{
  "format": "dataset-manifest/1",
  "slug": "logistics-performance-metrics",
  "version": 3,
  "seed": "logistics-performance-metrics:1",
  "engine": 1,
  "engineVersion": "1.0.0",
  "spec": {
    "format": "dataset/1",
    "slug": "logistics-performance-metrics",
    "name": "Logistics Performance Metrics",
    "tagline": "Logistics performance metrics including warehouses, products, customer orders, shippers, and payment data.",
    "description": "A logistics company moves products efficiently from suppliers to customers, managing the intricate details of warehouse operations, order fulfillment, and shipping logistics. Within this operation, warehouses store various products that are then picked and packed based on customer orders. Shippers are tasked with delivering these orders to customers, ensuring timely and reliable service. Payments processed through the system track customer transactions, including the usage of coupons, which reveals insights into purchasing behavior and sales trends.\n\nEach row in the warehouses table represents a unique storage facility, detailing its location and capacity. The products table lists the items available for order, including their specifications and stock levels. Customer orders document individual requests made by clients, linking to specific products and specifying which warehouse will fulfill the order. Shippers are linked to customer orders, indicating which company is responsible for delivery, while the payments table records transaction details that correspond to each order made by a customer.\n\nThis dataset is useful for practitioners seeking to analyze logistics operations, customer purchasing trends, and payment behaviors. Developers can explore questions like: How do warehouse capacities affect order fulfillment rates? What is the average shipping time for different shippers? How do coupon promotions influence overall sales? The interplay between these tables provides the data needed to uncover insights into efficiency, service quality, and areas for improvement within the logistics process. Note that this data is synthetic, designed to simulate real-world scenarios for practice and analysis.",
    "domain": "logistics",
    "shape": "star-schema",
    "useCases": [
      "bi-dashboards",
      "analytics-demo"
    ],
    "keywords": [
      "logistics",
      "performance",
      "warehouse",
      "orders",
      "shipping",
      "payments",
      "coupons",
      "metrics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "warehouses": {
        "description": "This table contains information about the warehouses where products are stored, including location and capacity.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each warehouse."
          },
          "location": {
            "kind": "city",
            "description": "City where the warehouse is located."
          },
          "capacity": {
            "kind": "int",
            "min": 1000,
            "max": 10000,
            "description": "Maximum storage capacity of the warehouse."
          },
          "region": {
            "kind": "enum",
            "values": [
              "North",
              "South",
              "East",
              "West"
            ],
            "weights": [
              25,
              25,
              25,
              25
            ],
            "description": "Geographical region of the warehouse."
          },
          "current_stock": {
            "kind": "int",
            "min": 0,
            "max": 10000,
            "distribution": "normal",
            "description": "Current amount of products stored in the warehouse."
          }
        }
      },
      "products": {
        "description": "This table holds details about products being stored and shipped, linking to the warehouse they belong to.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each product."
          },
          "name": {
            "kind": "productName",
            "description": "Name of the product."
          },
          "warehouse_id": {
            "kind": "fk",
            "ref": "warehouses.id",
            "description": "Identifier of the warehouse where the product is stored."
          },
          "price": {
            "kind": "decimal",
            "min": 5,
            "max": 500,
            "scale": 2,
            "description": "Price of the product.",
            "distribution": "long-tail"
          },
          "category": {
            "kind": "enum",
            "values": [
              "Electronics",
              "Clothing",
              "Food",
              "Furniture"
            ],
            "weights": [
              25,
              25,
              25,
              25
            ],
            "description": "Category of the product."
          },
          "weight": {
            "kind": "decimal",
            "min": 0.1,
            "max": 50,
            "scale": 2,
            "distribution": "normal",
            "description": "Weight of the product in kilograms."
          }
        }
      },
      "customer_orders": {
        "description": "This table tracks customer orders, detailing the products ordered, shipping status, and customer information.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each customer order."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "Identifier of the ordered product."
