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Automated SAP Data Migration​

KLM Engineering & Maintenance used ҹɫֱ����(formerly Qualibrate) for a mass data upload.

3000

Tasks automated​

10

Seconds​ Per task​

6

Hours​ Total duration​

8x

Faster than manual

The Challenge

Well beyond the regulatory standards stipulated at the international, European, and national level, AirFrance Industries and KLM Engineering & Maintenance places flight safety at the heart of its corporate culture.

The MRO (Maintenance Repair & Overhaul) unit offers comprehensive technical support, including line maintenance, engine overhaul, and aero structure maintenance for more than 3000 aircrafts around the world.

They use SAP systems and internal ERP to carry out daily tasks like ordering new components and scheduling maintenance. When AirFrance Industries and KLM Engineering & Maintenance merged, they were relying on 2 different SAP instances.

The objective was to unify the systems to achieve documentation centralization. This process, however, becomes very challenging when thousands of records need to be migrated from one system to the other.

For example, the process of manually migrating Engineering Orders (EO) takes 60 seconds for one task per person. For 3000 tasks, this would have taken 3000 minutes (50 hours) to finalize.

The Solution

AirFrance Industries and KLM Engineering & Maintenance utilized ҹɫֱ����(formerly Qualibrate) for regression testing

To overcome the challenge of data migration, KLM Engineering & Maintenance leveraged its record and playback capabilities, and automated the mass upload of the tasks from one SAP system to the other.

They selected the Engineering orders module as a pilot project in migrating a higher volume of data with the intention of later extending the practice to more modules.

The Outcome

To avoid manual work, KLM Engineering & Maintenance used ҹɫֱ�� to upload a set of 500 tasks per EO type for validation (3000 tasks). ​With ҹɫֱ��, each task took 10 seconds instead of 60, saving almost 42 hours!

After the pilot project was successful, they decided to keep using ҹɫֱ�� to migrate even higher volumes of data.

Plus, ҹɫֱ�� will soon accelerate the organization’s release process by automating regression testing and supporting the creation of business process documentation.

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