Procurement modeling system for predicting price reasonableness

A method implemented by computer servers associated with a procurement services platform includes accessing data for a set of transactions associated with a potential procurement transaction between a purchaser entity and a plurality of supplier entities. The transactions include a line-item. The method includes inputting the data for the set of transactions into a machine-learning model trained to generate a prediction of a price quotation for each of the plurality of supplier entities based on the line-item. The prediction of the price quotation includes an estimated reasonable price for a supplier entity to supply a product or service to the purchaser entity. The method includes generating, by the machine-learning model, the prediction of the price quotation for each of the plurality of supplier entities and generating a recommendation for each of the plurality of supplier entities based on the prediction of the price quotation.

TECHNICAL FIELD

This application relates generally to a procurement modeling system, and, more particularly, to a procurement modeling system for predicting price reasonableness for procuring a product or service by a purchaser entity.

BACKGROUND

Procurement services may generally include software for aiding purchaser entities to purchase and procure requisite products or services from one or more supplier entities for purposes of doing business. In many procurement transactions, a purchaser entity's paramount concern may be to ensure that the price to be paid for the requisite products and services is reasonable and/or cost-effective for its business. Thus, in many instances, a purchaser entity may rely upon complex spreadsheets or other legacy bookkeeping applications to manually perform price reasonableness analyses of, for example, one or more proposed quotes received by a purchaser entity. For example, the price reasonableness analysis may include a review and examination of each price quote proposed by respective supplier entities and a determination as to whether the proposed price quotes are reasonable to the purchaser entity. However, such price reasonableness analyses may typically be based on either incomplete or nonspecific transaction data. For example, in some instances, the actual price paid by the purchaser entity to procure requisite products or services may not be learned until after a report is created based on receipts of already completed procurements. Additionally, such reports typically include an aggregate of the price paid by the purchaser entity for one or more procurements, as opposed to, for example, a line-item breakdown of costs associated with one or more products or services. It may be useful to provide techniques to improve price reasonableness analyses.

SUMMARY

Embodiments of the present disclosure are directed toward a procurement modeling system including one or more computer servers for predicting price reasonableness for procuring a product or service by a purchaser entity. In certain embodiments, the one or more computer servers may access data for a set of transactions associated with a potential procurement transaction between a purchaser entity and at least one of a plurality of supplier entities. In one embodiment, at least one of the transactions includes a line-item. In certain embodiments, the set of transactions may include a line-item and an identification of the purchaser entity. In one embodiment, the line-item may include one or more of a price of the product or service, a quantity of the product or service, or a unit of measure (UOM) with respect to the product or service. In certain embodiments, the one or more computer servers may input the data for the set of transactions into a machine-learning model trained to generate a prediction of a price quotation for each of the plurality of supplier entities based on the line-item. In one embodiment, the prediction of the price quotation may include an estimated reasonable price for a supplier entity of the plurality of supplier entities to supply a product or service to the purchaser entity, in which the product or service corresponding to the line-item.

In certain embodiments, the one or more computer servers may then generate, by the machine-learning model, the prediction of the price quotation for each of the plurality of supplier entities. In some embodiments, generating the prediction of the price quotation for each of the plurality of supplier entities may include generating a respective price quotation for each of the plurality of suppliers. In certain embodiments, the one or more computer servers may then generate a recommendation for each of the plurality of supplier entities based on the prediction of the price quotation. In some embodiments, the one or more computer servers may provide the recommendation to each of the plurality of supplier entities. In some embodiments, the one or more computer servers may cause one or more electronic devices associated with the plurality of supplier entities to display the recommendation.

In some embodiments, the machine-learning model comprises a gradient boosting model, an adaptive boosting (AdaBoost) model, an eXtreme gradient boosting (XGBoost) model, a light gradient boosted machine (LightGBM) model, or a categorical boosting (CatBoost) model. In some embodiments, prior to inputting the data for the set of transactions into the machine-learning model, the one or more computer servers may train the machine-learning model based on historical data for a set of transactions. In some embodiments, the historical data for the set of transactions may include one or more of an identification of the plurality of supplier entities, a plurality of price quotations, or line-item master data.

