Supplier Performance Dashboard

Build an interactive Power BI dashboard from 180,000+ supply chain orders.

Introduction

30 Second Summary

Everyone has experienced the frustration of a late delivery. Now imagine you are a company managing 180,000 orders across five global markets. You need to know exactly which suppliers and shipping methods are causing the delays.

In this project, you will build an interactive supplier performance dashboard in Power BI using a real-world supply chain dataset. You will clean the data, create visualizations, and add interactivity so anyone can explore the delivery problems themselves.

What You'll Build

A polished, multi-page dashboard where you click a shipping mode or region and watch every chart instantly update to reveal where deliveries are failing and why.

By the end of this project, you'll have:

A KPI summary row showing total sales, order volume, and profit at a glance, with a donut chart proving that over half of all orders arrive late.
Interactive slicers that let you (or a hiring manager) click a shipping mode or market region and watch every visual on the page filter instantly.
A dedicated Shipping Analysis page comparing actual vs. scheduled shipping days, pinpointing which delivery methods consistently miss their targets.
Secret Mission: Write a custom DAX measure to calculate on-time delivery percentage and apply conditional formatting to highlight problem regions in red.

Are there any prerequisites?

You need a Windows PC and basic familiarity with Excel (opening files, reading columns). No prior Power BI experience is required.

Power BI Desktop is free and you will install it in Step 1.

Before We Start

Before we dive in, let's lock in what you're about to build and why it matters. This is your chance to connect the project to your goals before any hands-on work begins.

Set Up Power BI and Download the Dataset

To build a supplier performance dashboard, you need two things on your machine: the visualization tool and the data to analyze.

In this step, you will install Power BI Desktop (free on Windows) and download a real supply chain dataset containing over 180,000 order records.

In this step, get ready to:

Install and launch Power BI Desktop.
Download the DataCo supply chain dataset.
Create and save your Power BI project file.

Verify Power BI Desktop is installed

Power BI Desktop is a free Windows application for building interactive data reports. Let's check if it is already on your machine.

Press the Windows key to open your search bar.
Type Power BI Desktop and press Enter.

✔️ Power BI Desktop opened

You see Power BI Desktop with its Home ribbon and blank canvas. You are ready to continue.

If a welcome screen or sign-in dialog appeared, close it by clicking the X in the top-right corner of the dialog. You do not need to sign in for this project.

ⓧ I can't find Power BI Desktop

Power BI Desktop is not installed yet. The fastest way to get it is through the Microsoft Store.

Press the Windows key, type Microsoft Store, and press Enter to open it.
In the Microsoft Store search bar, type Power BI Desktop and select the result published by Microsoft.
Click Install (or Get). Wait for the download and installation to complete.
Click Open to launch Power BI Desktop.
If a welcome screen or sign-in dialog appears, close it by clicking the X in the top-right corner. You do not need to sign in.

Alternative install method

If the Microsoft Store is blocked on your machine, download the installer directly from the Microsoft Download Center at https://www.microsoft.com/en-us/download/details.aspx?id=58494. Run PBIDesktopSetup_x64.exe and follow the setup wizard.

You should now see a blank report canvas with the Home ribbon at the top and the Visualizations and Fields panes on the right.

Seeing a gray window or error on launch?

This is usually caused by antivirus software interfering with WebView2. Try temporarily disabling your antivirus and relaunching Power BI Desktop.

If the issue persists, help me troubleshoot Power BI Desktop not opening.

Download the supply chain dataset

The dataset you will analyze comes from the DataCo Smart Supply Chain repository on GitHub. It contains 180,000+ real order records with delivery statuses, shipping modes, and profit figures.

First, create a project folder. Open File Explorer and navigate to your C:\ drive.
Right-click in the empty space, select New then Folder.
Name the folder SupplyChainProject.

Open your browser and navigate to the DataCo repository:

https://github.com/ashishpatel26/DataCo-SMART-SUPPLY-CHAIN-FOR-BIG-DATA-ANALYSIS

In the file list, click the file named DataCoSupplyChainDataset.csv.
Click the download icon (downward arrow) in the top-right of the file view to download the raw file.
In the Save dialog, navigate to C:\SupplyChainProject\ and click Save.

