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In recent months, we have experienced enormous growth in the number of product and price changes that we process on a daily basis. To give you an idea, we currently process > 7.5 MIO products > 70 MIO prices per day between 23:00 and 08:00 and this number is still growing. We have therefore decided to re-develop the Product Exchange service towards the Product Data Store (import) so that we become more scalable to handle the future workload. There is also a user story in the product backlog to adjust the brands and product group matching so that they can be used in the suspecting algorithms you devised. Our development resources are limited and we expect to be working on this for the rest of the year and would like to work with you again in 2022.
Introduction
The current way of indexing the products has its limitations., sometimes wrong products are linked to a Product Data Index product, resulting in an inability to find the right products.
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Another idea is to use the database with 2.5 MIO products with the manual knowledge of classification inside, to extract that knowledge with machine learning so not the user has to decide on a classification mapping, but the machine does.
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The idea
The customer panel started to work on the product data indexing quality. The idea, in general, is to identify suspects by comparing GTIN's, brands and manufacturer part numbers from the linked supplier items with each other.
The suspects will be displayed in the portal to the suppliers for their own products, the portal will also show the products that failed processing and not available in the Product Data Store. They can either correct their internal product data, so with the next processing, it will be correct, or mark the product to be correct. In that case, another supplier has probably to correct the product data.
Also, for the buyers, a portal will be available to display the suspects. And on top of that, the suspect flag will be available in the product data output and in the real-time Product Now.
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