{
  "format": "dataset-manifest/1",
  "slug": "logistics-operations-overview",
  "version": 1,
  "seed": "logistics-operations-overview:1",
  "spec": {
    "format": "dataset/1",
    "slug": "logistics-operations-overview",
    "name": "Logistics Operations Overview",
    "tagline": "Shippers, suppliers, tasks, transactions, and assets in logistics operations for efficient supply chain management.",
    "description": "Shippers coordinate deliveries, suppliers provide goods, and tasks are systematically assigned to ensure seamless logistics operations. Each day, shippers work with suppliers to arrange the transportation of products, while tasks are monitored and executed to maintain efficiency. Performance metrics highlight how well these elements interact, with an emphasis on transaction details capturing each step of the supply chain journey. Asset management plays a critical role, overseeing the inventory of specialized equipment and resources required to fulfill shipping agreements.\n\nIn this dataset, the 'shippers' table tracks details about the companies responsible for delivering goods, such as their locations and performance stats. The 'suppliers' table contains information about the vendors providing those goods, including their reliability and product ranges. The 'tasks' table outlines the various assignments related to shipping operations, detailing who is responsible for what and when, while 'transactions' captures the financial exchanges and movements of goods. Lastly, the 'assets' table lists the equipment in use, which is essential for understanding the capacity and limitations of the logistics operations.\n\nRows in the 'shippers' table can be joined with the 'transactions' table using shipper IDs to analyze how specific shippers are performing based on transaction outcomes. Similarly, the 'suppliers' table connects to the 'tasks' table to evaluate how supplier performance can impact task efficiency. By investigating these connections, users can answer critical questions regarding cost-effectiveness, delivery timelines, and operational bottlenecks.\n\nThe data provides ample opportunities for practice, allowing developers to explore relationships between shipping entities, analyze transaction patterns, and assess asset utilization in logistics. This dataset is synthetic, crafted to replicate real-world scenarios while maintaining a focus on logistics operations.",
    "domain": "logistics",
    "shape": "star-schema",
    "useCases": [
      "bi-dashboards",
      "analytics-demo"
    ],
    "keywords": [
      "logistics",
      "shipping",
      "supply chain",
      "transactions",
      "performance metrics",
      "asset management"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "shippers": {
        "description": "This table contains information about the shippers involved in logistics operations, linking them to their respective suppliers.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each shipper."
          },
          "shipper_name": {
            "kind": "fullName",
            "description": "The name of the shipper."
          },
          "supplier_id": {
            "kind": "fk",
            "ref": "suppliers.id",
            "description": "Reference to the supplier associated with the shipper."
          }
        }
      },
      "suppliers": {
        "description": "This table holds information about suppliers providing goods and services to the logistics operations.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each supplier."
          },
          "supplier_name": {
            "kind": "fullName",
            "description": "The name of the supplier."
          },
          "contact_email": {
            "kind": "email",
            "from": "supplier_name",
            "description": "Contact email for the supplier."
          }
        }
      },
      "tasks": {
        "description": "This table outlines the various tasks assigned to shippers and assets within the logistics operations.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each task."
          },
          "task_name": {
            "kind": "sentence",
            "description": "Description of the task."
          },
          "shipper_id": {
            "kind": "fk",
            "ref": "shippers.id",
            "description": "Reference to the shipper responsible for the task."
          },
          "asset_id": {
            "kind": "fk",
            "ref": "assets.id",
            "nullable": true,
            "description": "Reference to the asset involved in the task."
          }
        }
      },
      "transactions": {
        "description": "This table records the transactions associated with shipping and asset management, capturing performance metrics.",
        "rows": 400,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction."
          },
          "task_id": {
            "kind": "fk",
            "ref": "tasks.id",
            "description": "Reference to the task linked to the transaction."
          },
          "transaction_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date of the transaction.",
            "dayProfile": "waking"
          },
          "amount": {
            "kind": "decimal",
            "min": 100,
            "max": 10000,
            "scale": 2,
            "description": "Monetary amount of the transaction."
          }
        }
      },
      "assets": {
        "description": "This table lists the assets used in logistics operations, including specialized equipment.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each asset."
          },
          "asset_name": {
            "kind": "productName",
            "description": "Name of the asset."
