Abstract: Commodity category values can be determined automatically for suppliers in an e-procurement system using a computer-implemented process that is supplier-focused and uses successive heuristics, supplemented with machine learning models that predict category and subcategory values based on supplier names and invoice descriptions. Embodiments can support community intelligence applications to enable buyer computers to query and obtain lists of suppliers corresponding to categories and to generate graphs or charts that aggregate historic invoice data based on canonical category values that have been determined for suppliers.

Abstract: Commodity category values can be determined automatically for suppliers in an e-procurement system using a computer-implemented process that is supplier-focused and uses successive heuristics, supplemented with machine learning models that predict category and subcategory values based on supplier names and invoice descriptions. Embodiments can support community intelligence applications to enable buyer computers to query and obtain lists of suppliers corresponding to categories and to generate graphs or charts that aggregate historic invoice data based on canonical category values that have been determined for suppliers.

Abstract: In an embodiment, a method for dynamically generating and using peer groups in an e-procurement system includes creating an entity database with a plurality of attributes associated with entities and an ordered table of matching rules. Each matching rule having a priority value and two or more matching attributes. The method includes receiving a user input specifying a particular entity for generating a current peer group of other entities. The method includes accessing the entity database to retrieve particular attributes of the particular entity and querying the entity database to receive a result set of matching entities as the current peer group of other entities for the particular entity based on the particular attributes of the particular entity and the matching rules. The method further includes appending data for the matching entities in the result set to a peer group data structure associated with the particular entity.

Abstract: In an embodiment, a method for dynamically generating and using peer groups in an e-procurement system includes creating an entity database with a plurality of attributes associated with entities and an ordered table of matching rules. Each matching rule having a priority value and two or more matching attributes. The method includes receiving a user input specifying a particular entity for generating a current peer group of other entities. The method includes accessing the entity database to retrieve particular attributes of the particular entity and querying the entity database to receive a result set of matching entities as the current peer group of other entities for the particular entity based on the particular attributes of the particular entity and the matching rules. The method further includes appending data for the matching entities in the result set to a peer group data structure associated with the particular entity.

Abstract: Commodity category values can be determined automatically for suppliers in an e-procurement system using a computer-implemented process that is supplier-focused and uses successive heuristics, supplemented with machine learning models that predict category and subcategory values based on supplier names and invoice descriptions. Embodiments can support community intelligence applications to enable buyer computers to query and obtain lists of suppliers corresponding to categories and to generate graphs or charts that aggregate historic invoice data based on canonical category values that have been determined for suppliers.

Abstract: A computer-implemented method of executing a programmed spend management computer system. The computer system comprises a data pre-processor that is communicatively coupled to a plurality of the application instances and accesses historic transaction data from any of the instances and thereby has access to a large community of data across all tenants. The data pre-processor is programmed to normalize transaction descriptions and determine line spend values, unit price values, quantity values, and buyer country data for a plurality of commodities, and to store the data in item sets in digital storage. A statistical processor is coupled to the digital storage to access the item sets and executes statistical calculation on the item sets to generate pricing insight data. Pricing insights and/or prescriptions are generated automatically under stored program control and provided to a presentation processor for output to and/or rendering to an end-user device.

Abstract: A fuel transport sensor system for precise loading and unloading of fuel for transports includes a transport computer configured to be coupled to a fuel transport having a plurality of fuel compartments. Each of a plurality of load head sensors is in operational communication with the transport computer and coupled to one of a plurality of load heads. A load arm sensor coupled to a load arm and the load arm sensors communicate with the transport computer to track which fuel compartment is being filled with what fuel type. A pair of hose sensors coupled to a delivery hose confirm the delivery hose is connecting the load head with the correct fuel type for an in-ground storage tank before allowing a fire valve of the load head to be opened.