About the client
Merida is one of the oldest and most renowned bicycle manufacturers in the world and in Poland. With a history spanning over 50 years, the company has a wealth of achievements, new technologies, and fruitful collaborations with cycling champions.
The main challenge was to analyze the complex way of storing product information in spreadsheets and translate it, together with the Product Owner, into the product structure and functionalities in Akeneo PIM. All of this was done to streamline business processes.
within our team
of the project
Reaching for more with Satisfly
The choice of Akeneo was driven by the need to organize the product database, which had been processed in spreadsheets before. The client needed more control over the database, automation of processes, and simplification of the necessary work required for creating and editing the product database.
“The collaboration with Satisfly during the implementation phase was exemplary. Continuous direct contact with the responsible person and weekly meetings with developers provided insights into the progress of work.
During the meetings, we not only received reports but also analyzed functionalities and the sequence of implementations. Familiar with AGILE principles, I felt that the team adhered to the methodology as a whole. Implementations are also prepared to be compatible with other system elements and can be seamlessly deployed in case of an Akeneo update.
The entire implementation was carried out as it should be and within the agreed timeframe and budget. Some minor inconsistencies arose, but they are inherent in any implementation or even daily work. In summary: 100% quality, 100% cooperation, 100% communication, 100% transparency, 1% errors (corrected promptly).”
- The project started with a pre-project analysis, during which we gathered necessary project requirements. After this 2-month stage, we built the Akeneo architecture and imported the product database.
- We proceeded with the implementation work, which lasted 8 months according to the Client’s planned budget. The project involved a Project Manager/Analyst, Akeneo Frontend Developer, and 3 Backend Developers, who worked in a rotational mode to maintain the desired work pace.
- Products were imported into the PIM, categorized into appropriate categories, channels, attribute groups, attributes, families, and family variants.
- We developed and implemented the export file profile function. This involves generating an individual export file from PIM data, compliant with Merida’s business requirements. The file is generated from Akeneo and contains all product information within the system.
- We developed and implemented the changelog function (product edit validations). This function validates changes made by users related to product information, verified by an administrator. This way, the Client maintains control over the accuracy of the database.
- We added the feature of generating CSV file download links. This allows generating a URL for the export file, enabling sharing with interested parties outside the Akeneo system.
- We implemented the feature of generating image download links. URLs for images added to Akeneo are created and made publicly accessible.
- We created an attribute for product implementation phase statuses. This attribute allows marking the current implementation status of a product in the store.
Akeneo PIM enabled Merida to organize data input – it is now structured and organized. Working with a large dataset of product information has become easier, faster, and more orderly. Additional functionalities allowed synchronization with existing systems without the need to rebuild the entire network and internal processes.
Product data input has accelerated from approximately two to three months to… just one day. Simply put: “save, and it’s everywhere it needs to be.” Data migration and catalog creation have also sped up from one to three weeks to just a few days for basic files.
The number of errors in product data has dropped almost to 0. Meanwhile, the percentage of completed product data has increased from around 70% to 95% (the remaining 5% comprises products that still lack data from suppliers).
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