Now verify the file downloaded correctly.

Open Excel and use File then Open to open DataCoSupplyChainDataset.csv from your C:\SupplyChainProject\ folder.
Confirm you see columns like Delivery Status, Shipping Mode, Sales, and Late_delivery_risk with thousands of rows of data.
Close Excel once you have confirmed the data is there. You do not need to save any changes.

Don't see the download icon on GitHub?

Make sure you clicked the file name first to open the file view. The download icon appears at the top-right of the file preview page, not the repository's main page.

If the file is too large to preview, look for a Download raw file button instead.

Still having trouble? Help me download the CSV file from GitHub.

Create your Power BI project file

Saving a .pbix file now gives you a working file you can return to throughout this project.

Switch back to Power BI Desktop.
Click File in the top-left corner.
Select Save As.
Navigate to C:\SupplyChainProject\.
Type Supplier_Performance_Dashboard as the file name and click Save.

The title bar now shows Supplier_Performance_Dashboard. Your project file is saved.

Open File Explorer and navigate to C:\SupplyChainProject\.
Confirm you see both DataCoSupplyChainDataset.csv and Supplier_Performance_Dashboard.pbix in the folder.

Can't find the Save As option?

Make sure you clicked File in Power BI Desktop's own menu ribbon, not the Windows title bar. The Save As option appears in the left-hand navigation after clicking File.

Need more help? Help me save my Power BI file.

Your environment is ready. Power BI Desktop is installed, the dataset is saved locally, and your project file is created. Next up, you will import this data into Power BI and build your first chart.

Import Data and Build Your First Visual

Your Power BI environment is set up and your dataset is ready. Now it is time to load that data and see what story it tells.

Within minutes of importing your supply chain data, you will discover the main pattern hiding in 180,000+ orders. Most deliveries are late. Your first chart will make that problem impossible to ignore.

In this step, get ready to:

Import the supply chain CSV into Power BI using Get Data.
Build a bar chart that reveals the delivery problem at a glance.
Add a KPI card showing total order volume.

Import the CSV into Power BI

Power BI needs a data source before it can visualize anything. You will connect it to your CSV file so all 180,000+ rows become available for analysis.

In Power BI Desktop, click Home in the ribbon at the top.
Click Get Data, then select Text/CSV.
Browse to your project folder and select DataCoSupplyChainDataset.csv.
Click Open.

A preview window appears showing a sample of your data columns and rows.

Verify you can see columns like Delivery Status, Shipping Mode, and Sales in the preview.
Click Load to bring all the data into Power BI.

Wait for the data to finish loading. With 180,000+ rows this takes a moment. When it completes, the Fields pane on the right side shows DataCoSupplyChainDataset with expandable field names underneath it.

What is the Fields pane?

The Fields pane lists every table and column in your data model. You will drag fields from this pane onto the canvas to create visuals throughout this project.

Don't see the table in the Fields pane?

Make sure you clicked Load (not Transform Data) in the preview window. If you accidentally clicked Transform Data, click Close & Apply in the Power Query ribbon to return to the report canvas with your data loaded.

If the Fields pane is not visible at all, go to View in the ribbon and check the Fields checkbox to show it.

Still stuck? Help me troubleshoot why my CSV didn't load into Power BI

Build a delivery status bar chart

The Delivery Status column categorizes every order as one of four outcomes: Advance shipping, Late delivery, Shipping on time, or Shipping canceled. A bar chart will instantly reveal which category dominates.

In the Fields pane on the right, expand the DataCoSupplyChainDataset table.
Find the Delivery Status field and drag it onto the empty report canvas in the center.

Power BI creates a table visual by default. You need to convert this to a bar chart.

With the table visual still selected, look at the Visualizations pane (the column of icons above the Fields pane).
Click the Stacked bar chart icon (horizontal bars stacked together).

