Enhancing Efficiency :Automating Return orders in D365F&O via Power automate and AI Builder

In today’s highly competitive supply chain landscape, organizations are continually looking for ways to improve operational efficiency. One common point in the process is handling return orders. Traditionally, processing returns in Dynamics 365 Finance & Operations (D365F&O) can be labor-intensive, requiring manual data entry, time-consuming reviews, and often resulting in errors that slow down the process.

This is where Power Automate and AI Builder come into play. These Microsoft technologies can be integrated to automate the return order process, reducing the time spent on manual tasks and improving overall efficiency. In this article, we explore how these tools work together and the benefits they bring to D365F&O users.

1. Challenges in Manual Return Order Processing

Managing return orders often involves multiple steps:

• Manually extracting data from return forms or emails.
• Entering return order details into D365F&O, such as customer information, product IDs, quantities, and reasons for return.
• Verifying the accuracy of this information, which is prone to human error.

In high-volume environments, the complexity and frequency of return orders can overwhelm teams, leading to delays and impacting customer satisfaction.

2. How Power Automate and AI Builder Can Help

Power Automate is a cloud-based tool that allows users to automate repetitive tasks by creating workflows, while AI Builder enables users to add AI capabilities to those workflows. Together, they offer a powerful solution for streamlining the return order process.

Step 1: Automating Data Extraction with AI Builder

When a return order is received (whether via email or a portal), the first challenge is extracting the relevant information from the document. With AI Builder, you can train models to automatically recognize and extract key data from documents such as:

• Customer details (name, contact, etc.).
• Return order number.
• Product SKUs and quantities.
• Reason for return.

These AI models can read from structured (forms) or unstructured (emails, PDFs) documents and convert the information into a structured format that Power Automate can use.

Login to the PowerApps: https://make.powerapps.com/

Select the AI models and choose the "Extract custom information from documents" option.

Select the Invoices and click on the "Next" button

Add the mandatory fields in the AI model required for creating return orders in D365F&O.

Upload the Minimum five documents with the different formats or different data in the five documents into the AI Builder.

Map the fields on the Five documents with the AI builder fields and train the AI Model

AI model is ready for extracting the data.

How Power Automate and AI Builder Can Help

Power Automate is a cloud-based tool that allows users to automate repetitive tasks by creating flows, while AI Builder enables users to add AI capabilities to those flows. Together, they offer a powerful solution for streamlining the return order process.

Login to the https://make.powerautomate.com/ and create a new flow

Step 1: Extracting and Validating PDF Documents from Emails

When a return order is received via email, the first challenge is extracting and validating the relevant information from the attached PDF document. With AI Builder, you can train models to automatically recognize and extract key data from these PDF documents. The process can be summarized as follows:

Extract PDF Attachment: Power Automate automatically detects and extracts the PDF return form from the incoming email.

Validate the Document: If no PDF document is attached, the flow exits and sends a notification to the relevant team, alerting them about the missing document.

Extract Data Using AI Builder: Once the document is validated, AI Builder extracts key information such as:

• Customer details (name, contact, etc.).
• Return order number.
• Product SKUs and quantities.
• Reason for return.

Insert Data via DMF Entities

Power Automate then triggers the Data Import Process in D365F&O by calling the DMF APIs to insert the return order data into the system.

• Return Order Header: The return order header is imported into the D365F&O environment.
• Return Order Lines: Similarly, the return order lines is imported.

Once the data is imported, Power Automate sends a notification confirming the successful creation of the return orders in D365F&O.

To simulate this process, create a test PDF document containing return order data.

Send the test email to the inbox that’s linked with the Power Automate flow.

Flow is running and its sucessfull.

Once the flow processes the email and inserts the data into D365FO, check D365FO to validate the order headers and lines. Return order: 000889

Return order is created automatically in the D365F&O

Return order header details

Practical Use Case Example

Let’s take a real-world scenario where an eCommerce company integrates Power Automate and AI Builder with D365F&O to handle return orders.

• The company receives multiple return requests via email daily.
• AI Builder extracts data such as the return order number, customer details, product information, and the reason for return from these emails.
• Power Automate automatically generates return orders in D365F&O, applies the correct processing status, and updates the inventory.
• Notifications are sent to both the customer service and warehouse teams, ensuring that all parties are aware of the pending return and can act accordingly.

Conclusion

Automating the return order process in D365F&O using Power Automate, AI Builder, and DMF entities creates a fully automated workflow that significantly enhances efficiency, accuracy, and speed. By automatically capturing data from PDFs and inserting it into D365F&O, companies can handle larger volumes of return orders with ease, while minimizing errors and improving customer satisfaction.

This approach is not only scalable but also provides a robust solution to common return order processing challenges, allowing businesses to focus more on growth and less on manual tasks.