{
  "format": "dataset-manifest/1",
  "slug": "inventory-control-management-system",
  "version": 1,
  "seed": "inventory-control-management-system:1",
  "spec": {
    "format": "dataset/1",
    "slug": "inventory-control-management-system",
    "name": "Logistics Inventory Dataset",
    "tagline": "An essential dataset for managing and analyzing logistics inventory.",
    "description": "Employees manage inventory levels, ensuring products flow smoothly through the warehouse. They track purchase orders, adjust stock levels, conduct quality inspections, and handle returns to maintain operational efficiency. Each product is assigned a unique identifier, detailing its attributes such as name, category, and supplier, making it easy to trace back through the purchase orders linked to that product. Purchase orders represent the transactions that initiate the stock adjustments, capturing when and how many units were acquired. Stock adjustments reflect changes in inventory levels, accounting for new deliveries or discrepancies due to damage or loss.\n\nQuality inspections serve as a checkpoint in this operation. When products arrive, employees perform inspections to assess their condition before they are shelved. This table is linked to both the products and purchase orders, indicating the quality status of goods upon arrival. The returns table tracks products sent back from customers, allowing the business to analyze return patterns and reasons, which can signal underlying issues with specific products or suppliers. The employees table logs individual staff interactions with these processes, providing insights into workforce efficiency and accountability.\n\nThis dataset answers questions regarding inventory turnover rates, the effectiveness of quality checks, and return trends, making it ideal for practicing data analysis and reporting. For example, you can examine which products are often returned and correlate that with inspection pass rates to identify quality control areas needing improvement. Additionally, tracking employee activities related to stock adjustments can help optimize workforce allocations. All data within this dataset is synthetic, created to simulate a realistic logistics environment while ensuring user privacy and data security.",
    "domain": "logistics",
    "shape": "star-schema",
    "useCases": [
      "bi-dashboards",
      "analytics-demo"
    ],
    "keywords": [
      "inventory",
      "logistics",
      "warehouse",
      "products",
      "quality control",
      "stock adjustments",
      "purchase orders",
      "returns",
      "employees"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "products": {
        "description": "This table contains details about each product in the inventory, linking to transactions and inspections via product_id.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each product."
          },
          "product_name": {
            "kind": "words",
            "max": 50,
            "description": "The name of the product."
          },
          "category": {
            "kind": "enum",
            "values": [
              "Electronics",
              "Clothing",
              "Furniture",
              "Food"
            ],
            "description": "The category to which the product belongs."
          },
          "price": {
            "kind": "decimal",
            "min": 1,
            "max": 1000,
            "scale": 2,
            "description": "The retail price of the product."
          },
          "sku": {
            "kind": "slug",
            "from": "product_name",
            "description": "A unique stock keeping unit identifier.",
            "nullable": true
          },
          "stock_level": {
            "kind": "int",
            "min": 0,
            "max": 1000,
            "description": "Current stock level of the product.",
            "nullable": true
          }
        }
      },
      "purchase_orders": {
        "description": "This table tracks all purchase orders made for products, linking to the products table via product_id.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each purchase order."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "The product being ordered."
          },
          "order_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date when the order was placed.",
            "trend": "growing"
          },
          "quantity": {
            "kind": "int",
            "min": 1,
            "max": 100,
            "description": "The number of units ordered.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
                "Cancelled"
              ]
            }
          },
          "status": {
            "kind": "enum",
            "values": [
              "Pending",
              "Completed",
              "Cancelled"
            ],
            "description": "The current status of the order."
          }
        }
      },
      "stock_adjustments": {
        "description": "This table records adjustments made to stock levels, linking back to the products table.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each stock adjustment."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "The product whose stock level is adjusted."
          },
          "adjustment_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date when the adjustment was made.",
            "trend": "seasonal"
          },
          "adjustment_type": {
            "kind": "enum",
            "values": [
              "Increase",
              "Decrease"
            ],
            "description": "Type of stock adjustment."
          },
          "adjusted_amount": {
            "kind": "int",
            "min": 1,
            "max": 500,
            "description": "The amount of stock adjusted.",
            "nullable": true,
            "when": {
              "column": "adjustment_type",
              "in": [
                "Increase"
              ]
            }
          }
        }
      },
      "quality_inspections": {
        "description": "This table documents quality inspections completed for products, linking to the products table.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each quality inspection."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "The product that was inspected."