Your canvas now shows a bar chart with four bars. Notice that Late delivery is the tallest bar by far, exceeding all other categories. This is the core insight: over half of all orders in this dataset arrive late.

Why is Late delivery so dominant?

The DataCo supply chain dataset contains real-world patterns. Roughly 57% of orders are flagged as late deliveries. This is the exact problem your dashboard will help a procurement team investigate.

By the end of this project, you will be able to drill into which shipping modes and which regions drive that number so high.

Chart not showing bars?

Make sure you clicked on the table visual before changing the chart type. If you clicked an empty area of the canvas first, Power BI creates a new blank visual instead of converting the existing one.

If the bars appear but show no data, confirm that Delivery Status appears in the Y-axis well of the Visualizations pane. Drag it there if it is missing.

Still stuck? Help me fix my bar chart in Power BI

Add a KPI card for total orders

A KPI card displays a single important number at a glance. Adding one for total orders confirms your full dataset is loaded and gives context to the bar chart.

Click an empty area on the canvas, away from your bar chart.
In the Visualizations pane, click the Card icon (it looks like a single number display).
From the Fields pane, drag Order Item Id into the Card's Fields well in the Visualizations pane.

The card displays a count of all order items. This number confirms your entire dataset loaded successfully.

Press Ctrl+S to save your dashboard file.

Card showing the wrong number?

If the card shows a sum instead of a count, click the dropdown arrow next to Order Item Id in the Fields well and change the aggregation from Sum to Count.

Need help? Help me fix my Card visual in Power BI

Before you look at the final result, take a moment to predict: based on what you know so far, what percentage of orders do you think are late deliveries?

Look at your canvas now. Your bar chart shows delivery status counts with Late delivery as the tallest bar, far exceeding Advance shipping, Shipping on time, and Shipping canceled. Your Card visual confirms the full dataset is loaded. You have already uncovered the headline story in this data.

You have your data loaded and the delivery problem is staring right at you from the canvas. Next up, you will clean and transform the data to remove clutter and set yourself up for a polished, professional dashboard.

Clean and Transform the Data

Your bar chart proved the headline finding. Most orders in this dataset are delivered late.

Right now, the Fields pane shows all 53 columns from the raw CSV, including irrelevant fields like Customer Password and Product Image. When you try to build visuals, you waste time scrolling through clutter to find useful fields. Power Query lets you trim the data down to exactly what matters for supplier performance analysis.

In this step, get ready to:

Open Power Query Editor and review your 53 columns.
Remove 39 unnecessary columns, keeping only the 14 that matter for supplier analysis.
Fix the date column and apply all transformations back to your report.

Open Power Query Editor

Power Query Editor is the dedicated data transformation environment inside Power BI Desktop. It records every change you make as a repeatable step, so your transformations run automatically if the source data ever updates.

In Power BI Desktop, go to Home > Transform data in the ribbon.

The Power Query Editor opens in a new window. You can see all 53 columns of your dataset displayed in a preview grid with column headers across the top.

What is Power Query?

Power Query is the data transformation engine built into Power BI. Instead of editing your original CSV file, you define transformation steps here.

These steps are saved and replayed every time you refresh your data, creating a reproducible pipeline.

Remove unnecessary columns

Of the 53 columns, only 14 are relevant for analysing supplier delivery performance. The Choose Columns dialog lets you pick exactly which columns to keep and discard the rest.

In the Power Query Editor ribbon, go to Home > Choose Columns (in the Manage Columns group).
In the dialog that appears, uncheck (Select All Columns) at the top of the list.
Now check only these 14 columns:

Delivery Status
Days for shipping (real)
Days for shipment (scheduled)
Late_delivery_risk
Shipping Mode
Category Name
Sales
Benefit per order
Order Profit Per Order
Order Region
Market
Customer Segment
Order Item Quantity
order date (DateOrders)

Click OK.

The preview grid now shows only your 14 selected columns. All irrelevant columns have disappeared from the view.

Why these 14 columns?

These columns cover the four dimensions of supplier delivery performance: timing (real vs. scheduled shipping days), outcomes (delivery status and late delivery risk), segmentation (shipping mode, market, region, category, customer segment), and financials (sales, profit, benefit per order).

