Enhancing Supply Chain Efficiency with Power BI: A DataCo Case Study – Part 1

Posted by

September 21, 2026

On August 16, 2024

Introduction

The proper management of the supply chain is not an easy task. Sometimes it can be intimidating to sift through the huge amount of supply chain data you have and know where to start in trying to figure out which problems such as delayed delivery or inventory loss might be affecting supply chain efficiency.

Take comfort, you are not alone in this: These are frequent issues that can happen to any supply chain practitioner. Thinking positively, harnessing the right software, data can indeed be turned into insights that allow for improvements in the company’s operations and overall supply chain efficiency.

Power BI can help you transform your supply chain management. Power BI helps design dashboards, modeling and create reports that are social and enable the discovery of useful patterns within large sets of data.

Read through this article and you will understand the way and means of using Power BI in enhancing supply chain effectiveness- the case of DataCo Smart Supply Chain.

So, in this blog, I will demonstrate how DataCo applied Power BI to improve the efficiency of supply chain, including the stacked column chart and line chart to show the delivery status over time and planned vs actual delivery days.

This first part of the case study will take you through how to clean up the data, how to build these models and the type of visualizations that you obtain from them. Please read PART 2 here.

That said, let’s start and discover the opportunities of transforming supply chain data with Power BI.

Understanding the Data for Improving Supply Chain Efficiency

For the purposes of this case study, I will make use of the DataCo Supply Chain data set that clearly depicts various aspects of supply chain processes such as procurement, manufacturing, marketing and selling, distribution among others.

You can also download the dataset from the download section given at the end of the blog.

Overview of the DataCo Supply Chain Dataset

Here’s a breakdown of the key components:

DataCoSupplyChainDataset. Csv
The Excel file contains 53 numbers of columns and 180520 numbers of rows. The rows exhibit a specific client’s order while the column portrays traits with regards to the order, the number of sales by the customer, status, and types of products offered.
DescriptionDataCoSupplyChain. Csv
The purpose of this file is to bring the specifications pertain to the core data set that concerns the attributes.

Key Data Points and Their Relevance

Having specified the key variables of DataCoSupplyChainDataset, the following areas of focus will be defined to set up the supply chain management at DataCo:

Days for Shipping (Real) and Days for Shipment (Scheduled): The amount of the planned time for the delivery of the goods versus the time that is really taken.
Delivery Status: The orders must be tracked so that they can help with the delivery of the order on time and even help trace areas that the organization needs to improve on.

The next procedure is to move this data to Power BI and then transform it.

Implementing Power BI for Supply Chain Efficiency

Now that we have a solid understanding of the dataset, let’s dive into implementing Power BI to analyze and optimize supply chain efficiency. This section will guide you through the steps of importing data, preparing it for analysis, and setting up key calculations and relationships.

Step 1: Importing and Preparing Data for Enhanced Supply Chain Efficiency

Load the Data

Open Power BI Desktop.
Click on Get Data and select the CSV file
csv.
Click on Transform Data to open the dataset in Power Query Editor.

Step 2: Data Cleaning and Modeling

Transform Data

Rename the DataCoSupplyChainDataset table to Order.
For current analysis we only need Days for shipping (real), Days for shipment (scheduled), Delivery Status, order date (DateOrders), Order Id, shipping date (DateOrders). Select these columns and remove all other columns.
Promote the headers to use the first row as column names.
Change the data types for order date (DateOrders) and shipping date (DateOrders) as date.
Create a Date table using CALENDARAUTO(). Date hierarchy like Date (Year, Quarter, Month, Day) allow for drill-down analysis.

Create Relationships - Supply Chain Efficiency

Establish relationships between Order table and Date

Step 3: DAX Calculationsc

Calculated Column

Create Late Delivery Calculated Column.

Late Delivery = IF(‘Order'[Delivery Status]==”Late delivery”, 1, 0)

Measures

Create DAX Measure Average Shipping Time.

Average Shipping Time = AVERAGE(‘Order'[Days for shipping (real)])

Create DAX Measure AvgDaysScheduled.

AvgDaysScheduled = AVERAGE(‘Order'[Days for shipment (scheduled)])

Create DAX Measure Late Delivery Percentage.

Late Delivery Percentage =

DIVIDE(

SUM(‘Order'[Late Delivery]),

COUNT(‘Order'[Order Id])

)

From measure tools format it as percentage.

Step 4: Creating Visualizations in Power BI

With your data imported, cleaned, and relationships established in Power BI, you can now create visualizations that offer valuable insights into your supply chain operations.

