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Competition - Supply Chain Analytics
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  • Supply Chain Analytics in Tableau or Power BI

    📖 Background

    Test your BI skills on a real-world dataset focusing on supply chain analytics. As the main data analyst for Just In Time, you will help solve key shipment and inventory management challenges, analyze supply chain inefficiencies, and create insightful dashboards to inform business stakeholders about potential problems and propose structural business improvements.

    Be creative and make use of your full skillset! Use this Workspace to prepare your data, import the tables into your local Tableau or Power BI instance, and share your insights below.

    The end goal will be a (set of) interactive dashboards that demonstrate clear insights for Just In Time.

    💾 The data

    GroupColumn nameDatasetDefinition
    CustomerCustomer IDorders_and_shipments.csvUnique customer identification
    CustomerCustomer Marketorders_and_shipments.csvGeographic grouping of customer countries, with values such as Europe, LATAM, Pacific Asia, etc.
    CustomerCustomer Regionorders_and_shipments.csvGeographic grouping of customer countries, with values such as Northern Europe, Western Europe, etc.
    CustomerCustomer Countryorders_and_shipments.csvCustomer's country
    Order infoOrder IDorders_and_shipments.csvUnique Order identification. Order groups one or multiple Order Items
    Order infoOrder Item IDorders_and_shipments.csvUnique Order Item identification. Order Item always belong to just one Order
    Order infoOrder Yearorders_and_shipments.csvYear of the order
    Order informationOrder Monthorders_and_shipments.csvMonth of the order
    Order informationOrder Dayorders_and_shipments.csvDay of the order
    Order informationOrder Timeorders_and_shipments.csvTimestamp of the order in UTC
    Order informationOrder Quantityorders_and_shipments.csvThe amount of items that were ordered within a given Order Item (1 record of the data)
    ProductProduct Departmentorders_and_shipments.csvProduct grouping into categories such as Fitness, Golf, Pet Shop, etc.
    ProductProduct Categoryorders_and_shipments.csvProduct grouping into categories such as Sporting Goods, Women's Apparel, etc.
    ProductProduct Nameorders_and_shipments.csvThe name of the purchased product
    SalesGross Salesorders_and_shipments.csvRevenue before discounts generated by the sales of the Order Item (1 record of the data)
    SalesDiscount %orders_and_shipments.csvDiscount % applied on the catalog price
    SalesProfitorders_and_shipments.csvProfit generated by the sales of the Order Item (1 record of data)
    Shipment informationShipment Yearorders_and_shipments.csvYear of the shipment
    Shipment informationShipment Monthorders_and_shipments.csvMonth of the shipment
    Shipment informationShipment Dayorders_and_shipments.csvDay of the shipment
    Shipment informationShipment Modeorders_and_shipments.csvInformation on how the shipment has been dispatched, with values as First Class, Same Day, Second Class, etc.
    Shipment informationShipment Days - Scheduledorders_and_shipments.csvInformation on typical amount of days needed to dispatch the goods from the moment the order has been placed
    WarehouseWarehouse Countryorders_and_shipments.csvCountry of the warehouse that has fulfilled this order, the only two values being Puerto Rico and USA
    Inventory & FulfillmentWarehouse Inventoryinventory.csvThe monthly level of inventory of a product, e.g. 930 units
    Inventory & FulfillmentInventory cost per unitinventory.csvThe monthly storage cost per unit of inventory, e.g. $2.07
    Inventory & FulfillmentWarehouse Order fulfillment (days)fulfillment.csvThe average amount of days it takes to refill stock if inventory drops below zero

    The data can be downloaded from the sidebar on the left (under Files).

    💪 Challenge

    Using either Tableau or Power BI, create an interactive dashboard to summarize your research. Things to consider:

    1. Use this Workspace to prepare your data (optional).
    2. Some ideas to get you started: visualize how shipments are delayed, by country, product, and over time. Analyze products by their supply versus demand ratio. Rank products by over or understock. Don't feel limited by these, you're encouraged to use your skills to consolidate as much information as possible.
    3. Create a screenshot of your (main) Tableau or Power BI dashboard, and paste that into the designated field.
    4. Summarize your findings in an executive summary.


    import pandas as pd
    data1 = pd.read_csv("data/orders_and_shipments.csv")
    data2 = pd.read_csv("data/inventory.csv")
    # Stats of data1
    # Combine order year, month, and day into order_Date
    # Convert order year, month, and day to string
    data1['Order Year'] = data1[' Order Year '].astype(str)
    data1[' Order Month '] = data1[' Order Month '].astype(str)
    data1[' Order Day '] = data1[' Order Day '].astype(str)
    # Combine order year, month, and day into order_Date
    data1['order_Date'] = data1['Order Year'] + '-' + data1[' Order Month '] + '-' + data1[' Order Day ']
    # Display the updated dataframe
    # Convert Order Time to string
    data1['Order Time'] = data1['Order Time'].astype(str)
    # Combine Order Year, Order Month, and Order Day into Order_Date
    data1['Order_Date'] = data1['Order Year'].astype(str) + '-' + data1[' Order Month '] + '-' + data1[' Order Day ']
    # Combine Shipment Month, Shipment Day, and Shipment Year into Shipment_Date
    data1['Shipment_Date'] = data1['Shipment Month'].astype(str) + '-' + data1['Shipment Day'].astype(str) + '-' + data1['Shipment Year'].astype(str)
    # Export data1 to CSV
    data1.to_csv('data2.csv', index=False)

    There are no missing data. So the data can be used for data visualization.


    ✅ Checklist before publishing

    • If you use Tableau, don't forget to publish your Tableau dashboard, make it available on Tableau Public and share the link.
    • If you use Power BI, upload your .pbix file to this Workspace through the sidebar on the left (under Files).
    • Remove redundant text cells like the background, data, challenge, and checklist. You can add cells if necessary.

    ✍️ Judging criteria

    • Appropriateness of visualizations used.
    • Clarity of insight from visualizations.
    • Clarity of insights - how clear and well presented the insights are.
    • Quality of recommendations - are appropriate analytical techniques used & are the conclusions valid?
    • Number of relevant insights found for the target audience.
    • How well the data and insights are connected to tell a story.
    • How the narrative and whole report connects together.
    • How balanced the report is: in-depth enough but also concise.
    • Up voting - most upvoted entries get the most points.