock level, detailing how much of that product is available, its location, and the associated shipment activity. Through this lens, users can explore the intricacies of product movement and stock management, as well as how products interact based on demand and logistics practices.\n\nThis data allows developers to practice querying relationships within the data, such as determining trends in stock levels over time, identifying which products are frequently overstocked or understocked, and analyzing the effects of seasonal fluctuations on inventory. Users can also investigate how stock levels correlate with shipment rates, enabling insights into supply chain efficiency and responsiveness.\n\nIt's important to note that the data within this dataset is synthetic, generated to mimic real-world scenarios in logistics stock management, providing a rich ground for practicing data analysis and visualization skills.", "domain": "logistics", "shape": "star-schema", "useCases": [ "bi-dashboards", "analytics-demo" ], "keywords": [ "logistics", "stock tracking", "inventory management", "supply chain", "data analysis" ], "locale": "en", "license": "CC0-1.0", "version": 1, "tables": { "stock_levels": { "description": "Each row in this table represents the stock level of a specific product at a given storage location, linked to a supplier. This enables analysis of inventory trends and supplier performance.", "rows": 500, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each stock level entry." }, "product_name": { "kind": "productName", "description": "Name of the product being tracked." }, "supplier_name": { "kind": "company", "description": "Name of the supplier providing the product." }, "location_name": { "kind": "city", "description": "Name of the storage location where the product is held." }, "stock_quantity": { "kind": "int", "min": 0, "max": 1000, "description": "Current quantity of the product in stock.", "distribution": "normal" }, "last_restocked": { "kind": "date", "from": "2022-01-01", "to": "2023-12-31", "description": "Date when the product was last restocked.", "nullable": true, "trend": "growing" }, "shipment_size": { "kind": "int", "min": 1, "max": 100, "description": "Size of the last shipment received for this product.", "nullable": true, "distribution": "long-tail" } } } }, "structure": "single-table", "traits": [ "categorical", "nullable", "timestamped" ], "analysisQuestions": [ "What are the top products with the highest stock levels?", "How frequently are products restocked and does it vary by supplier?", "What is the average shipment size for different products?", "Are there seasonal trends in stock levels for certain products?", "Which storage locations have the most inventory and why?" ], "suggestedQueries": [ { "title": "Retrieve all stock levels", "sql": "SELECT * FROM stock_levels;", "dialect": "postgres" }, { "title": "Count stock quantities by product", "sql": "SELECT product_name, SUM(stock_quantity) as total_stock FROM stock_levels GROUP BY product_name;", "dialect": "postgres" }, { "title": "Identify average shipment sizes per supplier", "sql": "SELECT supplier_name, AVG(shipment_size) as average_shipment FROM stock_levels GROUP BY supplier_name;", "dialect": "postgres" }, { "title": "Find products restocked within the last month", "sql": "SELECT product_name FROM stock_levels WHERE last_restocked >= CURRENT_DATE - INTERVAL '30 days';", "dialect": "postgres" } ], "usageScenarios": [ { "title": "Logistics Analyst", "body": "A logistics analyst can use this dataset to monitor stock levels across various products, helping to identify trends and optimize inventory management. By analyzing restocking patterns and shipment sizes, the analyst can provide recommendations for suppliers and storage practices." }, { "title": "Business Intelligence Developer", "body": "A BI developer can use this dataset to create dashboards that visualize stock levels, supplier performance, and inventory turnover. This data-driven approach allows businesses to make informed decisions and enhance their supply chain efficiency." }, { "title": "Supply Chain Manager", "body": "A supply chain manager can leverage this dataset to assess