Removing the other 39 columns eliminates noise like customer passwords, email addresses, product images, and redundant ID fields that add no analytical value.

Can't find a column in the list?

The Choose Columns dialog lists columns alphabetically. Scroll carefully through the list. Column names are case-sensitive, so look for the exact capitalisation shown above.

Help me find a column in the Choose Columns dialog

Fix the date column and apply changes

The date column has a clunky name and needs its data type confirmed so Power BI can use it for time-based filtering later.

Click the order date (DateOrders) column header in the preview grid to select it.
In the ribbon, click the Transform tab.
Click Data Type and select Date/Time from the dropdown.
If Power BI asks whether to replace or add a new step, select Replace current.
Double-click the order date (DateOrders) column header to make it editable.
Type Order Date as the new column name and press Enter.

The column header now reads Order Date and shows a calendar icon indicating the Date/Time type.

Why rename the column?

When you build visuals later, field names appear in axis labels and legends. A clean name like Order Date is easier to read than the original order date (DateOrders) from the raw dataset.

Before you apply these changes, what do you think will happen to the bar chart you built in the previous step?

Click the Home tab in the Power Query Editor ribbon.
Click Close and Apply to save your transformations and return to the report canvas.

Power BI reloads the data with your transformations applied. Check the Fields pane on the right. You should see only your 14 selected columns listed under DataCoSupplyChainDataset.

Your bar chart from the previous step still displays correctly with delivery status counts. The Delivery Status field the chart uses is one of the 14 columns you kept, so nothing broke.

Chart showing an error after Close and Apply?

If your bar chart displays a field error, you may have accidentally unchecked Delivery Status in the Choose Columns step. Go back to Home > Transform data to reopen Power Query Editor, click the Choose Columns step in the Applied Steps pane on the right, and verify all 14 columns are checked.

Help me fix my broken chart after Power Query transformations

Your data is clean and focused. Next up, you will use these 14 columns to build a full delivery performance dashboard with KPI cards, donut charts, and breakdowns by shipping mode and market.

Build the Delivery Performance Dashboard

Your cleaned dataset is ready, and the quick bar chart already revealed the main story: most orders arrive late. But a single chart does not tell a hiring manager where the problems are or how severe they look across different shipping modes and markets.

In this step, you will build a full dashboard page with KPI cards, a donut chart, and bar chart breakdowns. By the end, anyone opening your file can see the delivery crisis from multiple angles on a single page.

In this step, get ready to:

Set up the Delivery Performance page and create KPI cards showing total sales, order quantity, and profit.
Add a donut chart for delivery status and a clustered bar chart for shipping mode breakdown.
Add a market breakdown visual showing delivery performance across global regions.

Set up the page and create KPI cards

Before building fresh visuals, you need a clean canvas. The bar chart and Card from Step 2 served their purpose as a quick proof of concept. Now you will replace them with a polished layout.

Click the bar chart on the canvas to select it.
Press Delete on your keyboard to remove it.
Click the Card visual, then press Delete to remove it as well.
Double-click the Page 1 tab at the bottom of the canvas.
Rename it to Delivery Performance and press Enter.

Your canvas is now empty with a properly named page tab. Next, add three KPI cards across the top to give viewers instant headline numbers.

Click an empty area on the canvas.
In the Visualizations pane on the right, click the Card icon (displays a single number).
In the Fields pane, expand DataCoSupplyChainDataset and drag Sales into the card's Fields well.
The card now shows the total sales value across all 180,000+ orders.

Click another empty area on the canvas.
Add a second Card visual from the Visualizations pane.
Drag Order Item Quantity into the Fields well.
This card displays the total number of order items in the dataset.

Click another empty area on the canvas.
Add a third Card visual.
Drag Benefit per order into the Fields well.
This card shows the average profit per order.
Drag all three cards so they sit in a horizontal row across the top of the page.

You should see three number cards lined up at the top: one for total sales, one for total items, and one for profit per order.

What is a KPI card?

