{
  "format": "dataset-manifest/1",
  "slug": "freight-management-insights",
  "version": 4,
  "seed": "freight-management-insights:1",
  "spec": {
    "format": "dataset/1",
    "slug": "freight-management-insights",
    "name": "Freight Management Insights",
    "tagline": "Customer orders, delivery routes, product availability, and support tickets in logistics operations.",
    "description": "Customers place orders for various products needed for their businesses, and this business ensures those orders are fulfilled efficiently. Each order is linked to a specific customer, detailing what products are requested and their respective delivery routes. The logistics team meticulously plans the routes for deliveries, taking into account factors like distance, traffic patterns, and product availability at warehouses. This optimization is crucial, as timely deliveries are essential to maintaining customer satisfaction and loyalty.\n\nThe customers table contains information about the individual clients, including their locations and business needs. Each order in the orders table is mapped to a customer, linking back to the customers table, while the products table lists the available items, detailing specifications and stock levels. The routes table provides data on the planned logistics pathways for deliveries, crucial for assessing delivery efficiency. Finally, the support_tickets table captures customer inquiries, ranging from routine questions to urgent issues regarding delivery delays or product availability, offering insight into customer service performance.\n\nThis dataset can answer vital questions around order fulfillment rates, product demand variations by region, and the effectiveness of delivery routes. Analyzing support tickets can uncover patterns related to common delivery issues, helping to enhance service quality. Practicing with this data allows developers to explore relationships between customer behavior, product performance, and logistical challenges in a realistic logistics environment. It’s important to note that all data included is synthetic, designed to mimic real-world scenarios while ensuring privacy and confidentiality.",
    "domain": "logistics",
    "shape": "slowly-changing",
    "useCases": [
      "etl-pipelines",
      "teaching"
    ],
    "keywords": [
      "logistics",
      "freight",
      "orders",
      "delivery",
      "inventory",
      "support",
      "routes",
      "customers"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "This table holds information about customers, linking to their orders and support tickets. Each customer has a unique ID.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each customer."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the customer."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "Email address of the customer."
          },
          "city": {
            "kind": "city",
            "description": "City where the customer is located.",
            "nullable": true
          },
          "country": {
            "kind": "country",
            "from": "city",
            "description": "Country where the customer resides.",
            "nullable": true
          }
        }
      },
      "orders": {
        "description": "This table captures details of customer orders, linking to the customers and products involved. Each order is uniquely identified.",
        "rows": 600,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each order."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Reference to the customer who placed the order."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "Reference to the product ordered."
          },
          "order_date": {
            "kind": "datetime",
            "from": "2021-01-01",
            "to": "2023-12-31",
            "description": "Date when the order was placed.",
            "dayProfile": "waking"
          },
          "quantity": {
            "kind": "int",
            "min": 1,
            "max": 10,
            "description": "Quantity of the product ordered.",
            "nullable": true
          }
        }
      },
      "products": {
        "description": "This table contains details about products available for order, linking to their inventory levels.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each product."
          },
          "product_name": {
            "kind": "productName",
            "description": "Name of the product."
          },
          "stock_level": {
            "kind": "int",
            "min": 0,
            "max": 100,
            "description": "Current stock level of the product.",
            "nullable": true
          },
          "category": {
            "kind": "enum",
            "values": [
              "Electronics",
              "Furniture",
              "Clothing"
            ],
            "description": "Category of the product.",
            "weights": [
              6,
              2,
              2
            ]
          }
        }
      },
      "routes": {
        "description": "This table details delivery routes, linking to orders and providing logistics information.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each route."
          },
          "order_id": {
            "kind": "fk",
            "ref": "orders.id",
            "description": "Reference to the order associated with this route."
