{
  "format": "dataset-manifest/1",
  "slug": "fashion-design-software",
  "version": 1,
  "seed": "fashion-design-software:1",
  "spec": {
    "format": "dataset/1",
    "slug": "fashion-design-software",
    "name": "Fashion Logistics Insights",
    "tagline": "Tables of materials, garment orders, shipments, promotional events, and returns from the fashion logistics sector.",
    "description": "Fashion designers manage complex operations to ensure the timely delivery of materials and the efficient processing of garment orders. They oversee everything from fabric procurement to the logistics of shipping finished products. Each day, teams monitor material inventory alongside garment types, ensuring they keep pace with market trends and customer demands. Key activities often align with promotional events, where timely shipments are crucial for capturing seasonal interest and maximizing sales opportunities. Designers must also be mindful of returns, which, while relatively low, can spike during off-peak seasons, impacting overall sales strategy.\n\nThe dataset includes several related tables that encapsulate different aspects of this logistical operation. The `materials` table contains records of various fabrics and components, detailing their specifications and current stock levels. Each material is linked to specific `garment_orders`, which represent customer requests for finished clothing items. These orders also relate to the `shipments` table, tracking deliveries and the logistics involved in getting products from the warehouse to retailers or directly to customers.\n\nAdditionally, the `promotional_events` table provides insights into marketing strategies employed during peak seasons, revealing patterns in material orders and shipment timing. Finally, the `returns` table captures information about garments that are sent back, offering data to analyze customer satisfaction and product quality. By exploring these relationships, practitioners can gain valuable insights into the fashion logistics landscape, answering questions about inventory management, seasonal trends, and the effectiveness of promotional activities.\n\nThis dataset is synthetic, modeled to simulate the kinds of data a logistics operation in the fashion industry would produce, allowing users to practice and develop their analytical skills.",
    "domain": "logistics",
    "shape": "event-log",
    "useCases": [
      "analytics-demo",
      "etl-pipelines"
    ],
    "keywords": [
      "fashion",
      "logistics",
      "materials",
      "shipments",
      "inventory",
      "promotions",
      "events",
      "data analysis"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "materials": {
        "description": "This table contains information about various fabric types used in fashion design. Each row represents a unique fabric with attributes including type and current inventory levels.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each fabric."
          },
          "fabric_type": {
            "kind": "enum",
            "values": [
              "Cotton",
              "Silk",
              "Linen",
              "Wool",
              "Polyester"
            ],
            "description": "Type of fabric used."
          },
          "inventory_level": {
            "kind": "int",
            "min": 0,
            "max": 1000,
            "description": "Current inventory level of the fabric.",
            "nullable": true,
            "when": {
              "column": "fabric_type",
              "in": [
                "Cotton",
                "Silk",
                "Linen",
                "Wool",
                "Polyester"
              ]
            }
          }
        }
      },
      "garment_orders": {
        "description": "This table tracks orders for garments made from various fabrics. Each order includes details about the garment type, quantity, and associated materials.",
        "rows": 350,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each garment order."
          },
          "garment_type": {
            "kind": "enum",
            "values": [
              "Dress",
              "Shirt",
              "Pants",
              "Skirt",
              "Jacket"
            ],
            "description": "Type of garment ordered."
          },
          "quantity": {
            "kind": "int",
            "min": 1,
            "max": 100,
            "description": "Number of garments ordered."
          },
          "material_id": {
            "kind": "fk",
            "ref": "materials.id",
            "description": "Reference to the material used for the garment."
          }
        }
      },
      "shipments": {
        "description": "This table records the shipments of materials to fashion designers. Each shipment includes details like the shipment date, status, and associated garment orders.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each shipment."
          },
          "shipment_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date when the shipment was made.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
                "Shipped",
                "Delivered"
              ]
            },
            "dayProfile": "business"
          },
          "status": {
            "kind": "enum",
            "values": [
              "Pending",
              "Shipped",
              "Delivered"
            ],
            "description": "Current status of the shipment."
          },
          "garment_order_id": {
            "kind": "fk",
            "ref": "garment_orders.id",
            "description": "Reference to the garment order associated with the shipment."
