How to Create a Supply Chain Dashboard in Power BI

Building a supply chain dashboard in Power BI gives you a central command center to track everything from inventory levels to delivery times, turning confusing spreadsheets into clear, actionable insights. In this tutorial, we’ll walk through the process of defining your metrics, connecting your data, and building the essential visualizations you need to create a powerful supply chain dashboard from scratch.

Why a Supply Chain Dashboard is a Game-Changer

Modern supply chains are complex webs of suppliers, warehouses, carriers, and customers. Trying to manage this system using disconnected spreadsheets and reports is like navigating a maze blindfolded. You know there are bottlenecks and inefficiencies, but you can’t pinpoint exactly where they are or what’s causing them.

A well-designed dashboard solves this by bringing all your critical data into one place. It helps you:

Spot Problems Instantly: See which suppliers are consistently late or where inventory is piling up with a quick glance.
Make Data-Backed Decisions: Instead of guessing, you can use real data to optimize shipping routes, negotiate with carriers, and manage stock levels.
Improve Collaboration: When everyone from procurement to logistics is looking at the same real-time data, communication becomes more efficient and everyone is aligned on the same goals.

Step 1: Plan Your Dashboard Before You Build

Jumping straight into Power BI without a plan is a recipe for a cluttered, confusing dashboard. Before you even open the application, take a few minutes to outline what you’re trying to achieve.

Ask the Right Questions

First, define the core purpose of your dashboard. Who is it for, and what questions should it answer?

For a Warehouse Manager: "What is our current inventory turnover rate?" "Which products are moving the slowest?" "How does our order-picking accuracy look this month?"
For a Logistics Coordinator: "What is our on-time delivery rate by carrier?" "Which shipping lanes are the most expensive?" "What’s the average transit time?"
For an Executive: "What is our total landed cost per unit?" "How are our overall supply chain costs trending over the last year?"

Thinking about the end-user helps you focus on what truly matters and avoid getting lost in irrelevant data points.

Identify Key Supply Chain Metrics (KPIs)

Based on the questions you need to answer, list the key performance indicators (KPIs) you need to track. Here are some of the most common supply chain metrics:

Cash-to-Cash Cycle Time: The time it takes for money invested in inventory to come back as revenue from sales.
Perfect Order Rate: The percentage of orders delivered on-time, complete, damage-free, and with the correct documentation. A holistic measure of customer satisfaction.
Inventory Turnover: How many times inventory is sold during a period. A high number indicates efficient inventory management.
On-Time Delivery (OTD): The percentage of orders that arrive by the promised delivery date.
Order Cycle Time: The average time from when a customer places an order to when they receive it.
Cost Per Shipment: The total cost associated with delivering an order, including transportation, labor, and fuel.
Warehouse Carrying Cost: The cost to hold unsold inventory, including storage, insurance, and labor.

Gather Your Data Sources

Where does all this data live? Your information is likely scattered across several systems. Make a list of your sources, which might include:

ERP Systems: Platforms like SAP, Oracle, or Microsoft Dynamics hold financial data, sales orders, and purchase orders.
Warehouse Management Systems (WMS): Contain data on inventory levels, order picking, and stock locations.
Transportation Management Systems (TMS): Track carrier performance, shipping times, and freight costs.
Excel or Google Sheets: Often used for manual tracking, supplier lists, or temporary data exports.
SQL Databases: Custom databases that might house more granular transaction data.

Step 2: Connect and Prepare Your Data in Power BI

With a solid plan in place, it’s time to launch Power BI Desktop and start pulling your data together.

Connect to Your Data Sources

Power BI makes it simple to connect to a wide range of data sources. From the Home ribbon, click on Get Data.

You’ll see a list of common connectors like Excel Workbook, SQL Server, and CSV. If your source isn't listed, click More… to find a comprehensive list of connectors for everything from specific SaaS platforms to generic web APIs.

For this example, let's say you have order data in an Excel file and supplier information in a SQL database. You would connect to each source one by one. Power BI will pull them into its data model, where you can then create relationships between the tables (e.g., connecting an 'Orders' table to a 'Products' table using a Product ID).

Clean and Transform in Power Query

Once your data is loaded, Power BI will likely prompt you to Transform Data. This opens the Power Query Editor, a powerful tool for cleaning and reshaping your data before you build visualizations with it.

Common transformation steps include:

Removing unnecessary columns.
Renaming columns to be more readable (e.g., changing "cust_ID" to "Customer ID").
Fixing data types (e.g., ensuring a 'Date' column is recognized as a date format, not text).
Handling errors or blank cells.
Splitting a column into multiple columns (e.g., separating a city and state from a full address).

