Project Description
SELF-LEARNING CUSTOMER ORDER DATA ENTRY
TRANSPORT & LOGISTICS SECTOR
BRIEF
Manual processes cost companies time and money. Employees often spend half their time doing repetitive tasks and data entry. For one of the most prestigious transport & logistics companies in Europe, our Belgian team created a proof of concept on how to automate their manual order entry process. The company receives 300+ customer-specific orders daily in the form of native PDFs that are manually entered into their system. Our goal was to automate this manual process which can lead to a significant decrease in operational cost.
Within a short period of time, our team of software engineers and data scientists created a proof of concept in the form of a live demo. By extending it to 4-5 months, we can further develop the proof of concept and turn it into a real-life solution with a 60% automation level. This solution will not only improve work efficiency but also significantly reduce costs. In order to reach the maximum automation level, this project requires our support in the areas of:
- Project Management
- Frontend development for processing Order PDFs
- Backoffice development for updating the algorithms/ model
- Data structuring and preparations
- Algorithm development and training
- Testing and improving
THE CONCEPT
300+ customer-specific orders in the form of native PDFs are manually entered into the system daily.
The idea is to create an application that loads PDF orders and detects as much information as possible using an intelligent self-learning algorithm. It means that it can fill in the required fields with data from the PDF, business logic, and historical data. Once the user is satisfied, the application either generates an export file or calls an API which will be used for further processing in the actual order entry system.
CUSTOMER CHALLENGE
As we further develop the application, the real challenge is to make sure that the application can recognize texts inside of images or unstructured PDF documents. Lastly, the application needs to be able to read and understand e-mails and one-time orders of infrequent customers. All of this requires intensive training, testing, and improving the algorithms/ model so the application can process information accurately.
MEDIAAN IN ACTION
During the development of the proof of concept until the development of the actual solution, the following techniques are used:
- Angular
- Python
- Microsoft Azure
- Docker
- Scrum/ Agile
SOLUTION IN DETAILS
The proof of concept is able to
- Collect data and label examples to reach the maximum automation level
- Convert PDFs to text
- Apply business logic and use historical data to fill in required data-fields
- Provide a graphical user interface to display PDF’s while processing it
- Aid in annotating data that is automatically recognized or suggest likely options based on data formats
- Generate exports files or call API’s as output to other systems
Send training data back to self-learning algorithm
To reach the maximum automation, the application needs to be enhanced so it can
- Recognize texts inside images/unstructured PDF documents
- Read and understand e-mails
- Continuously trained to ultimately result in a self-learning algorithm for detecting data in the PDFs
- Make a technical/automated connection to Order Entry/ERP applications
- Accurately process one-time orders of infrequent customers
RESULTS
Within a short period of time, our team of software engineers and data scientists created a proof of concept that shows the benefits of automating the manual order entry process. By extending it to 4-5 months, our team can further develop this proof of concept into an actual solution that will lead to a 60% automation level. This solution will not only improve work efficiency but also significantly reduce costs.
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