In certain embodiments, the one or more computer servers may receive a requisition for the potential procurement transaction. In certain embodiments, the one or more computer servers may determine, by the machine-learning model, a prediction of an approvability of the requisition based on one or more characteristics of the requisition. In certain embodiments, the one or more computer servers may generate a recommendation for the purchaser entity based on the prediction of the approvability of the requisition. In certain embodiments, the one or more computer servers may generate, by the machine-learning model, a prediction of a purchasing process for the purchaser entity, the prediction of the purchasing process being generated to execute the potential procurement transaction at the reasonable price. In certain embodiments, subsequent to executing the potential procurement transaction at the reasonable price, the one or more computer servers may determine, by the machine-learning model, one or more anomalies based on the procurement transaction or the set of transactions.

Indeed, the present techniques of providing a procurement modeling system including one or more computer servers for predicting price reasonableness for procuring a product or service by a purchaser entity may provide technical improvements to previous or existing procurement-related cloud-computing based platforms. For example, in accordance with the presently disclosed techniques, the procurement modeling system may provide technical improvements to previous or existing procurement-related cloud-computing based platforms by increasing processing speeds of data processors and reducing database queries by indexing and organizing entities of data and manages data in a predetermined structured manner, such that a number of queries to the database in order to surface desired data is reduced as compared to performing a brute-force search of all of the entities of data stored in the database. For example, in accordance with the presently disclosed embodiments, the procurement modeling system may utilize one or machine-learning models to surface and generate desired data including, for example, one or more of a reasonable price quotation, an approvability of a price quote, a requisition, a purchase order, or a contract, a procurement process to procure products or services at the reasonable price quotation, or exceptions or anomalies included in price quote, a requisition, a purchase order, or a contract.

DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG.1illustrates a procurement modeling system and network environment100that may be utilized for predicting price reasonableness for procuring a product or service by a purchaser entity, in accordance with the presently disclosed embodiments. As depicted, in certain embodiments, the procurement modeling system and network environment100may include a data services system102, a server system104, and one or more client systems106. In certain embodiments, the data services system102may include a predictive procurement orchestration (PPO) system, which may include one or more machine-learning model pipelines108for predicting price reasonableness for procuring a product or service by a purchaser entity, for example. Indeed, as will be described in greater detail below with respect toFIGS.2-6, in accordance with the presently disclosed embodiments, the one or more machine-learning model pipelines108may be utilized to predict price reasonableness for procuring a product or service by a purchaser entity by, for example, predicting a price quotation for procuring the product or service by the purchaser entity, predicting an approvability of a requisition, purchase order, or a price quote for the purchaser entity, predicting a purchasing process for the purchaser entity, and determining one or more exceptions or anomalies of a procurement transaction for the purchaser entity.

In certain embodiments, the data services system102may be stored to one or more databases112and hosted and executed utilizing the server system104. For example, in one embodiment, the server system104may include a Platform as a Service (PaaS), a Software as a Service (SaaS), a Compute as a Service (CaaS), an Infrastructure as a Service (IaaS), a Data as a Service (DaaS), a Database as a Service (DBaaS), or other similar cloud-based computing architecture (e.g., “X” as a Service (XaaS)) that may be suitable for generating and providing procurement recommendations to the one or more client systems106. In certain embodiments, as further illustrated byFIG.1, the server system104may include one or more asynchronous processing workers114, a message generator116, and application programming interface (API) services118.

For example, in some embodiments, the one or more asynchronous processing workers114, message generator116, and API services118may operate in conjunction (e.g., in the background) for synchronously or asynchronously providing procurement recommendations to the one or more client systems106by way of browser client(s)120, public APIs122, messaging services124, notification services126, and email services128. For example, in certain embodiments, the one or more client systems106may be associated, for example, with any number of respective purchaser entities, supplier entities, sourcing entities, or other entities that may be included as part of a procurement supply chain to which the procurement recommendations may be provided.

FIG.2illustrates an embodiment of a workflow diagram200for predicting price reasonableness for procuring a product or service by a purchaser entity, in accordance with the presently disclosed embodiments. For example, in certain embodiments, the workflow diagram200may begin with a procurement entity202(e.g., one or more procurement users utilizing one or more of the client systems106) creating a transaction204. For example, in one embodiment, the transaction204may include a price proposal including one or more line-items. The workflow diagram200may then proceed with generating a price prediction206based on the transaction204. For example, in some embodiments, the transaction204may be inputted into a machine-learning model (e.g., one or more ensemble-learning based regression models) trained to generate the price prediction206for each of a number of different supplier entities208,210, and212.