Spending time here ensures your data is accurate and structured correctly, which makes building visuals much easier.

Step 3: Build Your Dashboard Visuals

This is where your dashboard starts to come to life. The key is to choose the right visualization for the metric you want to display.

Choosing the Right Visualizations

Card: Perfect for displaying a single, important KPI like "Total Orders" or "Average Shipping Cost."
Gauge: Great for showing a KPI against a target, such as "On-Time Delivery Rate" vs. a 95% goal.
Line Chart: Ideal for tracking trends over time, like "Inventory Levels by Month" or "Weekly Shipping Costs."
Bar/Column Chart: Use these for comparisons, such as "Order Volume by Warehouse" or "Performance by Carrier."
Map: Visualize geographic data, like tracking shipments on a map or showing supplier locations.
Table or Matrix: For showing detailed, granular data that a user might want to drill down into.

Creating Some Essential Visuals - Step-by-Step Examples

Let's walk through building a few common visuals using the visuals pane on the right side of Power BI.

1. KPI Card for Inventory Turnover

If your data doesn’t have this metric pre-calculated, you can create it with a DAX formula. Right-click on your primary data table and select New Measure. Then, enter a formula like:

Inventory Turnover Rate = SUM(SalesData[CostOfGoodsSold]) / AVERAGE(InventoryData[InventoryValue])

Select the Card visual from the Visualizations pane.
Drag your new Inventory Turnover Rate measure into the "Fields" area for the card.
Power BI will display the number in a large, easy-to-read format.

2. Line Chart for Order Volume Over Time

Select the Line chart visual.
Drag your 'Order Date' field to the X-axis. Power BI will automatically create a date hierarchy (Year, Quarter, Month, Day) you can use.
Drag your 'Order ID' field to the Y-axis. By default, Power BI will try to sum the IDs. Click the dropdown on the 'Order ID' field and change the aggregation to Count (Distinct) to get the total number of unique orders.

You’ll now have a chart that shows how your order volume is trending—perfect for spotting seasonal patterns or growth anomalies.

3. Bar Chart for Performance by Shipping Carrier

Select the Stacked bar chart visual.
Drag your shipper or carrier name field (e.g., 'CarrierName') to the Y-axis.
Drag your calculated 'On-Time Delivery Rate' measure to the X-axis.

This instantly shows you which carriers are your top performers and which might be struggling, giving you clear data for your next performance review meeting with them.

Step 4: Make Your Dashboard Interactive

A static dashboard is just a picture. A great dashboard lets users explore the data themselves.

Add Slicers: Slicers are on-canvas filters. Drag the Slicer visual onto your dashboard and then add a field like 'Date', 'Product Category', or 'Warehouse Location' to it. Now users can click a date range or a specific category to filter the entire dashboard.
Enable Drill-Down: Certain visuals, like bar charts, allow you to create hierarchies. For instance, you could have a geography hierarchy of Region > State > City. With a click, users can "drill down" from a world view to a country view, then to specific states, and so on.
Use Tooltips: Customize the information that appears when a user hovers over a data point. You can add extra metrics to the tooltip to provide more context without cluttering the main visual.

Step 5: Publish, Share, and Automate

Once your dashboard is complete in Power BI Desktop, the final step is to get it into the hands of your team.

Publish to Power BI Service: From the Home ribbon, click Publish. This uploads your report to your organization's Power BI web service.
Set a Refresh Schedule: In the Power BI Service, find your dataset and configure a scheduled refresh. You can set it to update automatically on a daily or hourly basis, ensuring your stakeholders are always looking at the latest data.
Share Your Dashboard: Use the sharing options in the service to grant access to colleagues. You can embed it in SharePoint, link to it in Teams, or simply provide a secure link to view the report.

This automated flow means no more manually exporting data and emailing static reports every Monday morning. Your dashboard becomes a live, reliable source of truth for the entire team.

Final Thoughts

Building a supply chain dashboard in Power BI transforms your raw operational data into a powerful decision-making tool. By planning your goals, connecting and cleaning your data, and choosing clear visualizations, you can create a central command center that provides complete visibility into your logistics and inventory operations.

Managing all that business data can be complex. For our customers analyzing marketing and sales activities, we built Graphed to do the heavy lifting automatically. We eliminate the steep learning curves of traditional BI tools by letting you connect sources like Google Analytics, Shopify, and Salesforce and build dashboards just by asking questions in plain English. This way, your team can get straight to the insights without getting bogged down in the technical setup.