A KPI (Key Performance Indicator) card shows a single headline number. Decision-makers look at these first to get the big picture before drilling into charts.

In Power BI, a Card visual automatically aggregates the field you drop into it. Numeric fields default to Sum, so your Sales card shows total revenue and your Order Item Quantity card shows total items.

Card showing a count instead of a sum?

If a card displays a number that looks wrong, the field might be aggregated as Count instead of Sum. Click the dropdown arrow on the field name inside the Fields well and change the aggregation to Sum.

Still not seeing the right value? Help me fix my Card visual aggregation in Power BI

Add a delivery status donut chart and shipping mode breakdown

The KPI cards give headline numbers, but they do not reveal the proportion of late versus on-time orders. A donut chart makes that split instantly visible.

Click an empty area on the canvas below your cards.
In the Visualizations pane, click the Donut chart icon.
Drag Delivery Status to the Legend well.
Drag Order Item Quantity to the Values well.

The donut chart fills in with colored slices. Notice how the Late delivery slice dominates the chart, visually confirming the problem you discovered in Step 2.

Next, add a bar chart that breaks down late delivery risk by shipping mode to show which methods are failing most.

Click another empty area on the canvas.
In the Visualizations pane, click the Clustered bar chart icon.
Drag Shipping Mode to the Y-axis well.
Drag Late_delivery_risk to the X-axis well.

You should now see four bars representing Standard Class, First Class, Second Class, and Same Day. The bar lengths show which shipping modes accumulate the most late delivery risk.

Why use Late_delivery_risk instead of Delivery Status here?

The Late_delivery_risk field is numeric (1 = late, 0 = not late). When Power BI sums it, you get the total count of late-risk orders per shipping mode as a single bar length.

Using Delivery Status would require a stacked chart with multiple categories. The numeric field keeps this visual clean and focused on late deliveries specifically.

Only seeing one bar or no data?

Make sure Shipping Mode is in the Y-axis well (not Values) and Late_delivery_risk is in the X-axis well (not Legend). Swapped axes produce a confusing layout with one tall bar instead of four separate ones.

Still stuck? Help me fix my clustered bar chart field placement in Power BI

Add a market breakdown visual

You now know which shipping modes have the most late deliveries. The last piece of the story is geography: which global markets are hit hardest?

Click an empty area on the canvas.
In the Visualizations pane, click the Stacked bar chart icon.
Drag Market to the Y-axis well.
Drag Order Item Quantity to the X-axis well.
Drag Delivery Status to the Legend well.

The chart shows bars for Africa, Europe, LATAM, Pacific Asia, and USCA. Each bar is color-coded by delivery status, making it easy to see which markets have the largest Late delivery segments.

Arrange all six visuals so they fill the page without overlapping: cards in a row at the top, and the three charts below them.
Press Ctrl+S to save your dashboard.

Before you look at the final result, predict: which market do you think has the highest proportion of late deliveries based on what you have seen so far?

Look at your completed Delivery Performance page. You should see six visuals: three KPI cards across the top, a donut chart showing the delivery status split, a clustered bar chart comparing shipping modes, and a stacked bar chart revealing market-level performance.

What should I see?

The donut chart's Late delivery slice is the largest segment, confirming over half of orders arrive late. The stacked bar chart shows this pattern holds across all five markets, with color-coded segments making it immediately clear.

The KPI cards give context: total revenue, total items shipped, and average profit per order. Together, these six visuals tell a complete delivery performance story on a single page.

Visuals overlapping or cut off?

Click a visual to select it, then drag its corner handles to resize. Make cards narrower and charts wider. If a chart title is cut off, drag the top edge down to give it room.

Need layout help? Help me arrange multiple visuals on a Power BI page without overlapping

Your Delivery Performance page now tells the full story of where deliveries are failing. Next up, you will add slicers for interactivity and build a second page that compares actual versus scheduled shipping days.

Add Interactivity and a Shipping Analysis Page

Your Delivery Performance page tells the story of late deliveries, but right now it is a static picture. A hiring manager looking at this dashboard cannot ask "what about First Class shipping specifically?" without you manually rebuilding the chart.

