Skip to content
0

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
data = pd.read_csv("data/orders_and_shipments.csv")
data
Spinner
DataFrameas
Products_Omitted
variable
[6]
--Exclude Product Names that have NULL matches between Inventory table and Orders table.
SELECT i."Product Name", o."Product Name"
FROM 'data/inventory.csv' AS i
LEFT JOIN 'data/orders_and_shipments.csv' AS o
	ON i."Product Name" = o."Product Name"
WHERE o."Product Name" IS NULL
GROUP BY o."Product Name", i."Product Name"
LIMIT 10;
Spinner
DataFrameas
shipment_days
variable
--For each shipment_mode, determine number of days to ship from order date
SELECT "Shipment Mode", AVG("Shipment Days - Scheduled") AS avg_ship
FROM 'data/orders_and_shipments.csv'
GROUP BY "Shipment Mode"
LIMIT 10;
Spinner
DataFrameas
Orders
variable
SELECT 
	"Order YearMonth",
	LEFT(MAKE_DATE("Order Year", "Order Month", "Order Day"),11)  AS OrderDate,
	LEFT(MAKE_DATE("Shipment Year", "Shipment Month", "Shipment Day"),11) AS ShipDate,
	"Customer Market",
	"Customer Region",
	"Customer Country",
	"Customer ID",
	"Order ID",
	"Order Item ID" AS OrderItemId,
	"Product Category",
	"Product Department",
	"Product Name",
	"Shipment Mode",
	"Warehouse Country",
	"Gross Sales",
	"Order Quantity",
	"Profit",
	"Discount %"
FROM 'data/orders_and_shipments.csv';
Spinner
DataFrameas
ShipDate_OrderDate
variable
/* Assuming OrderDate is the DateOrderCreated and ShipDate is created after placing an Order. 
With this assumption ShipDate should not be earlier than OrderDate.
Exclude Order_Item_Id where ShipDate is less than OrderDate. 
Assume reasons for this happening can be due to system error, human error (typo), etc.
Total of 2735 rows excluded from data set.
*/

SELECT OrderDate, ShipDate, OrderItemId, 
	DATEDIFF('day', CAST(OrderDate AS DATE), 
	CAST(ShipDate AS DATE)) AS diff
FROM Orders
GROUP BY OrderItemId, OrderDate, ShipDate
HAVING diff < 0
ORDER BY diff

✅ 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

CATEGORYWEIGHTINGDETAILS
Visualizations35%
  • Appropriateness of visualizations used.
  • Clarity of insight from visualizations.
Insights25%
  • 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.
Storytelling25%
  • 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.
Votes15%
  • Up voting - most upvoted entries get the most points.

🧾 Executive summary

In a couple of lines, write your main findings here.

📷 Dashboard screenshot

Paste one screenshot of your Tableau or Power BI dashboard here.

🌐 Upload your dashboard

For Tableau: paste the link to your Tableau Public dashboard here.

For Power BI: upload your .pbix file to Files in the sidebar on the left.

⌛️ Time is ticking. Good luck!