          },
          "quantity": {
            "kind": "int",
            "min": 1,
            "max": 20,
            "description": "Quantity of the product ordered.",
            "distribution": "long-tail"
          },
          "order_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date when the order was placed.",
            "dayProfile": "waking"
          },
          "shipping_status": {
            "kind": "enum",
            "values": [
              "pending",
              "shipped",
              "delivered",
              "returned"
            ],
            "description": "Current shipping status of the order."
          }
        }
      },
      "shippers": {
        "description": "This table includes information about shipping companies responsible for delivering orders.",
        "rows": 30,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each shipper."
          },
          "name": {
            "kind": "company",
            "description": "Name of the shipping company."
          },
          "contact_info": {
            "kind": "words",
            "description": "Contact information for the shipping company."
          },
          "service_area": {
            "kind": "enum",
            "values": [
              "Local",
              "Regional",
              "National"
            ],
            "weights": [
              50,
              30,
              20
            ],
            "description": "Scope of the shipping company's delivery area."
          },
          "average_delivery_time": {
            "kind": "int",
            "min": 1,
            "max": 14,
            "distribution": "normal",
            "description": "Average delivery time in days."
          }
        }
      },
      "payments": {
        "description": "This table records payment details related to customer orders, including coupon usage.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each payment."
          },
          "order_id": {
            "kind": "fk",
            "ref": "customer_orders.id",
            "description": "Identifier of the associated customer order."
          },
          "payment_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date when the payment was made.",
            "dayProfile": "business"
          },
          "amount": {
            "kind": "decimal",
            "min": 5,
            "max": 5000,
            "scale": 2,
            "description": "Total amount paid for the order.",
            "distribution": "heavy-tail"
          },
          "coupon_used": {
            "kind": "bool",
            "trueRate": 0.3,
            "description": "Indicates if a coupon was used for the payment."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "monetary",
      "timestamped",
      "categorical"
    ],
    "analysisQuestions": [
      "What is the average order value across different warehouses?",
      "How do coupon usages affect sales performance?",
      "What shipping statuses are most common for customer orders?",
      "Which products have the highest return rates?",
      "How does order volume vary by day of the week?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Orders and Payments",
        "sql": "SELECT co.id, co.order_date, p.amount FROM customer_orders co JOIN payments p ON co.id = p.order_id",
        "dialect": "postgres"
      },
      {
        "title": "Average Order Value by Warehouse",
        "sql": "SELECT w.location, AVG(p.price * co.quantity) AS avg_order_value FROM warehouses w JOIN products p ON w.id = p.warehouse_id JOIN customer_orders co ON p.id = co.product_id GROUP BY w.location",
        "dialect": "postgres"
      },
      {
        "title": "Count Orders by Shipping Status",
        "sql": "SELECT shipping_status, COUNT(*) FROM customer_orders GROUP BY shipping_status",
        "dialect": "postgres"
      },
      {
        "title": "Payment Amounts Over Time",
        "sql": "SELECT DATE(payment_date) AS payment_day, SUM(amount) FROM payments GROUP BY payment_day ORDER BY payment_day",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Business Intelligence Dashboard",
        "body": "Data analysts at a logistics company can use this dataset to create a BI dashboard that visualizes key performance metrics. By integrating order data, payment information, and shipping statuses, analysts can provide stakeholders with insights into operational efficiency and financial health."
      },
      {
        "title": "Sales Performance Analysis",
        "body": "Marketing teams can leverage this dataset to analyze the effectiveness of promotional campaigns by examining coupon usage against sales performance. Understanding which promotions drive sales will help refine future marketing efforts and improve overall strategy."
      },
      {
        "title": "Operational Efficiency Review",
        "body": "Operations managers can utilize the data to review shipping performance and identify bottlenecks. By analyzing shipping statuses and return rates, they can make informed decisions to enhance logistics processes and improve service quality."
      },
      {
        "title": "Product Demand Forecasting",
        "body": "Product managers can exploit the dataset to forecast demand for various products by analyzing order trends and quantities. This information will aid in inventory management and ensuring that warehouses are stocked appropriately to meet customer needs."
      }
    ]
  },
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    "shippers": 30,
    "warehouses": 50,
    "products": 200,
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    "payments": 500
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