Searching for AI solutions to improve the quality of master data affecting consumer safety
[ 1 ] Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ SzD ] doktorant ze Szkoły Doktorskiej | [ S ] student
2022
rozdział w monografii naukowej / referat
angielski
- AI
- Artificial intelligence
- Supply chains
- Catalogues
- Master data synchronization
- Security
- GDM
- Global Data Model
EN The quality and completeness of the master data has a direct impact on the accuracy of purchasing processes in supply chains. Today, manufacturers and retail chains have both centralized catalogue solutions and distributed repositories supported by appropriate standards at their disposal. Despite the popularization of digitization of the synchronization processes of data describing products, research conducted around the world indicates basic errors that concern packaging at various levels, from the basic item, through cartons, to pallets. Therefore, incompleteness and unreliability of the data force the parties involved in the processes to remove errors, which leads to a deterioration of the sales economic parameters. However, the master data used in both B2B and B2C relations are not only the identifiers, classifiers, and dimension and weight information, but also a set of information on the composition and content of products, e.g. food products, which may affect the safety of consumers. Therefore, a detailed verification of the information content provided by suppliers and producers for individual participants in the supply chain is required. Such activities require the work of specialized teams of expert auditors who must deliver a verdict on timeliness, quality, and completeness. The elements of Artificial Intelligence (AI), which can take over most of the controlling activities, are the perfect solution for this role. This paper identifies important factors as the places where important decisions are made regarding the approval or rejection of product/master data. AI will be an important element of content verification in terms of consumer safety. The role of these mechanisms is particularly important in the context of the sale of food products and cosmetics, i.e. items that come into contact with the human body. Automation of these processes using this methodology and self-learning mechanisms will enable mass checking of entire databases in search of places that do not meet user safety requirements. The implementation of such mechanisms, whether in catalogue systems or distributed systems, will improve the substantive quality of product descriptions, and thus increase their usability and safety and build customer trust in the brands of individual manufacturers.
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