          },
          "route_distance": {
            "kind": "decimal",
            "min": 1,
            "max": 1000,
            "scale": 2,
            "description": "Distance of the delivery route in kilometers.",
            "distribution": "normal"
          },
          "estimated_time": {
            "kind": "decimal",
            "min": 0.1,
            "max": 24,
            "scale": 2,
            "description": "Estimated delivery time in hours.",
            "distribution": "long-tail"
          }
        }
      },
      "support_tickets": {
        "description": "This table tracks support tickets related to customer orders, linking to the customers for follow-up.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each support ticket."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Reference to the customer who submitted the ticket."
          },
          "issue": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Description of the issue reported by the customer.",
            "nullable": true,
            "pool": [
              "delay",
              "damage",
              "missing",
              "billing",
              "shipment",
              "tracking",
              "service",
              "communication",
              "error",
              "invoice",
              "schedule",
              "pickup",
              "delivery",
              "customs",
              "notification",
              "request",
              "refund",
              "claim",
              "process",
              "confirmation",
              "discrepancy",
              "quality",
              "inspection",
              "status",
              "support",
              "resolution",
              "feedback",
              "system",
              "policy",
              "procedure",
              "solution",
              "problem",
              "assistance",
              "information",
              "update",
              "logistics",
              "transport",
              "warehouse",
              "dispatch",
              "route",
              "availability",
              "scheduling",
              "coordination",
              "performance",
              "customer",
              "response",
              "issue",
              "ticket",
              "case",
              "priority",
              "escalation",
              "contact",
              "agent"
            ]
          },
          "created_at": {
            "kind": "datetime",
            "from": "2021-01-01",
            "to": "2023-12-31",
            "description": "Date when the support ticket was created.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
                "closed"
              ]
            },
            "dayProfile": "business"
          },
          "status": {
            "kind": "enum",
            "values": [
              "open",
              "closed",
              "pending"
            ],
            "description": "Current status of the support ticket.",
            "weights": [
              1,
              8,
              3
            ]
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "nullable",
      "timestamped"
    ],
    "analysisQuestions": [
      "What are the most common support issues reported by customers?",
      "Which products have the highest stock levels and how does this impact sales?",
      "How do delivery times vary across different routes and orders?",
      "What trends can be observed in customer orders over time?",
      "Which regions have the highest demand for specific product categories?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Customers and Orders",
        "sql": "SELECT customers.full_name, orders.order_date FROM customers JOIN orders ON customers.id = orders.customer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Orders per Customer",
        "sql": "SELECT customer_id, COUNT(*) FROM orders GROUP BY customer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Route Distance by Product Category",
        "sql": "SELECT products.category, AVG(routes.route_distance) FROM routes JOIN orders ON routes.order_id = orders.id JOIN products ON orders.product_id = products.id GROUP BY products.category;",
        "dialect": "postgres"
      },
      {
        "title": "Support Ticket Status Counts",
        "sql": "SELECT status, COUNT(*) FROM support_tickets GROUP BY status;",
        "dialect": "postgres"
      },
      {
        "title": "Monthly Order Trends",
        "sql": "SELECT DATE_TRUNC('month', order_date) AS month, COUNT(*) FROM orders GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Logistics Analyst",
        "body": "A logistics analyst can use this dataset to analyze customer order trends, identifying peak times for orders, and correlating them with delivery route performance. This analysis will help in optimizing operational efficiency and improving customer satisfaction."
      },
      {
        "title": "Data Science Educator",
        "body": "An educator in data science might use this dataset to teach students about relational databases and SQL querying. By using real-world examples from logistics, students can practice writing complex queries and performing data analysis."
      },
      {
        "title": "Customer Support Manager",
        "body": "A customer support manager can leverage this dataset to track common support issues reported by customers. By analyzing the data, they can identify areas for improvement in the delivery process and enhance customer service strategies."
      },
      {
        "title": "Inventory Manager",
        "body": "An inventory manager can use the product and order data to forecast stock requirements based on historical order trends. This will ensure that inventory levels are optimized and that popular products are always available for customers."
      }
    ]
  },
  "counts": {
    "customers": 300,
    "products": 200,
    "orders": 600,
    "routes": 150,
    "support_tickets": 100
  },
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          "max": "Zoe Tanaka"
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        },
        "country": {
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