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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

Group	Column name	Dataset	Definition
Customer	Customer ID	orders_and_shipments.csv	Unique customer identification
Customer	Customer Market	orders_and_shipments.csv	Geographic grouping of customer countries, with values such as Europe, LATAM, Pacific Asia, etc.
Customer	Customer Region	orders_and_shipments.csv	Geographic grouping of customer countries, with values such as Northern Europe, Western Europe, etc.
Customer	Customer Country	orders_and_shipments.csv	Customer's country
Order info	Order ID	orders_and_shipments.csv	Unique Order identification. Order groups one or multiple Order Items
Order info	Order Item ID	orders_and_shipments.csv	Unique Order Item identification. Order Item always belong to just one Order
Order info	Order Year	orders_and_shipments.csv	Year of the order
Order information	Order Month	orders_and_shipments.csv	Month of the order
Order information	Order Day	orders_and_shipments.csv	Day of the order
Order information	Order Time	orders_and_shipments.csv	Timestamp of the order in UTC
Order information	Order Quantity	orders_and_shipments.csv	The amount of items that were ordered within a given Order Item (1 record of the data)
Product	Product Department	orders_and_shipments.csv	Product grouping into categories such as Fitness, Golf, Pet Shop, etc.
Product	Product Category	orders_and_shipments.csv	Product grouping into categories such as Sporting Goods, Women's Apparel, etc.
Product	Product Name	orders_and_shipments.csv	The name of the purchased product
Sales	Gross Sales	orders_and_shipments.csv	Revenue before discounts generated by the sales of the Order Item (1 record of the data)
Sales	Discount %	orders_and_shipments.csv	Discount % applied on the catalog price
Sales	Profit	orders_and_shipments.csv	Profit generated by the sales of the Order Item (1 record of data)
Shipment information	Shipment Year	orders_and_shipments.csv	Year of the shipment
Shipment information	Shipment Month	orders_and_shipments.csv	Month of the shipment
Shipment information	Shipment Day	orders_and_shipments.csv	Day of the shipment
Shipment information	Shipment Mode	orders_and_shipments.csv	Information on how the shipment has been dispatched, with values as First Class, Same Day, Second Class, etc.
Shipment information	Shipment Days - Scheduled	orders_and_shipments.csv	Information on typical amount of days needed to dispatch the goods from the moment the order has been placed
Warehouse	Warehouse Country	orders_and_shipments.csv	Country of the warehouse that has fulfilled this order, the only two values being Puerto Rico and USA
Inventory & Fulfillment	Warehouse Inventory	inventory.csv	The monthly level of inventory of a product, e.g. 930 units
Inventory & Fulfillment	Inventory cost per unit	inventory.csv	The monthly storage cost per unit of inventory, e.g. $2.07
Inventory & Fulfillment	Warehouse Order fulfillment (days)	fulfillment.csv	The 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:

Use this Workspace to prepare your data (optional).
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.
Create a screenshot of your (main) Tableau or Power BI dashboard, and paste that into the designated field.
Summarize your findings in an executive summary.

STEP 1: DATA PREPARATION AND INITIAL DATA EXPLORATION

import pandas as pd
data1 = pd.read_csv("data/orders_and_shipments.csv")
data1.isnull().sum()

data2 = pd.read_csv("data/inventory.csv")
data2.isnull().sum()

data1.info()
data2.info()

# Stats of data1
data1.describe()

# 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
data1.head()

# 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)

data1.head()

# 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.

STEP 2: 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

CATEGORY	WEIGHTING	DETAILS
Visualizations	35%	Appropriateness of visualizations used. Clarity of insight from visualizations.
Insights	25%	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.
Storytelling	25%	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.
Votes	15%	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.