          },
          "inspection_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date of the quality inspection.",
            "trend": "growing"
          },
          "result": {
            "kind": "enum",
            "values": [
              "Pass",
              "Fail"
            ],
            "description": "The outcome of the quality inspection."
          },
          "notes": {
            "kind": "sentence",
            "max": 200,
            "description": "Additional notes regarding the inspection.",
            "nullable": true,
            "when": {
              "column": "result",
              "in": [
                "Fail"
              ]
            }
          }
        }
      },
      "returns": {
        "description": "This table tracks product returns, linking back to the products table.",
        "rows": 80,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each return."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "The product being returned."
          },
          "return_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date when the product was returned.",
            "trend": "growing"
          },
          "reason": {
            "kind": "sentence",
            "max": 150,
            "description": "The reason for the return.",
            "nullable": true
          },
          "quantity": {
            "kind": "int",
            "min": 1,
            "max": 100,
            "description": "The number of units returned.",
            "nullable": true
          }
        }
      },
      "employees": {
        "description": "This table contains records of employees involved in managing inventory, linking to various activities.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each employee."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the employee."
          },
          "role": {
            "kind": "enum",
            "values": [
              "Manager",
              "Warehouse Staff",
              "Quality Inspector"
            ],
            "description": "The role of the employee."
          },
          "hire_date": {
            "kind": "date",
            "from": "2015-01-01",
            "to": "2023-12-31",
            "description": "The date when the employee was hired.",
            "trend": "growing"
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The email address of the employee.",
            "nullable": true,
            "when": {
              "column": "role",
              "in": [
                "Manager"
              ]
            }
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "nullable",
      "timestamped"
    ],
    "analysisQuestions": [
      "What is the average stock level of products over time?",
      "How many products were returned and what were the common reasons?",
      "What percentage of quality inspections passed?",
      "Which employees have the most stock adjustments recorded?",
      "What trends can be observed in purchase orders by category?"
    ],
    "suggestedQueries": [
      {
        "title": "List all products and their stock levels.",
        "sql": "SELECT product_name, stock_level FROM products;",
        "dialect": "postgres"
      },
      {
        "title": "Count of purchase orders by status.",
        "sql": "SELECT status, COUNT(*) FROM purchase_orders GROUP BY status;",
        "dialect": "postgres"
      },
      {
        "title": "Average stock adjustments for each product.",
        "sql": "SELECT p.product_name, AVG(sa.adjusted_amount) FROM stock_adjustments sa JOIN products p ON sa.product_id = p.id GROUP BY p.product_name;",
        "dialect": "postgres"
      },
      {
        "title": "Quality inspection results by product category.",
        "sql": "SELECT p.category, COUNT(*) FILTER (WHERE qi.result = 'Pass') AS passed, COUNT(*) FILTER (WHERE qi.result = 'Fail') AS failed FROM quality_inspections qi JOIN products p ON qi.product_id = p.id GROUP BY p.category;",
        "dialect": "postgres"
      },
      {
        "title": "Monthly returns trend.",
        "sql": "SELECT DATE_TRUNC('month', return_date) AS month, SUM(quantity) AS total_returns FROM returns GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Warehouse Manager's Inventory Overview",
        "body": "A warehouse manager can utilize this dataset to monitor stock levels across various products, ensuring that inventory is maintained at optimal levels. By analyzing purchase orders and stock adjustments, they can identify trends and make informed decisions about reordering and managing supplies effectively."
      },
      {
        "title": "Quality Control Inspector's Insights",
        "body": "Quality control inspectors can leverage this dataset to evaluate the outcomes of inspections conducted on products. By analyzing inspection results, they can identify recurring quality issues and collaborate with the purchasing department to address concerns with specific suppliers or batches."
      },
      {
        "title": "Returns Analysis for Product Improvement",
        "body": "Product managers can utilize the returns data to analyze patterns and reasons for returns. This analysis can provide insights into product quality and customer satisfaction, enabling them to make data-driven improvements to product design or supplier selection, ultimately enhancing customer experience."
      },
      {
        "title": "Employee Performance Tracking",
        "body": "Human resources can use the employee dataset to track performance metrics related to inventory management tasks. By analyzing the frequency of stock adjustments and other activities, they can identify top performers and provide additional training or support to those needing improvement."