          },
          "asset_type": {
            "kind": "enum",
            "values": [
              "vehicle",
              "equipment",
              "container"
            ],
            "description": "Type of the asset."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "monetary",
      "timestamped",
      "nullable"
    ],
    "analysisQuestions": [
      "What percentage of tasks are completed on time?",
      "How do transaction amounts vary across different suppliers?",
      "Which assets are most frequently involved in tasks?",
      "What is the average transaction amount per shipper?",
      "Are there any seasonal trends in transaction volumes?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Shippers and Suppliers",
        "sql": "SELECT s.shipper_name, sp.supplier_name FROM shippers s JOIN suppliers sp ON s.supplier_id = sp.id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Tasks per Shipper",
        "sql": "SELECT s.shipper_name, COUNT(t.id) AS task_count FROM shippers s LEFT JOIN tasks t ON s.id = t.shipper_id GROUP BY s.shipper_name;",
        "dialect": "postgres"
      },
      {
        "title": "Average Transaction Amount per Asset Type",
        "sql": "SELECT a.asset_type, AVG(t.amount) AS avg_transaction FROM assets a JOIN tasks ts ON a.id = ts.asset_id JOIN transactions t ON ts.id = t.task_id GROUP BY a.asset_type;",
        "dialect": "postgres"
      },
      {
        "title": "Transactions Over Time",
        "sql": "SELECT DATE(t.transaction_date) AS transaction_day, COUNT(t.id) AS transaction_count FROM transactions t GROUP BY transaction_day ORDER BY transaction_day;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Logistics Analyst",
        "body": "A logistics analyst can use this dataset to evaluate the efficiency of shipping operations by analyzing task completion rates and transaction amounts. By identifying bottlenecks in the supply chain, they can propose improvements and optimizations."
      },
      {
        "title": "Business Intelligence Developer",
        "body": "A BI developer can leverage this dataset to create dashboards that visualize shipping performance metrics, allowing stakeholders to monitor key performance indicators and make data-driven decisions. The insights derived can be presented in regular meetings with the management team."
      },
      {
        "title": "Operations Manager",
        "body": "An operations manager can utilize this dataset to assess the utilization of assets in logistics operations. By examining the frequency of tasks associated with each asset type, they can allocate resources more effectively and plan maintenance schedules."
      },
      {
        "title": "Data Scientist",
        "body": "A data scientist can explore this dataset to identify trends in transaction volumes and the impact of seasonal variations on logistics performance. By applying statistical analysis, they can forecast future demand and recommend strategic adjustments."
      }
    ]
  },
  "counts": {
    "assets": 100,
    "suppliers": 150,
    "shippers": 200,
    "tasks": 300,
    "transactions": 400
  },
  "stats": {
    "shippers": {
      "rows": 200,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 200,
          "min": 1,
          "max": 200
        },
        "shipper_name": {
          "nulls": 0,
          "distinct": 197,
          "min": "Alice Becker",
          "max": "Zoe Martin"
        },
        "supplier_id": {
          "nulls": 0,
          "distinct": 106,
          "min": 1,
          "max": 150
        }
      }
    },
    "suppliers": {
      "rows": 150,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 150,
          "min": 1,
          "max": 150
        },
        "supplier_name": {
          "nulls": 0,
          "distinct": 147,
          "min": "Alice Diaz",
          "max": "Zoe Nakamura"
        },
        "contact_email": {
          "nulls": 0,
          "distinct": 149
        }
      }
    },
    "tasks": {
      "rows": 300,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 300,
          "min": 1,
          "max": 300
        },
        "task_name": {
          "nulls": 0,
          "distinct": 300
        },
        "shipper_id": {
          "nulls": 0,
          "distinct": 152,
          "min": 1,
          "max": 200
        },
        "asset_id": {
          "nulls": 28,
          "distinct": 93,
          "min": 1,
          "max": 100
        }
      }
    },
    "transactions": {
      "rows": 400,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 400,
          "min": 1,
          "max": 400
        },
        "task_id": {
          "nulls": 0,
          "distinct": 216,
          "min": 3,
          "max": 300
        },
        "transaction_date": {
          "nulls": 0,
          "distinct": 400,
          "min": "2023-01-01 16:30:39",
          "max": "2023-12-31 19:44:55"
        },
        "amount": {
          "nulls": 0,
          "distinct": 400,
          "min": 190.03,
          "max": 9989.87
        }
      }
    },
    "assets": {
      "rows": 100,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 100,