In certain embodiments, the workflow diagram200may proceed with the number of different supplier entities208,210, and212viewing (216) a price proposal218corresponding to the transaction204. In certain embodiments, the workflow diagram200may then proceed with the number of different supplier entities208,210, and212accepting (e.g., “ACCEPTANCE”) or rejecting (e.g., “REJECTION”) the proposed price by submitting and/or resubmitting a quote220based on the price prediction206. In certain embodiments, the workflow diagram200may then continue with the purchaser entity202evaluating one or more quotes222and awarding a transaction224.

FIG.3illustrates an embodiment of a procurement modeling system300for predicting a price quotation for procuring a product or service by a purchaser entity, in accordance with the presently disclosed embodiments. For example, as illustrated byFIG.3, the procurement modeling system300may include one or more database(s)304and one or more machine-learning model pipelines306. In certain embodiments, during the training phase of the one or more machine-learning model pipelines304, for example, a data ingestion server308of the one or more database(s)304may receive transaction data302(e.g., historical transaction data collected from any number of various purchaser entities or supplier entities over some period of time). For example, in some embodiments, the transaction data302may include historical line-item data including, for example, prices of products or services previously procured, quantities of the products or services previously procured, units of measure (UOM) with respect to the products or services previously procured, and so forth.

In certain embodiments, as further depicted byFIG.3, the transaction data302may be stored to a relational database316(e.g., PostgreSQL or other similar object-relational database (ORD)) suitable for storing the transaction data302along with supplier data attributes310, price quotes data attributes312, and line-item master data314. For example, in certain embodiments, the supplier data attributes310may include, for example, an identification of supplier entities, a specific company name for supplier entities, geographical location of supplier entities, an identification of key supplier entities, an identification of valuable supplier entities, and so forth. Similarly, in certain embodiments, the price quotes data attributes312may include, for example, line-item data including an identification of a particular product or service, a price of the particular product or service, a quantity of the particular product or service, a UOM with respect to the particular product or service, a currency utilized to purchase the particular product or service, an identification of the purchaser entity associated with the particular product or service, a line-item identification associated with the particular product or service, a category identification associated with the particular product or service, and a supplier entity identification associated with the particular product or service.

In certain embodiments, the line-item master data314may include, for example, a catalog or data log of all of the information that may be associated with products or services regularly, intermittently, or previously purchased by a particular purchaser entity, and may further include an identification of the purchaser entity, a description of the products or services identified by item-line, a category identification associated with the products or services, and so forth. In certain embodiments, the transaction data302along with supplier data attributes310, price quotes data attributes312, and line-item master data314stored to the relational database316(e.g., PostgreSQL, ORD) may be utilized to train the one or more machine-learning model pipelines304, and particularly one or more predictive procurement models322. For example, in some embodiments, the one or more predictive procurement models322may include an ensemble-learning based regression model, such as a gradient boosting model, an adaptive boosting (AdaBoost) model, an eXtreme gradient boosting (XGBoost) model, a light gradient boosted machine (LightGBM) model, a categorical boosting (CatBoost) model, or a random forest model.

For example, in certain embodiments, the transaction data302and the supplier data attributes310, price quotes data attributes312, and line-item master data314stored to the relational database316(e.g., PostgreSQL, ORD) may be accessed. In certain embodiments, before the transaction data302and the supplier data attributes310, price quotes data attributes312, and line-item master data314are inputted to the one or more predictive procurement models322for training the one or more predictive procurement models322to predict a price quotation for procuring a product or service by a purchaser entity, these data may be passed to an outlier detector324and a data cleaner326that may be utilized to removed anomalous line item prices (e.g., baseline prices) and outliers (e.g., edge-case prices) to avoid potentially overfitting the one or more predictive procurement models322.

In certain embodiments, the transaction data302and the supplier data attributes310, price quotes data attributes312, and line-item master data314may be then inputted to the one or more predictive procurement models322, which may be iteratively trained to generate predictions of price quotations (e.g., based on training data at a line-item level of granularity as generally discussed above) for procuring particular products or services at a reasonable price. For example, as further illustrated by the procurement modeling system300ofFIG.3, once trained, the one or more predictive procurement models322may receive a newly proposed transaction318(e.g., corresponding to one or more price quotes received by a purchaser entity in real-time or near real-time) by way of a prediction query server320. For example, as previously noted, the proposed transaction318may include line-item data for one or more particular products or services being potentially procured by a purchaser entity.

