Omnichannel procurement orchestration system for generating recommendations and scoring impact

A method for generating one or more recommendations based on a scoring for each of a plurality of procurement activities or channels associated with procuring a product or service, comprising, by one or more servers of an omnichannel procurement orchestration system:

• accessing, omnichannel procurement orchestration system, data for a set of transactions associated with a proposed procurement transaction between at least one of a plurality of purchaser entities and at least one of a plurality of supplier entities, wherein the data for the set of transactions is received from a plurality of disparate data sources and by way of a plurality of disparate procurement channels;
• determining, by the omnichannel procurement orchestration system, and based on the data for the set of transactions associated with the proposed procurement transaction, a plurality of procurement activities to be performed to execute the proposed procurement transaction;
• generating, by the omnichannel procurement orchestration system, and based on the plurality of procurement activities, a score for each of the plurality of procurement activities, wherein the score for each of the plurality of procurement activities comprises a likelihood of a favorable outcome for proposed procurement transaction;
• and
• generating, by the omnichannel procurement orchestration system, a recommendation based on the score for each of the plurality of procurement activities.

An omnichannel procurement orchestration system for generating a recommendation for procuring a product or service particular line-item by a purchaser, comprising:

• one or more non-transitory computer-readable storage media including instructions;
• and
• one or more processors coupled to the one or more storage media, the one or more processors configured to execute the instructions to:
• receiving data for a set of transactions associated with a proposed procurement transaction between at least one of a plurality of purchasers and at least one of a plurality of suppliers,
• wherein the data for the set of transactions is received from a plurality of disparate data sources associated with disparate data structures or schemas,
• wherein the data for the set of transactions is received by way of a plurality of disparate procurement channels,
• wherein the plurality of disparate procurement channels comprises a plurality of software-based applications or computing devices separate from the omnichannel procurement orchestration system, and
• wherein the data comprises:
• a set of attributes of a plurality of line-items associated with the set of transactions, wherein the plurality of line-items comprises one or more line-items other than the particular line-item, and
• a set of attributes of the at least one of a plurality of suppliers;
• determine a quantifiable feature or attribute associated with the proposed procurement transaction by comparing one or more data values associated with the proposed procurement transaction to a predicted range of values for the quantifiable feature or attribute, the predicted range of values being identified using machine learning based on the data for the set of transactions received from the plurality of disparate data sources and by way of the plurality of disparate procurement channels;
• and
• in response to the comparison of the one or more data values associated with the proposed procurement transaction and the predicted range of values for the quantifiable feature or attribute:
• generate, by one or more machine-learning models, a recommendation comprising a set of instructions for acting upon the quantifiable feature or attribute associated with the proposed procurement transaction and further comprising a next-best-action toward completing the proposed procurement transaction;
• and
• providing, by way of one or more of the plurality of disparate procurement channels, the recommendation to the at least one of the plurality of purchasers or the at least one of the plurality of suppliers;
• and
• receive, by the omnichannel procurement orchestration system, selection data comprising a user-selected approval, rejection, or update of the set of instructions or the next-best action of the recommendation;
• execute the recommendation based on the selection data comprising the user-selected approval, rejection, or update of the set of instructions or the next-best action of the recommendation;
• iteratively train the one or more machine-learning models of the omnichannel procurement orchestration system based at least partially on the selection data comprising the user-selected approval, rejection, or update of the set of instructions or the next-best action of the recommendation;
• and
• execute, by the omnichannel procurement orchestration system, the iteratively trained one or more machine-learning models to determine one or more updated recommendations.