          "min": 1,
          "max": 100
        },
        "asset_name": {
          "nulls": 0,
          "distinct": 92,
          "min": "Classic Backpack",
          "max": "Wireless Notebook"
        },
        "asset_type": {
          "nulls": 0,
          "distinct": 3,
          "min": "container",
          "max": "vehicle"
        }
      }
    }
  },
  "artifacts": [
    {
      "file": "csv/assets.csv",
      "format": "csv",
      "table": "assets",
      "key": "datasets/logistics-operations-overview/v1/csv/assets.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/csv/assets.csv",
      "bytes": 2843,
      "sha256": "e2ce628796e41f46fa729182903ac4180ed347b66fe3ca2582c0ffb70dbd0a83",
      "rows": 100,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/suppliers.csv",
      "format": "csv",
      "table": "suppliers",
      "key": "datasets/logistics-operations-overview/v1/csv/suppliers.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/csv/suppliers.csv",
      "bytes": 6096,
      "sha256": "11fc78114f1e459fd4932b9acc613d374865885e7b848a4fa9abd6d41882e769",
      "rows": 150,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/shippers.csv",
      "format": "csv",
      "table": "shippers",
      "key": "datasets/logistics-operations-overview/v1/csv/shippers.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/csv/shippers.csv",
      "bytes": 4007,
      "sha256": "f4380b55644accfb38f5b71addc94b66ccd180f04916c1e757a2db76c3c57ff2",
      "rows": 200,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/tasks.csv",
      "format": "csv",
      "table": "tasks",
      "key": "datasets/logistics-operations-overview/v1/csv/tasks.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/csv/tasks.csv",
      "bytes": 20272,
      "sha256": "7221b336335494b1daaaa0e432eb1f90e9c3423674d11e03ebf855fb68f0994f",
      "rows": 300,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/transactions.csv",
      "format": "csv",
      "table": "transactions",
      "key": "datasets/logistics-operations-overview/v1/csv/transactions.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/csv/transactions.csv",
      "bytes": 14564,
      "sha256": "a5bbad9b0574bca1c76510d100d5a88425b788ce9c8a22c281c62018049ffc4c",
      "rows": 400,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "logistics-operations-overview.json",
      "format": "json",
      "table": null,
      "key": "datasets/logistics-operations-overview/v1/logistics-operations-overview.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/logistics-operations-overview.json",
      "bytes": 141271,
      "sha256": "4501f0dccd9604632a1ed9334bbd1fe0fcf089da74bf113710575fbd17b89b62",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    },
    {
      "file": "sql/postgres.sql",
      "format": "sql-postgres",
      "table": null,
      "key": "datasets/logistics-operations-overview/v1/sql/postgres.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/sql/postgres.sql",
      "bytes": 62990,
      "sha256": "a72c6dd4edc2a0813e0c619f141c58ad3a9f3800d7b40e6964364efb4d2e95d4",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mysql.sql",
      "format": "sql-mysql",
      "table": null,
      "key": "datasets/logistics-operations-overview/v1/sql/mysql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/sql/mysql.sql",
      "bytes": 62442,
      "sha256": "4eb2d52f2d2d7e082704b9200d8b8d7773f888f9497074238ffb381e4dd53c88",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mssql.sql",
      "format": "sql-mssql",
      "table": null,
      "key": "datasets/logistics-operations-overview/v1/sql/mssql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/sql/mssql.sql",
      "bytes": 64162,
      "sha256": "e94285fcae25abf576adae6bf6b8630ca22b33ecdb93b1a399f9b431a07fefd3",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/sqlite.sql",
      "format": "sql-sqlite",
      "table": null,
      "key": "datasets/logistics-operations-overview/v1/sql/sqlite.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/sql/sqlite.sql",
      "bytes": 62267,
      "sha256": "3a16be1f28d32c859fd6f4f4095a6ce4295bdd8e7b58dea016c9fae490d45193",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "prisma/seed.ts",
      "format": "prisma-seed",
      "table": null,
      "key": "datasets/logistics-operations-overview/v1/prisma/seed.ts",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/prisma/seed.ts",
      "bytes": 118863,
      "sha256": "d42bbcd2f529c2ceb8d664baf95db3745ad24afa9a46827890098e1987c2fdb7",
      "rows": null,
      "contentType": "application/typescript; charset=utf-8"
    },
    {
      "file": "django/fixtures.json",
      "format": "django-fixture",
      "table": null,
      "key": "datasets/logistics-operations-overview/v1/django/fixtures.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-operations-overview/v1/django/fixtures.json",
      "bytes": 190072,
      "sha256": "677dd1fe7c85132d47c2f69f389e967ed76902bfed013c8a32b1cda45ae642e9",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    }
  ],
  "publishedAt": 1788766760780,
  "updatedAt": 1788766760780
}