In this step, you will add slicers that transform your dashboard into an interactive tool. You will also build a second page dedicated to shipping time analysis, proving you can deliver multi-page analytics reports.

In this step, get ready to:

Add slicers so anyone can filter the dashboard by shipping mode and market.
Create a Shipping Analysis page comparing actual vs. scheduled shipping days.
Apply a professional theme and add page titles to polish the report.

Add slicers to the Delivery Performance page

A slicer is an on-canvas filter that lets anyone click a value and instantly update every visual on the page. You will add two slicers: one for Shipping Mode and one for Market.

Click an empty area on the canvas so no existing visual is selected.
In the Visualizations pane, click the Slicer icon (it looks like a funnel with a list).
From the Fields pane on the right, drag Shipping Mode into the slicer's Field well.
The slicer now shows four options: Standard Class, First Class, Second Class, and Same Day.

Now add a second slicer for the Market field.

Click an empty area on the canvas again.
Select the Slicer icon in the Visualizations pane.
Drag Market from the Fields pane into the new slicer's Field well.
Position both slicers along the top or left edge of your dashboard so they do not overlap your charts.

What is a slicer?

A slicer is a visual filter that sits directly on the report canvas. When you click a value in a slicer, Power BI automatically filters every other visual on the same page to show only that slice of data.

This means your dashboard becomes interactive without writing any code. Anyone viewing it can explore the data themselves.

Before you test, predict: if you click "First Class" in the Shipping Mode slicer, what do you expect to happen to your donut chart and bar charts?

Click First Class in the Shipping Mode slicer.

Every chart on the page updates to show only First Class orders. Your KPI cards, donut chart, and bar charts all reflect the filtered data.

Click First Class again to deselect it and restore the full view.

Slicer not filtering other visuals?

Make sure you clicked an empty area of the canvas before creating the slicer. If you accidentally created it inside another visual, delete it and try again.

Also confirm you dragged the field into the slicer's Field well, not into one of the other chart wells.

Help me troubleshoot why my slicer is not filtering other visuals on the page.

Create a Shipping Analysis page

Your first page answers "how many deliveries are late?" but does not explain why. A second page comparing actual vs. scheduled shipping days will reveal which modes consistently overshoot their promised timeline.

Click the + icon at the bottom of the canvas (next to the "Delivery Performance" tab) to add a new page.
Double-click the new page tab and rename it to Shipping Analysis.
In the Visualizations pane, click the Clustered column chart icon.
Drag Shipping Mode to the X-axis well.
Drag Days for shipping (real) to the Y-axis well.
Drag Days for shipment (scheduled) to the Y-axis well as well (it appears as a second bar next to the first).

You should see a column chart with paired bars for each shipping mode. The "real" bars should be taller than the "scheduled" bars for modes that consistently ship late.

Click an empty area on the canvas, then add a Slicer visual.
Drag Market into the slicer's Field well so users can filter this page by region.

What does this chart reveal?

By placing actual shipping days next to scheduled shipping days for each mode, you can immediately see which modes promise fast delivery but fail to deliver on time.

If First Class shows actual days far exceeding scheduled days, that is evidence the shipping provider is underperforming on premium services.

Both bars showing the same height?

Make sure you dragged both Days for shipping (real) and Days for shipment (scheduled) into the Y-axis well, not the X-axis or Legend well.

If you still see unexpected results, click the chart and check that the Y-axis shows "Average of Days for shipping (real)" and "Average of Days for shipment (scheduled)" in the field wells.

Help me fix my clustered column chart that is not showing the correct comparison.

Add titles, apply a theme, and save

Professional dashboards always have clear titles so viewers immediately know what they are looking at. A consistent theme ties the visuals together.

Click the Delivery Performance tab at the bottom to switch to page 1.
Go to Insert in the ribbon, then click Text box.
Type Supplier Delivery Performance Dashboard into the text box.
Drag the text box to the top of the page and increase the font size so it reads as a clear title.

