Searching For Ai Solutions To Improve The Quality Of Master Data Affecting Consumer Safety

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.