In certain embodiments, based on the line-item data included in the proposed transaction318, the one or more predictive procurement models322may then generate a prediction of a price quotation328(e.g., an estimated reasonable price) for procuring the one or more particular products or services corresponding to the line-item data included in the proposed transaction318. For example, in some embodiments, the prediction of the price quotation328(e.g., an estimated reasonable price) may include a respective recommendation that may be provided to each of a number of respective supplier entities. Specifically, the prediction of the price quotation328(e.g., an estimated reasonable price) may include, for example, a respective reasonable price that each of the number of respective supplier entities is expected to propose to the purchaser entity for procuring one or more particular products or services.

FIG.4illustrates an embodiment of a procurement modeling system400for predicting an approvability of a requisition, a purchase order, or a price quote for a purchaser entity, in accordance with the presently disclosed embodiments. For example, as illustrated byFIG.4, the procurement modeling system400may include one or more database(s)404and one or more machine-learning model pipelines406. In certain embodiments, during the training phase of the one or more machine-learning model pipelines404, for example, a data ingestion server408of the one or more database(s)404may receive transaction data402(e.g., historical transaction data collected from any number of various purchaser entities or supplier entities over some period of time). For example, in some embodiments, the transaction data402may include historical line-item data including, for example, prices of products or services previously procured, quantities of the products or services previously procured, UOM with respect to the products or services previously procured, and so forth.

In certain embodiments, as further depicted byFIG.4, the transaction data408may be stored to a relational database416(e.g., PostgreSQL or other similar ORD)) suitable for storing the transaction data408along with purchaser orders data attributes410, line-item master data412, requisitions data attributes414, and supplier data attributes416. For example, in certain embodiments, purchaser orders data attributes410may include any information that may be included, for example, in purchase orders generated by various purchaser entities once prices for a certain number of products or services have been agreed upon between the purchaser entity and associated supplier entities. For example, in one embodiment, the purchaser orders data attributes410may include, for example, a date of an order of the products or services, address location of the supplier entities, shipping information associated the products or services and/or the supplier entities, discount terms that may be associated with the products or services, an identification of the supplier entities, an identification of the purchaser entity, a description of the products or services identified by item-line, an identification of the purchaser order (e.g., PO number), and so forth.

Similarly, in some embodiments, the line-item master data412may include, for example, a catalog or data log of all of the information that may be associated with products or services regularly, intermittently, or previously purchased by a particular purchaser entity, and may further include an identification of the purchaser entity, a description of the products or services identified by item-line, a category identification associated with the products or services, and so forth. In certain embodiments, the requisitions data attributes414may include any information that may be included, for example, in requisitions generated by various purchaser entities for internal record-keeping and/or accounting with respect to any products or services ordered or to be ordered.

For example, in one embodiment, the requisitions data attributes414may include, for example, an identification of the products or services ordered or to be ordered, a price of the products or services ordered or to be ordered, a quantity of the products or services ordered or to be ordered, a UOM with respect to the products or services ordered or to be ordered, a currency utilized purchase or price the products or services ordered or to be ordered, an identification of the purchaser entity, a date of the order for the products or services, an identification of a line-item associated with the products or services ordered or to be ordered, an identification of a category for the products or services ordered or to be ordered, and an identification of the supplier entities. In certain embodiments, the supplier data attributes416may include, for example, an identification of supplier entities, a specific company name for supplier entities, geographical location of supplier entities, an identification of key supplier entities, an identification of valuable supplier entities, and so forth.

In certain embodiments, the transaction data408along with the purchaser orders data attributes410, the line-item master data412, the requisitions data attributes414, and the supplier data attributes416stored to the relational database418(e.g., PostgreSQ or other similar ORD) may be utilized to train the one or more machine-learning model pipelines404, and particularly one or more predictive procurement models424. For example, in some embodiments, the one or more predictive procurement models424may include an ensemble-learning based regression model, such as a gradient boosting model, an AdaBoost model, an XGBoost model, a LightGBM model, a CatBoost model, or a random forest model.

For example, in certain embodiments, the transaction data408along with the purchaser orders data attributes410, the line-item master data412, the requisitions data attributes414, and the supplier data attributes416stored to the relational database418stored to the relational database418may be accessed. In certain embodiments, before the transaction data408and the purchaser orders data attributes410, the line-item master data412, the requisitions data attributes414, and the supplier data attributes416are inputted to the one or more predictive procurement models424for training the one or more predictive procurement models424to predict a price quotation for procuring a product or service by a purchaser entity, these data may be passed to an outlier detector426and a data cleaner428that may be utilized to remove anomalous line item data and outliers (e.g., edge-cases) to avoid potentially overfitting the one or more predictive procurement models424.