Click the Shipping Analysis tab to switch to page 2.
Go to Insert > Text box again.
Type Shipping Time Analysis and position it at the top of the page.

Now apply a visual theme to give the report a polished, consistent look.

Go to View in the ribbon, then click Themes.
Select a professional-looking theme (for example, "Executive" or "Innovation").
Press Ctrl+S to save your completed dashboard.

Your entire report now has a unified colour scheme, clear titles, and polished formatting.

Why apply a theme?

Themes apply a coordinated set of colours, fonts, and formatting across all visuals at once. This saves time compared to styling each chart individually and ensures visual consistency across pages.

Theme not applying to all visuals?

Some manually set colours on individual visuals override theme colours. If a chart does not update, click it, go to Format visual, and reset any manually set colour back to the default.

Help me understand why my Power BI theme is not applying to all visuals.

Take one final look at your finished dashboard. Switch between the two pages and click different slicer values to confirm everything responds correctly.

On the Delivery Performance page, click First Class in the Shipping Mode slicer.
Confirm all KPI cards, the donut chart, and bar charts update to reflect only First Class data.
Switch to the Shipping Analysis page and confirm you see the clustered column chart comparing actual vs. scheduled days.

Your dashboard is now fully interactive and presentation-ready. Next up, the Secret Mission challenges you to go deeper with a custom DAX measure and conditional formatting.

Secret mission

Add a DAX Measure and Conditional Formatting

DAX (Data Analysis Expressions) is what separates basic Power BI users from analysts who get hired. In this mission, you'll write a custom measure that calculates on-time delivery percentage, then apply conditional formatting to make problem regions jump off the screen in red.

Clean Up Your Resources

Decide whether to keep your resources running, pause them to come back later, or delete them entirely. This project runs entirely locally using free tools, so there are no ongoing costs.

Resources you used:

Supplier_Performance_Dashboard.pbix file (your Power BI dashboard).
DataCoSupplyChainDataset.csv file (the raw supply chain data).
Power BI Desktop (installed application).

Good news about the CSV

Once you imported the CSV into Power BI, the data was embedded in the .pbix file. You can safely delete the CSV without losing anything in your dashboard.

Keep everything running

No action needed. Choose this if you want to keep building on your dashboard or use it as a portfolio piece.

Your Supplier_Performance_Dashboard.pbix file stays in your project folder, ready to open anytime.
You can export screenshots from Power BI (File > Export > Export to PDF or use the Snipping Tool) to add to LinkedIn or your resume.
Power BI Desktop remains installed for future projects at no cost.

Pause - I'll come back to this later

Close Power BI Desktop to free up memory. Your files stay on disk for when you return.

Save your dashboard with Ctrl+S if you have unsaved changes.
Close Power BI Desktop.
Delete DataCoSupplyChainDataset.csv to save disk space. The data is already embedded in your .pbix file.

Delete - I don't want to use this again

Remove all project files and optionally uninstall Power BI Desktop.

Delete your project folder (e.g., C:\SupplyChainProject\) which contains both Supplier_Performance_Dashboard.pbix and DataCoSupplyChainDataset.csv. Right-click the folder in File Explorer and select Delete.
To uninstall Power BI Desktop: open Settings on your PC, select Apps, then Installed apps. Search for Power BI Desktop, click the three-dot menu, and select Uninstall.

Nice Work!

You just built a multi-page interactive supplier performance dashboard in Power BI Desktop from scratch. Starting with 180,000+ rows of raw supply chain data, you turned messy procurement records into a story about delivery failures that anyone can explore with a click.

You've learned how to:

Import and transform real supply chain data using Power Query, trimming 53 columns down to 14 relevant fields for supplier performance analysis.
Build KPI cards, donut charts, bar charts, and column charts that visualize delivery performance across global markets and shipping modes.
Add interactive slicers that let users filter the entire dashboard by shipping mode and market region, turning static charts into an explorable analytics tool.
Secret Mission: Created a custom DAX measure for on-time delivery percentage and applied conditional formatting to highlight problem regions in red.

Ready to quiz yourself?