      }
    ]
  },
  "counts": {
    "employees": 50,
    "products": 200,
    "purchase_orders": 300,
    "quality_inspections": 100,
    "returns": 80,
    "stock_adjustments": 150
  },
  "stats": {
    "products": {
      "rows": 200,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 200,
          "min": 1,
          "max": 200
        },
        "product_name": {
          "nulls": 0,
          "distinct": 200
        },
        "category": {
          "nulls": 0,
          "distinct": 4,
          "min": "Clothing",
          "max": "Furniture"
        },
        "price": {
          "nulls": 0,
          "distinct": 200,
          "min": 7.76,
          "max": 995.93
        },
        "sku": {
          "nulls": 16,
          "distinct": 184
        },
        "stock_level": {
          "nulls": 22,
          "distinct": 165,
          "min": 10,
          "max": 986
        }
      }
    },
    "purchase_orders": {
      "rows": 300,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 300,
          "min": 1,
          "max": 300
        },
        "product_id": {
          "nulls": 0,
          "distinct": 156,
          "min": 2,
          "max": 199
        },
        "order_date": {
          "nulls": 0,
          "distinct": 271,
          "min": "2020-01-21",
          "max": "2023-12-28"
        },
        "quantity": {
          "nulls": 204,
          "distinct": 61,
          "min": 5,
          "max": 100
        },
        "status": {
          "nulls": 0,
          "distinct": 3,
          "min": "Cancelled",
          "max": "Pending"
        }
      }
    },
    "stock_adjustments": {
      "rows": 150,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 150,
          "min": 1,
          "max": 150
        },
        "product_id": {
          "nulls": 0,
          "distinct": 102,
          "min": 2,
          "max": 200
        },
        "adjustment_date": {
          "nulls": 0,
          "distinct": 140,
          "min": "2020-01-02",
          "max": "2023-12-25"
        },
        "adjustment_type": {
          "nulls": 0,
          "distinct": 2,
          "min": "Decrease",
          "max": "Increase"
        },
        "adjusted_amount": {
          "nulls": 85,
          "distinct": 57,
          "min": 9,
          "max": 498
        }
      }
    },
    "quality_inspections": {
      "rows": 100,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 100,
          "min": 1,
          "max": 100
        },
        "product_id": {
          "nulls": 0,
          "distinct": 79,
          "min": 1,
          "max": 200
        },
        "inspection_date": {
          "nulls": 0,
          "distinct": 99,
          "min": "2020-01-11",
          "max": "2023-12-27"
        },
        "result": {
          "nulls": 0,
          "distinct": 2,
          "min": "Fail",
          "max": "Pass"
        },
        "notes": {
          "nulls": 47,
          "distinct": 53
        }
      }
    },
    "returns": {
      "rows": 80,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 80,
          "min": 1,
          "max": 80
        },
        "product_id": {
          "nulls": 0,
          "distinct": 68,
          "min": 2,
          "max": 200
        },
        "return_date": {
          "nulls": 0,
          "distinct": 78,
          "min": "2020-01-18",
          "max": "2023-12-16"
        },
        "reason": {
          "nulls": 5,
          "distinct": 75
        },
        "quantity": {
          "nulls": 8,
          "distinct": 53,
          "min": 1,
          "max": 98
        }
      }
    },
    "employees": {
      "rows": 50,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 50,
          "min": 1,
          "max": 50
        },
        "full_name": {
          "nulls": 0,
          "distinct": 50,
          "min": "Andre Schmidt",
          "max": "Zoe Sato"
        },
        "role": {
          "nulls": 0,
          "distinct": 3,
          "min": "Manager",
          "max": "Warehouse Staff"
        },
        "hire_date": {
          "nulls": 0,
          "distinct": 50,
          "min": "2015-06-01",
          "max": "2023-11-14"
        },
        "email": {
          "nulls": 33,
          "distinct": 17
        }
      }
    }
  },
  "artifacts": [
    {
      "file": "csv/employees.csv",
      "format": "csv",
      "table": "employees",
      "key": "datasets/inventory-control-management-system/v1/csv/employees.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/csv/employees.csv",
      "bytes": 2520,
      "sha256": "2410c72158b1754906ea41b38911c2916a3827c8082b1f315ced1ec56abe7091",
      "rows": 50,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/products.csv",
      "format": "csv",
      "table": "products",