In certain embodiments, the transaction data404and the purchaser orders data attributes410, the line-item master data412, the requisitions data attributes414, and the supplier data attributes416may be then inputted to the one or more predictive procurement models424, which may be iteratively trained to generate predictions of an approval and/or disapproval of a purchase order or requisition (e.g., based on training data at a line-item level of granularity as generally discussed above) for procuring particular products or services at a reasonable price. For example, as further illustrated by the procurement modeling system400ofFIG.4, once trained, the one or more predictive procurement models424may receive a newly proposed transaction420(e.g., corresponding to one or more purchaser orders or requisitions received by a purchaser entity in real-time or near real-time) by way of a prediction query server422.

For example, as previously noted, the proposed transaction420may include line-item data in a requisition, a purchase order, or a price quote for one or more particular products or services being potentially procured by a purchaser entity. In certain embodiments, based on the line-item data included in the proposed transaction420, the one or more predictive procurement models424may then generate a prediction of an approval and/or disapproval430for the purchase order, requisition, or price quote corresponding to the proposed transaction420. For example, in some embodiments, the prediction of an approval and/or disapproval430may include a recommendation of whether the purchaser entity should accept or reject the purchase order, requisition, or price quote. Specifically, the prediction of an approval and/or disapproval430may include, for example, a recommendation that is provided to the purchaser entity of whether to accept or reject the purchase order, requisition, or price quote received from one or more supplier entities.

FIG.5illustrates an embodiment of a procurement modeling system500for predicting a purchasing process for a purchaser entity, in accordance with the presently disclosed embodiments. For example, as illustrated byFIG.5, the procurement modeling system500may include one or more database(s)504and one or more machine-learning model pipelines506. In certain embodiments, during the training phase of the one or more machine-learning model pipelines506, for example, a data ingestion server508of the one or more database(s)504may receive transaction data502(e.g., historical transaction data collected from any number of various purchaser entities or supplier entities over some period of time). For example, in some embodiments, the transaction data502may include historical line-item data including, for example, prices of products or services previously procured, quantities of the products or services previously procured, UOM with respect to the products or services previously procured, and so forth.

In certain embodiments, as further depicted byFIG.5, the transaction data502may be stored to a relational database518(e.g., PostgreSQL or other similar ORD)) suitable for storing the transaction data502along with category master data510, line-item master data512, historical transactions data attributes514, and supplier data attributes516. For example, in certain embodiments, the supplier data attributes516may include, for example, an identification of supplier entities, a specific company name for supplier entities, geographical location of supplier entities, an identification of key supplier entities, an identification of valuable supplier entities, and so forth. In certain embodiments, the line-item master data512may include, for example, a catalog or data log of all of the information that may be associated with products or services regularly, intermittently, or previously purchased by a particular purchaser entity, and may further include an identification of the purchaser entity, a description of the products or services identified by item-line, a category identification associated with the products or services, and so forth. Similarly, in certain embodiments, the historical transactions data attributes514may include, for example, line-item data derived from one or more historical data sets of requisitions, invoices, purchase orders, price quotes, sourcing events, and so forth.

For example, in some embodiments, the historical transactions data attributes514may include an identification of products or services included in the historical transaction data, a price of the product or services included in the historical transactions data, a quantity of the products or services in the historical transactions data, a UOM with respect to the products or services included in the historical transactions data, a currency utilized to purchase the products or services included in the historical transactions data, an identification of the purchaser entity associated with the products or services included in the historical transactions data, a line-item identification associated with the products or services included in the historical transactions data, a category identification associated with the products or services included in the historical transactions data, a supplier entity identification associated with the products or services included in the historical transactions data, a number of the requisitions, invoices, purchase orders, or sourcing events included in the historical transactions data, and a duration associated with the requisitions, invoices, purchase orders, or sourcing events included in the historical transactions data.

