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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.

Status

Status
colourGreen
titleCurrent project

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Please find below a table for illustration with the processing errors and suspects, next to that is the user stories describing the suspect identification, supplier and buyer portal.

Sample processing errors and suspects

SCENARIOSELLERSKUGTIN/EANMFPRBRANDPRICEREQUIRED OUTPUT
Valid productAAAAA€ 100,00Valid product

BBAAA€ 101,00Valid product








Duplicate SKUAAAAA€ 100,00

AABBB€ 200,00Report duplicate SKU to the supplier, product not available








Duplicate MFPRAAAAA€ 100,00

ABBAA€ 200,00Report duplicate MFPR to the supplier, product not available








Duplicate GTINAAAAA€ 100,00

ABABB€ 200,00Report duplicate GTIN to the supplier, product not available








Duplicate GTIN/MFPRAAAAA€ 100,00

ABAAA€ 200,00Report duplicate GTIN/MFPR to the supplier, product not available








Brand differenceAAAAA€ 100,00Report suspect on brand, if correct, then report to other suppliers

BBAAB€ 101,00Required in output with suspect flag

CCAAB€ 102,00








GTIN differenceAAAAA€ 100,00Report suspect on GTIN to PDM, check and add to PDI or reject

BBBAA€ 101,00








MFPR differenceAAAAA€ 100,00Report suspect on MFPR, required in output with suspect flag

BBABB€ 101,00

User stories

Suspect identification

As a Product Exchange user
I want matched products to be compared and suspects are identified
So that the buyers don’t get different products in their search results

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Products are matched on GTIN or MFPR and brand. When the GTIN or MFPR is invalid, this can result in different products for one GTIN or MFPR. These can be identified as suspects by comparing prices and brands.

Supplier dashboard

As a Product Exchange seller
I would like to, in case of suspects and errors as described in user story 1 and user story 3, be notified by mail and in the portal
So that we can monitor our product data quality and integrity to improve the searchability of our products

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  • Only the own products of the seller should be visible
  • Email:
    • How many errors/suspects per flag-type
    • Date - time stamp of oldest error and flag
    • Number of new flagged or error products
    • Send once a day
    • With a link to the dashboard
  • Dashboard:
    • Filter on headers
    • Sort on headers
    • Show all items with error or suspect-type
    • Show the number of days/date
    • Headers of the columns: SKU/MFPR/GTIN/Brand/Upload date/reason of error or suspicion/Flagged by (system, user)
    • Export to Excel with the current selection (filtered output)
    • Unflag products because the product data is right, multi-select if the filter is used
    • Additional user role required
  • Product exchange processing:
    • Flag’s remain, unless the suspicion has been resolved in either the data or unflagged in the portal
    • The flag will not return if the product is unflagged and the data is unchanged
  • Unflagging:
    • Multiple brands can be the same
    • Products can have multiple GTIN’s
    • Other
    • If more sellers are unflagging a suspicious product, then Onetrail should be involved
    • Optionally specify textual reason when unflagging 
  • History for unflagged products (nice2have in the dashboard, mandatory for backend)
    • Keep track of the history of the unflagging
  • In case of duplicate error (single seller, different SKU’s), the seller can select the single product to process
  • Check master-file against cust specific file. if not in master and in customer file then show error

Buyer dashboard

As a Product Exchange buyer
I would like to, in case of suspects and errors as described in story 1 and 2, be notified by mail and in the portal
So that we can monitor the data quality of our suppliers and Onetrail

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