product availability across different locations, ensuring that customer demands are met without overstocking. By analyzing the data, they can also negotiate better terms with suppliers based on performance metrics." } ] }, "counts": { "stock_levels": 500 }, "stats": { "stock_levels": { "rows": 500, "columns": { "id": { "nulls": 0, "distinct": 500, "min": 1, "max": 500 }, "product_name": { "nulls": 0, "distinct": 306, "min": "Classic Backpack", "max": "Wireless Umbrella" }, "supplier_name": { "nulls": 0, "distinct": 330, "min": "Acme Digital", "max": "Willow Works" }, "location_name": { "nulls": 0, "distinct": 63, "min": "Amsterdam", "max": "Zurich" }, "stock_quantity": { "nulls": 0, "distinct": 327, "min": 112, "max": 914 }, "last_restocked": { "nulls": 49, "distinct": 345, "min": "2022-01-03", "max": "2023-12-30" }, "shipment_size": { "nulls": 50, "distinct": 92, "min": 1, "max": 100 } } } }, "artifacts": [ { "file": "csv/stock_levels.csv", "format": "csv", "table": "stock_levels", "key": "datasets/general-stock-tracking-application/v2/csv/stock_levels.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/general-stock-tracking-application/v2/csv/stock_levels.csv", "bytes": 29286, "sha256": "7731eb9d1108a38d51ff8ebeab04b2267196d6efec2bd5ae07d8942cc00ef853", "rows": 500, "contentType": "text/csv; charset=utf-8" }, { "file": "general-stock-tracking-application.json", "format": "json", "table": null, "key": "datasets/general-stock-tracking-application/v2/general-stock-tracking-application.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/general-stock-tracking-application/v2/general-stock-tracking-application.json", "bytes": 118022, "sha256": "d0ab66791a62f6d62001f0016e57e5c59a2a298b0158fea300e38bc2a680b227", "rows": null, "contentType": "application/json; charset=utf-8" }, { "file": "sql/postgres.sql", "format": "sql-postgres", "table": null, "key": "datasets/general-stock-tracking-application/v2/sql/postgres.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/general-stock-tracking-application/v2/sql/postgres.sql", "bytes": 40662, "sha256": "7f26af28a59caa4d2aa19598a04967b098b9f98e40fa76173da207b0f285f0c4", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mysql.sql", "format": "sql-mysql", "table": null, "key": "datasets/general-stock-tracking-application/v2/sql/mysql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/general-stock-tracking-application/v2/sql/mysql.sql", "bytes": 40547, "sha256": "75b40b1f1c67e4a72a39abda7820f82a310404b6acd3e386b2d6239d8c030873", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mssql.sql", "format": "sql-mssql", "table": null, "key": "datasets/general-stock-tracking-application/v2/sql/mssql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/general-stock-tracking-application/v2/sql/mssql.sql", "bytes": 42570, "sha256": "9b18462692f0f7de81bb70f0af78170127f056c96e003751b0a772c19268074a", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/sqlite.sql", "format": "sql-sqlite", "table": null, "key": "datasets/general-stock-tracking-application/v2/sql/sqlite.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/general-stock-tracking-application/v2/sql/sqlite.sql", "bytes": 40517, "sha256": "81c8aae47f92be056c14da45fb56d5960232c1e8693ddbe738ba7fe7e9328db9", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "prisma/seed.ts", "format": "prisma-seed", "table": null, "key": "datasets/general-stock-tracking-application/v2/prisma/seed.ts", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/general-stock-tracking-application/v2/prisma/seed.ts", "bytes": 92753, "sha256": "70b6c33333282595d3eef08dfafa3d78adc24dc139aae3c4536c1c847421e6b6", "rows": null, "contentType": "application/typescript; charset=utf-8" }, { "file": "django/fixtures.json", "format": "django-fixture", "table": null, "key": "datasets/general-stock-tracking-application/v2/django/fixtures.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/general-stock-tracking-application/v2/django/fixtures.json", "bytes": 141498, "sha256": "f741c882004e6428ec9c76717efbbd9d6c2984374382914e7593e4aab41c9c78", "rows": null, "contentType": "application/json; charset=utf-8" } ], "publishedAt": 1788767130604, "updatedAt": 1788778940811 }