This section will showcase several key visualizations and explain how to interpret them for better decision-making.

Understanding the Initial Challenges

DataCo faced several challenges in their supply chain management, including:

Inconsistent Delivery Times: Variability in actual shipping times compared to scheduled times.
High Late Delivery Rates: A significant percentage of orders were delivered late.

1. Comparison of Scheduled vs Actual Shipping Days

Line Charts are useful for showing trends over time, such as monthly sales and shipping times. Comparing actual versus scheduled shipping times highlighted consistent delays during certain periods.

Steps to Create a Line Chart

Add a Line Chart Visual

Click on the Line Chart icon in the Build pane.

Set Up the Axis

Drag the Date field from the Date table to the X-axis.
Remove Day from Date hierarchy.
Drag the Average Shipping Time field to the Y-axis.
Drag the AvgDaysScheduled field to the Y-axis as well.
Rename the fields for clarity. AvgDaysScheduled to Avg Days Shipping (Scheduled) and Average Shipping Time to Avg Days Shipping (Real).

Format the Line Chart

Customize the colors and line styles to differentiate between scheduled and actual shipping days.
Adjust the title, axis titles, and tooltips for better readability.

Additionally, we have also added a trend line in the downloadable file as shown in the visual. (The .pbix file is available for download below) To learn how to make this trend line and other advanced features, register for our courses.

Example Visual

Interpretation

This visualization helps identify periods where actual shipping times consistently exceed scheduled times, indicating potential inefficiencies or external factors affecting delivery.

2. Delivery Status Over Time

Stacked column charts are useful for showing the distribution of different categories over time, such as delivery statuses. Analyzing the distribution of delivery statuses over time highlighted months with higher incidences of late deliveries.

Steps to Create Line and Stacked Column Chart

Add a Stacked Bar Chart Visual

Click on the Line and Stacked Column Chart icon in the Build pane.

Set Up the Axis

Drag the Date field from the Date table to the X-axis.
Remove Day & Quarter from Date hierarchy.
Drag the Order Id field to the Column Y-axis and set it to count the number of orders.
Drag Late Delivery Parentage to Line Y-axis.
Rename the fields in Line Y-axis to Late Delivery %.

Set Up the Legend Values

Drag the Delivery Status field to the Column Legend area.

Format the Stacked Bar Chart

Customize the colors for each delivery status (e.g., on time, late, cancelled).
Adjust the title, axis titles, and tooltips for better readability.

Example Visual

Interpretation

By analyzing the distribution of delivery statuses over time, you can pinpoint trends and identify months with higher incidences of late deliveries or cancellations, allowing for targeted process improvements.

Complete Dashboard

In the downloadable Power BI file linked below, you will find a comprehensive dashboard that includes these visualizations and more. This dashboard is interactive, allowing users to filter by different time periods and delivery statuses for deeper insights. The dashboard provides a detailed analysis of supply chain performance, allowing you to gain insights into delivery times, statuses, and overall efficiency. Additionally, the line chart features a tooltip that shows a day-by-day analysis. You can learn how to build this tooltip and other advanced features by registering for our Power BI courses. See details in resources section below.

Example Dashboard

Sneak Peek into the Tooltip Feature

Here’s a glimpse of the tooltip feature in action. The tooltip provides a detailed, day-by-day analysis of shipping times, giving you granular insights into delivery performance.

Key Improvements and Results

Below are some of the values that were provided by DataCo from using the Power BI;

Reduced Delivery Times: Identified and addressed factors causing delays, resulting in a reduction in average shipping time.
Decreased Late Deliveries: Implemented process improvements that lowered the late delivery rate.

Take the first step towards a smarter supply chain by integrating Power BI into your data analysis toolkit. With the right approach, you can turn data challenges into opportunities for growth and optimization.

Downloads

Conclusion

As highlighted in the first part of the DataCo analysis, the focus of this stage was to explain data cleaning and modeling in Power BI with regards to data in the Supply Chain. In addition to using the visualization to analyze, the difference between the planned and actual delivery days and whether or not a delivery is on time within a particular period.

The present set of visualizations will assist you to recognize the potential sources of the weak position in the delivery cycle and enhance the supply chain of your company.

Additional sample visualizations and detailed analysis of the Power BI information for improvement of the sample supply chain will be included in the next part of the given case study.

Find Part 2 of the Case Study Here

Further Learning and Resources

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Contact Us: Make an enquiry about our Power BI courses and find the right training for you.

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