Logistics Automation - Invoice Processing

Problem to Solve:
In a B2B scenario the logistics finance team would spends a significant amount of time manually capturing data from invoices, which is both time-consuming and prone to errors. By having access to more accurate and up-to-date data, the logistics finance team will have a better understanding of the company's financial situation, allowing them to make informed decisions about future budget planning

This is the reverse of the invoice generation problem

Conceptual Architecture View:
We would leverage the AI builder and all the data capture and

Platform Setup:

1. SharePoint List Setup Define the list of filed that you are interested to capture leveraging AI Builder.

1. Mapping the AI builder output to the share point list The content of the file is saved as an attachment and the below data attributes are mapped

The automation would work as below

1. The trigger condition to initiate the cloud flow is the email with PDF attachment to a specific mailbox.
2. The AI builder would be leveraged to pass the PDF data and the AI form recogoniser pre-built model would provide a JSON message with the necessary data. Please read through the limitation with respect to file format, file size, support language and the API call limit

1. The AI builder would return a JSON with the data

Once the data is made available the planning team can leverage the data and build cool dashboard. Below is a sample one giving a view of

a) Time line view of the invoice amount to be managed every month based on the due date / payment condition
b) % of invoice approved and unapproved
c) # of invoice to be processed by team

Conclusion:
AI data capture can help the logistics finance team with budget forecasting by providing accurate and up-to-date data, which is the foundation for effective budget planning.

1. Improved data quality: By automating the data capture process, the quality of data captured from invoices will be improved, reducing the risk of errors and increasing accuracy. This leads to more reliable data that the finance team can use for budget forecasting.
2. Faster data access: AI-powered form recognizers can process invoices much faster than manual data entry, providing the finance team with more up-to-date information. This allows them to quickly identify trends and make informed decisions that can impact the budget.
3. Increased visibility: By having access to more accurate and up-to-date data, the logistics finance team will have a better understanding of the company's financial situation, allowing them to make informed decisions about future budget planning.
4. Improved forecasting accuracy: With accurate data at their fingertips, the finance team will be able to make more accurate forecasts, reducing the risk of unexpected expenses and allowing them to allocate resources more effectively.

Overall, the use of AI data capture in budget planning will improve the quality, speed, and accuracy of the data available to the logistics finance team, leading to more informed budget forecasting and improved financial planning.

Product Documentation:

1. https://learn.microsoft.com/en-us/ai-builder/flow-invoice-processing