In certain embodiments, the category master data510may include an identification category, a description category, one or more naming categories, or a parent identification category that may be associated with supplier entities, purchaser entities, and/or one or more particular products or services. In certain embodiments, the transaction data502along with the category master data510, the line-item master data512, the historical transactions data attributes514, and the supplier data attributes516stored to the relational database518(e.g., PostgreSQL, ORD) may be utilized to train the one or more machine-learning model pipelines506, and particularly one or more predictive procurement models524. For example, in some embodiments, the one or more predictive procurement models524may include an ensemble-learning based regression model, such as a gradient boosting model, an AdaBoost model, an XGBoost model, a LightGBM model, a CatBoost model, or a random forest model.

For example, in certain embodiments, the category master data510, the line-item master data512, the historical transactions data attributes514, and the supplier data attributes516stored to the relational database518(e.g., PostgreSQL, ORD) may be accessed. In certain embodiments, before the transaction data502and the category master data510, the line-item master data512, the historical transactions data attributes514, and the supplier data attributes516are inputted to the one or more predictive procurement models524for training the one or more predictive procurement models524to generate a prediction of a purchasing process for a purchaser entity for procuring a product or service at a reasonable price, these data may be passed to an outlier detector526and a data cleaner528that may be utilized to removed anomalous line item prices (e.g., baseline prices) and outliers (e.g., edge-case prices) to avoid potentially overfitting the one or more predictive procurement models524.

In certain embodiments, the category master data510, the line-item master data512, the historical transactions data attributes514, and the supplier data attributes516may be then inputted to the one or more predictive procurement models524, which may be iteratively trained to generate predictions of purchasing processes for procuring particular products or services at a reasonable price (e.g., the predicted price quotation328as described above with respect toFIG.3). For example, as further illustrated by the procurement modeling system500ofFIG.5, once trained, the one or more predictive procurement models524may receive a newly proposed transaction520(e.g., corresponding to one or more price quotes received by a purchaser entity in real-time or near real-time) by way of a prediction query server522.

For example, as previously noted, the proposed transaction520may include line-item data for one or more particular products or services being potentially procured by a purchaser entity. In certain embodiments, based on the line-item data included in the proposed transaction520, the one or more predictive procurement models524may then generate a prediction of the purchasing process530, which may include a recommendation of a prescribed course of negotiation or procurement in which a purchaser entity should follow in order to procure the one or more particular products or services corresponding to the line-item data included in the proposed transaction520at a reasonable price (e.g., the predicted price quotation328as described above with respect toFIG.3). In one embodiment, the prediction of the purchasing process530may include, for example, a recommendation provided to a purchaser entity regarding the procurement process that is to be used to evaluate the reasonableness of proposed price.

FIG.6illustrates an embodiment of a procurement modeling system600for determining one or more anomalies of a procurement transaction for a purchaser entity, in accordance with the presently disclosed embodiments. For example, as illustrated byFIG.6, the procurement modeling system600may include one or more database(s)604and one or more machine-learning model pipelines606. In certain embodiments, during the training phase of the one or more machine-learning model pipelines606, for example, a data ingestion server608of the one or more database(s)604may receive transaction data602(e.g., historical transaction data collected from any number of various purchaser entities or supplier entities over some period of time). For example, in some embodiments, the transaction data602may include historical line-item data including, for example, prices of products or services previously procured, quantities of the products or services previously procured, UOM with respect to the products or services previously procured, and so forth.

In certain embodiments, as further depicted byFIG.6, the transaction data602may be stored to a relational database618(e.g., PostgreSQL or other similar ORD)) suitable for storing the transaction data602along with contracts data attributes610, line-item master data612, price quotes or purchase orders data attributes614, and supplier data attributes616. For example, in certain embodiments, the supplier data attributes616may include, for example, an identification of supplier entities, a specific company name for supplier entities, geographical location of supplier entities, an identification of key supplier entities, an identification of valuable supplier entities, and so forth. In certain embodiments, the line-item master data612may include, for example, a catalog or data log of all of the information that may be associated with products or services regularly, intermittently, or previously purchased by a particular purchaser entity, and may further include an identification of the purchaser entity, a description of the products or services identified by item-line, a category identification associated with the products or services, and so forth.

In certain embodiments, the price quotes or purchase orders data attributes614may include, for example, line-item data including an identification of a particular product or service, a price of the particular product or service, a quantity of the particular product or service, a UOM with respect to the particular product or service, a currency utilized to purchase the particular product or service, an identification of the purchaser entity associated with the particular product or service, a line-item identification associated with the particular product or service, a category identification associated with the particular product or service, and a supplier entity identification associated with the particular product or service. In certain embodiments, the contracts data attributes610may include, for example, line-item data including a line-item identification associated with a particular product or service, a price of the particular product or service, a quantity of the particular product or service, a UOM with respect to the particular product or service, a currency utilized to purchase the particular product or service, a start date of a contract, a license, a warranty, and so forth, an expiration date of a contract, a license, a warranty, and so forth.

