Case Study: The New Zwiesel Glas Data Centre

Thanks to the PIM system, data is now stored in a central location. This enables uniform maintenance of data and ensures that redundant data is excluded and that only the latest data is kept in the system. The central organization of the data management results in a considerable reduction of the time required reduces the error rate caused by outdated or redundant data to practically zero and allows the rapid provision of sales-promoting product information in the online shop. 

By introducing the PIM system based on Pimcore, Zwiesel Glas now has a solid digital database. And thanks to the flexibility and modular structure of Pimcore, the company is well equipped to continue to optimize the digital transformation of the shopping experience step by step.

1. Smooth import of ERP data and assets

The SAP import process for ERP data, which we implemented, transfers the product data stored in a simple Excel file into a multi-brand assortment hierarchy in the PIM daily. Assets can be added to the PIM via a hot folder or upload. Assets are automatically assigned to products and series via name matching. This means that the user only needs to import the images. Pimcore does the correct and logically structured filing based on the object assignment. Different types of images are also taken into account, e.g., clipping of filled or unfilled glasses, impression, or packaging images. If namings are misspelled, the corresponding assets remain in the Inbox folder, from where they can be linked to products and series either by correcting the naming or manually.

2. Intelligent, multi-level discount calculation

With our solution, multi-level dynamic price calculations based on discounts are also possible without any problems. Discounts are defined as objects so that they can be linked to brands, series, or individual products as desired. In conjunction with designated dates or terms, the system selects the most favorable discount for the individual product and displays the corresponding price in the online shop.

3. Connection to shopware while retaining the data hierarchy

The challenge was that shopware uses a relatively flat data model. Therefore, we created an interface that transforms the multi-brand assortment hierarchy with many relations that we created in the PIM into the flat Shopware model.

4. Automated channel-specific output hierarchies

Integrated into our solution is also an automated rejection into any output channel. Separate category structures can be created for each channel. The assignment of articles is done by maintainable rule definitions. If, for example, a new shot glass is added to the product range, this article is automatically included in the corresponding rejection categories.