      "key": "datasets/inventory-control-management-system/v1/csv/products.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/csv/products.csv",
      "bytes": 71409,
      "sha256": "d32e8dc53927026a93259b8d0fd25b81b9cbfe74299da6497480ab56764b5e43",
      "rows": 200,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/purchase_orders.csv",
      "format": "csv",
      "table": "purchase_orders",
      "key": "datasets/inventory-control-management-system/v1/csv/purchase_orders.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/csv/purchase_orders.csv",
      "bytes": 9066,
      "sha256": "b219d855cfbfcd65d0404bc7f9500b2de2e3f45bad328e252ffc64b0e5a776e6",
      "rows": 300,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/quality_inspections.csv",
      "format": "csv",
      "table": "quality_inspections",
      "key": "datasets/inventory-control-management-system/v1/csv/quality_inspections.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/csv/quality_inspections.csv",
      "bytes": 5294,
      "sha256": "28a04d4de25937b496ef0eb937c44e14226022bbf4f2954272d5eca30bbb2b79",
      "rows": 100,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/returns.csv",
      "format": "csv",
      "table": "returns",
      "key": "datasets/inventory-control-management-system/v1/csv/returns.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/csv/returns.csv",
      "bytes": 6039,
      "sha256": "7e752501132bb9769bc123b2f74e8c924837883adef3ee5f84eefe8237cbb40b",
      "rows": 80,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/stock_adjustments.csv",
      "format": "csv",
      "table": "stock_adjustments",
      "key": "datasets/inventory-control-management-system/v1/csv/stock_adjustments.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/csv/stock_adjustments.csv",
      "bytes": 4553,
      "sha256": "29d677c5a7b8f3927c2b490abeb2cd87833fac3f4bc10230560c3c35560f9cc4",
      "rows": 150,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "inventory-control-management-system.json",
      "format": "json",
      "table": null,
      "key": "datasets/inventory-control-management-system/v1/inventory-control-management-system.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/inventory-control-management-system.json",
      "bytes": 200524,
      "sha256": "91fabe08b9594ce6ed5bd8e021694fa8f2ab9b12d4543391a1394fddf797d306",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    },
    {
      "file": "sql/postgres.sql",
      "format": "sql-postgres",
      "table": null,
      "key": "datasets/inventory-control-management-system/v1/sql/postgres.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/sql/postgres.sql",
      "bytes": 116894,
      "sha256": "7ff00e855104ce9addca0e0211e2d3106b2d7bd4d8f698475e5ff18ca4be5f72",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mysql.sql",
      "format": "sql-mysql",
      "table": null,
      "key": "datasets/inventory-control-management-system/v1/sql/mysql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/sql/mysql.sql",
      "bytes": 116169,
      "sha256": "a700eb646fc1a64b0da317f26a226931f6f56e18bcf012dbdfc1bec6a22289d0",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mssql.sql",
      "format": "sql-mssql",
      "table": null,
      "key": "datasets/inventory-control-management-system/v1/sql/mssql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/sql/mssql.sql",
      "bytes": 118675,
      "sha256": "57f50efe57a0f0388aa8370483cac80696609ec5a6db15e7eb941aea87526c32",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/sqlite.sql",
      "format": "sql-sqlite",
      "table": null,
      "key": "datasets/inventory-control-management-system/v1/sql/sqlite.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/sql/sqlite.sql",
      "bytes": 115921,
      "sha256": "37a7ccc86deb8e8aa340022a71184a786b2a45fbd7f0e6f4217e10bffaf3d720",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "prisma/seed.ts",
      "format": "prisma-seed",
      "table": null,
      "key": "datasets/inventory-control-management-system/v1/prisma/seed.ts",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/prisma/seed.ts",
      "bytes": 168674,
      "sha256": "2345d96e78ed1a20b40ad7015cc7efe6895a05d0e3497b4ee2714748ceed47ce",
      "rows": null,
      "contentType": "application/typescript; charset=utf-8"
    },
    {
      "file": "django/fixtures.json",
      "format": "django-fixture",
      "table": null,
      "key": "datasets/inventory-control-management-system/v1/django/fixtures.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/inventory-control-management-system/v1/django/fixtures.json",
      "bytes": 241605,
      "sha256": "e7dfa1db8263035952fd8aa8edbdba24efc7294e9456988b1b39e9a7c3b99f7a",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    }
  ],
  "publishedAt": 1788768604247,
  "updatedAt": 1788768604247
}