In certain embodiments, the transaction data602along with the contracts data attributes610, the line-item master data612, the price quotes or purchase orders data attributes614, and the supplier data attributes616stored to the relational database618(e.g., PostgreSQL, ORD) may be utilized to train the one or more machine-learning model pipelines606, and particularly one or more predictive procurement models624. For example, in some embodiments, the one or more predictive procurement models624may include an ensemble-learning based regression model, such as a gradient boosting model, an AdaBoost model, an XGBoost model, a LightGBM model, a CatBoost model, or a random forest model.

For example, in certain embodiments, the contracts data attributes610, the line-item master data612, the price quotes or purchase orders data attributes614, and the supplier data attributes616stored to the relational database618(e.g., PostgreSQL, ORD) may be accessed. In certain embodiments, before the transaction data602and the contracts data attributes610, the line-item master data612, the price quotes or purchase orders data attributes614, and the supplier data attributes616are inputted to the one or more predictive procurement models624for training the one or more predictive procurement models624to generate a prediction of one or more exceptions or anomalies, these data may be passed to an outlier detector626and a data cleaner628that may be utilized to removed anomalous line item prices (e.g., baseline prices) and outliers (e.g., edge-case prices) to avoid potentially overfitting the one or more predictive procurement models624.

In certain embodiments, the contracts data attributes610, the line-item master data612, the price quotes or purchase orders data attributes614, and the supplier data attributes616may be then inputted to the one or more predictive procurement models624, which may be iteratively trained to generate predictions of one or more exceptions or anomalies (e.g., the anomalies or exceptions may be detected before ever appearing in a potential purchase order or may be detected in a purchase order for products or services previously procured by a purchaser entity). For example, as further illustrated by the procurement modeling system600ofFIG.6, once trained, the one or more predictive procurement models624may receive a previous transaction620by way of a prediction query server622.

For example, the previous transaction620may include line-item data for one or more particular products or services being potentially procured by a purchaser entity or a purchase order for products or services previously procured by the purchaser entity. In certain embodiments, based on the line-item data included in the previous transaction620, the one or more predictive procurement models624may then detect one or more exceptions or anomalies632, which may include a recommendation generated by a notification queue630. Specifically, in some embodiments, the detected one or more exceptions or anomalies632may include, for example, a recommendation with respect to one or more risk signals or exceptions regarding prices included in the previous transaction620. In one embodiment, the recommendation may be provided to, for example, a financial controller or other similar accounting professional that may be associated with a purchaser entity.

FIG.7illustrates a flow diagram of a method700for predicting a price quotation for procuring a product or service by a purchaser entity, in accordance with the presently disclosed embodiments. The method700may be performed utilizing one or more processing devices that may include hardware (e.g., a general purpose processor, a graphic processing unit (GPU), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a microcontroller, a field-programmable gate array (FPGA), a central processing unit (CPU), an application processor (AP), a visual processing unit (VPU), a neural processing unit (NPU), a neural decision processor (NDP), a deep learning processor (DLP), a tensor processing unit (TPU), neuromorphic processing unit (NPU), a wafer-scale engine (WSE), or any of various hardware artificial intelligence (AI) accelerators) that may be suitable for processing various transactions and making one or more decisions based thereon, software (e.g., instructions running/executing on one or more processing devices), firmware (e.g., microcode), or some combination thereof.

The method700may begin at block702with one or more processing devices accessing data for a set of transactions associated with a potential procurement transaction between a purchaser entity and at least one of a plurality of supplier entities. For example, at least one transaction of the set of transactions may include a line-item. The method700may continue at block704with one or more processing devices inputting the data for the set of transactions into a machine-learning model trained to generate a prediction of a price quotation for each of the plurality of supplier entities based on the line-item. For example, the prediction of the price quotation may include an estimated reasonable price for a supplier entity of the plurality of supplier entities to supply a product or service to the purchaser entity, in which the product or service corresponds to the line-item. The method700may then continue at block706with one or more processing devices generating, by the machine-learning model, the prediction of the price quotation for each of the plurality of supplier entities. The method700may then continue at block708with one or more processing devices generating a recommendation for each of the plurality of supplier entities based on the prediction of the price quotation.