          }
        }
      },
      "promotional_events": {
        "description": "This table outlines promotional events tied to garment launches. Each event includes the date, type, and linked garment orders.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each promotional event."
          },
          "event_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date of the promotional event.",
            "nullable": true,
            "when": {
              "column": "event_type",
              "in": [
                "Launch",
                "Sale",
                "Exhibition"
              ]
            },
            "dayProfile": "waking",
            "trend": "seasonal"
          },
          "event_type": {
            "kind": "enum",
            "values": [
              "Launch",
              "Sale",
              "Exhibition"
            ],
            "description": "Type of promotional event."
          },
          "garment_order_id": {
            "kind": "fk",
            "ref": "garment_orders.id",
            "description": "Reference to the garment order associated with the promotional event."
          }
        }
      },
      "returns": {
        "description": "This table captures information about garment returns. Each return entry links to the original garment order and includes the return reason.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each return."
          },
          "return_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date the return was processed.",
            "nullable": true,
            "when": {
              "column": "reason",
              "in": [
                "Defective",
                "Wrong Size",
                "Changed Mind"
              ]
            },
            "dayProfile": "business"
          },
          "reason": {
            "kind": "enum",
            "values": [
              "Defective",
              "Wrong Size",
              "Changed Mind"
            ],
            "description": "Reason for the return."
          },
          "garment_order_id": {
            "kind": "fk",
            "ref": "garment_orders.id",
            "description": "Reference to the garment order that was returned."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "nullable",
      "high-cardinality",
      "timestamped"
    ],
    "analysisQuestions": [
      "How do material orders vary before major promotional events?",
      "What are the most common reasons for garment returns?",
      "Which fabric types have the highest inventory turnover?",
      "What is the average time from shipment to delivery for different garment types?",
      "How does the quantity of garment orders change during promotional events?"
    ],
    "suggestedQueries": [
      {
        "title": "List all garment orders with associated materials",
        "sql": "SELECT g.id, g.garment_type, g.quantity, m.fabric_type FROM garment_orders g JOIN materials m ON g.material_id = m.id;",
        "dialect": "postgres"
      },
      {
        "title": "Count shipments by status",
        "sql": "SELECT status, COUNT(*) FROM shipments GROUP BY status;",
        "dialect": "postgres"
      },
      {
        "title": "Average quantity of garments ordered per fabric type",
        "sql": "SELECT m.fabric_type, AVG(g.quantity) FROM garment_orders g JOIN materials m ON g.material_id = m.id GROUP BY m.fabric_type;",
        "dialect": "postgres"
      },
      {
        "title": "Returns by reason over time",
        "sql": "SELECT r.reason, COUNT(*) FROM returns r JOIN garment_orders g ON r.garment_order_id = g.id GROUP BY r.reason, r.return_date ORDER BY r.return_date;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Fashion Designers Analyzing Trends",
        "body": "Fashion designers can utilize this dataset to analyze trends in material usage before major promotional events. By identifying which fabrics are in high demand, designers can better align their inventory with upcoming collections, ensuring they have the necessary materials on hand for production."
      },
      {
        "title": "Logistics Managers Optimizing Shipments",
        "body": "Logistics managers can use this dataset to track the status of shipments and optimize delivery schedules. By understanding the average time from shipment to delivery, they can improve efficiency and ensure that materials arrive on time for fashion launches."
      },
      {
        "title": "Data Analysts Exploring Returns",
        "body": "Data analysts can leverage this dataset to explore patterns in garment returns. By analyzing the reasons for returns, they can provide insights to design teams on potential improvements in product quality and sizing, helping to reduce future returns."
      },
      {
        "title": "Marketing Teams Planning Promotions",
        "body": "Marketing teams can access this dataset to plan promotional events effectively. By correlating garment orders with promotional events, they can strategize marketing campaigns that align with inventory levels, maximizing sales during key periods."
      }
    ]
  },
  "counts": {
    "materials": 200,
    "garment_orders": 350,
    "promotional_events": 100,
    "returns": 50,
    "shipments": 150
  },
  "stats": {
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