FIG.8illustrates an example procurement modeling computing system800(which may be included as part of the procurement services platform102) that may be utilized for predicting price reasonableness for procuring a product or service by a purchaser entity, in accordance with the presently disclosed embodiments. In certain embodiments, one or more financial services computing system800perform one or more steps of one or more methods described or illustrated herein. In certain embodiments, one or more financial services computing system800provide functionality described or illustrated herein. In certain embodiments, software running on one or more financial services computing system800performs one or more steps of one or more methods described or illustrated herein or provides functionality described or illustrated herein. Certain embodiments include one or more portions of one or more procurement modeling computing system800. Herein, reference to a computer system may encompass a computing device, and vice versa, where appropriate. Moreover, reference to a computer system may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of financial services computing systems800. This disclosure contemplates computer system800taking any suitable physical form. As example and not by way of limitation, computer system800may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (e.g., a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, an augmented/virtual reality device, or a combination of two or more of these. Where appropriate, computer system800may include one or more financial services computing system800; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks.

Where appropriate, one or more financial services computing system800may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example, and not by way of limitation, one or more financial services computing system800may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more financial services computing system800may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.

In certain embodiments, computer system800includes a processor802, memory804, storage806, an input/output (I/O) interface808, a communication interface810, and a bus812. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement. In certain embodiments, processor802includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, processor802may retrieve (or fetch) the instructions from an internal register, an internal cache, memory804, or storage806; decode and execute them; and then write one or more results to an internal register, an internal cache, memory804, or storage806.

In certain embodiments, processor802may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor802including any suitable number of any suitable internal caches, where appropriate. As an example, and not by way of limitation, processor802may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in memory804or storage806, and the instruction caches may speed up retrieval of those instructions by processor802.

Data in the data caches may be copies of data in memory804or storage806for instructions executing at processor802to operate on; the results of previous instructions executed at processor802for access by subsequent instructions executing at processor802or for writing to memory804or storage806; or other suitable data. The data caches may speed up read or write operations by processor802. The TLBs may speed up virtual-address translation for processor802. In certain embodiments, processor802may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor802including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor802may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors802. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.

In certain embodiments, memory804includes main memory for storing instructions for processor802to execute or data for processor802to operate on. As an example, and not by way of limitation, computer system800may load instructions from storage806or another source (such as, for example, another computer system800) to memory804. Processor802may then load the instructions from memory804to an internal register or internal cache. To execute the instructions, processor802may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor802may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor802may then write one or more of those results to memory804. In certain embodiments, processor802executes only instructions in one or more internal registers or internal caches or in memory804(as opposed to storage806or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory804(as opposed to storage806or elsewhere).

One or more memory buses (which may each include an address bus and a data bus) may couple processor802to memory804. Bus812may include one or more memory buses, as described below. In certain embodiments, one or more memory management units (MMUs) reside between processor802and memory804and facilitate accesses to memory804requested by processor802. In certain embodiments, memory804includes random access memory (RAM). This RAM may be volatile memory, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memory804may include one or more memory devices804, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.

In certain embodiments, storage806includes mass storage for data or instructions. As an example, and not by way of limitation, storage806may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storage806may include removable or non-removable (or fixed) media, where appropriate. Storage806may be internal or external to computer system800, where appropriate. In certain embodiments, storage806is non-volatile, solid-state memory. In certain embodiments, storage806includes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. This disclosure contemplates mass storage806taking any suitable physical form. Storage806may include one or more storage control units facilitating communication between processor802and storage806, where appropriate. Where appropriate, storage806may include one or more storages806. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.

In certain embodiments, communication interface810includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system800and one or more other computer systems800or one or more networks. As an example, and not by way of limitation, communication interface810may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interface810for it.

As an example, and not by way of limitation, computer system800may communicate with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, computer system800may communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination of two or more of these. Computer system800may include any suitable communication interface810for any of these networks, where appropriate. Communication interface810may include one or more communication interfaces810, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.

In certain embodiments, bus812includes hardware, software, or both coupling components of computer system800to each other. As an example, and not by way of limitation, bus812may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these. Bus812may include one or more buses812, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

Herein, “automatically” and its derivatives means “without human intervention,” unless expressly indicated otherwise or indicated otherwise by context.