# US 2022/0277331 A1 · Systems and methods for procurement cost forecasting

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- Number: US 2022/0277331 A1
- Document ID: US20220277331A1
- Status: Published application
- Kind: A1
- Application number: 17/754,554
- Filed: 2020-10-29
- Published: 2022-09-01
- Earliest claimed: 2019-10-30
- Assignees: Complete Intelligence Technologies, Inc.
- Applicants: Complete Intelligence Technologies, Inc.
- Inventors: Anthony Nash; Robert Bryngelson
- Web page: https://patentgrep.com/patent/US20220277331A1

## CPC

- G06Q 30/0206: {Price or cost determination based on market factors}
- G06Q 10/087: Inventory or stock management, e.g. order filling, procurement or balancing against orders
- G06Q 10/08726: {for replenishment processing, procedures, or recommendations using forecasting or optimisation}
- G06Q 30/0201: Market modelling; Market analysis; Collecting market data
- G06Q 30/0633: {Managing shopping lists, e.g. compiling or processing purchase lists (shipping orders G06Q10/083; order filling G06Q10/087)}
- G06Q 40/04: Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Systems and methods for automatically predicting component procurement costs using machine learning and a procurement cost forecasting platform are herein disclosed. In one example, a method for a procurement cost forecasting platform comprises, collecting component procurement cost data for a component, pre-processing the component procurement cost data to produce screened and tested component procurement cost data, acquiring historical data, pre-processing the historical data to produce screened and tested historical data, determining a component procurement cost forecast for the component over a pre-determined future duration using a composite machine learning model, the screened and tested component procurement cost data, and the screened and tested historical data, wherein the composite machine learning model comprises a plurality of models, wherein each of the plurality of models comprises one or more internal parameters, and one or more associated weights, and displaying the component procurement cost forecast to a user via a graphical user interface.

## Claims

### Claim 1

A method comprising:
collecting component procurement cost data for a component;
pre-processing the component procurement cost data to produce screened and tested component procurement cost data;
acquiring historical data;
pre-processing the historical data to produce screened and tested historical data;
determining a component procurement cost forecast for the component over a pre-determined future duration using a composite machine learning model, the screened and tested component procurement cost data, and the screened and tested historical data, wherein the composite machine learning model comprises a plurality of models, wherein each of the plurality of models comprises one or more internal parameters, and one or more associated weights; and
displaying the component procurement cost forecast to a user via a graphical user interface.

### Claim 2 (depends on 1)

The method of claim 1, wherein the component procurement cost data comprises one or more purchase orders for the component, and wherein collecting component procurement cost data for the component comprises acquiring the one or more purchase orders from an enterprise device.

### Claim 3 (depends on 2)

The method of claim 2, wherein pre-processing the component procurement cost data to produce screened and tested component procurement cost data comprises:
determining if a purchase order of the one or more purchase orders includes a timestamp; and
responding to the purchase order not including the timestamp by removing the purchase order from the component procurement cost data to produce the screened and tested component procurement cost data.

### Claim 4 (depends on 1)

The method of claim 1, wherein pre-processing the historical data to produce screened and tested historical data comprises:
filtering the historical data using one or more pre-determined filters.

### Claim 5 (depends on 1)

The method of claim 1, wherein the historical data comprises one or more of market data, and economic data.

### Claim 6 (depends on 1)

The method of claim 1, wherein the plurality of models includes one or more of a deep neural network, a recurrent neural network, an ensemble approach including autoregressive integrated moving average (ARIMA), correlation analysis, vector autoregression (VAR), a decision tree, generalized autoregressive conditional heteroscedasticity (GARCH), and exponential smoothing.

### Claim 7 (depends on 1)

The method of claim 1, wherein determining the component procurement cost forecast for the component over the pre-determined future duration using the composite machine learning model, the screened and tested component procurement cost data, and the screened and tested historical data, comprises:
determining if the composite machine learning model was previously trained to predict procurement costs of the component; and
responding to the composite machine learning model having been previously trained to predict procurement costs of the component by:
accessing a memory location where the composite machine learning model is stored;
inputting the screened and tested component procurement cost data and the screened and tested historical data into the plurality of models of the composite machine learning model;
mapping the screened and tested component procurement cost data and the screened and tested historical data to a plurality of component procurement cost forecasts using the plurality of models; and
aggregating the plurality of component procurement cost forecasts to produce the component procurement cost forecast.

### Claim 8 (depends on 7)

The method of claim 7, wherein aggregating the plurality of component procurement cost forecasts to produce the component procurement cost forecast comprises:
determining a weighted average of the plurality of component procurement cost forecasts by multiplying each of the plurality of component procurement cost forecasts by the one or more associated weights to produce a plurality of weighted component procurement cost forecasts; and
averaging the plurality of weighted component procurement cost forecasts to produce a bill of material procurement cost forecast.

### Claim 9 (depends on 1)

The method of claim 1, wherein determining the component procurement cost forecast for the component over the pre-determined future duration using the composite machine learning model, the screened and tested component procurement cost data, and the screened and tested historical data, comprises:
determining if the composite machine learning model was previously trained to predict procurement costs of the component; and
responding to the composite machine learning model having not been previously trained to predict procurement costs of the component by:
separating the screened and tested component procurement cost data and the screened and tested historical data into a first period and a second period, wherein the first period and the second period do not overlap in time, and wherein the first period immediately precedes the second period;
initializing the composite machine learning model;
mapping the screened and tested component procurement cost data and the screened and tested historical data, of the first period, to a predicted procurement cost forecast for the second period;
determining an error of the predicted procurement cost forecast based on the screened and tested component procurement cost data of the second period; and
adjusting one or more parameters of the composite machine learning model based on the error.

### Claim 10 (depends on 9)

The method of claim 9, the method further comprising:
responding to the error being less than a threshold by:
storing the composite machine learning model in a location of non-transitory memory;
inputting the screened and tested component procurement cost data and the screened and tested historical data into the plurality of models of the composite machine learning model;
mapping the screened and tested component procurement cost data and the screened and tested historical data to a plurality of component procurement cost forecasts using the plurality of models; and
determining a weighted average of the plurality of component procurement cost forecasts; and
setting the component procurement cost forecast based on the weighted average of the plurality of component procurement cost forecasts.

### Claim 11 (depends on 1)

The method of claim 1, wherein displaying the component procurement cost forecast to the user via the graphical user interface comprises:
displaying a table within the graphical user interface, wherein the table comprises a plurality of predicted component procurement costs for each of a plurality of pre-determined time points over the pre-determined future duration.

### Claim 12

A procurement cost forecasting platform comprising:
a communication subsystem, wherein the communication subsystem communicably couples the procurement cost forecasting platform with a client device, one or more historical data sources, and an enterprise device;
a data holding subsystem comprising instructions; and
a logic subsystem, wherein, when executing the instructions, the logic subsystem is configured to:
collect component procurement cost data of a component from the enterprise device;
pre-process the component procurement cost data to produce screened and tested component procurement cost data;
acquire historical data from the one or more historical data sources;
pre-process the historical data to produce screened and tested historical data;
determine a component procurement cost forecast for a component over a pre-determined future duration using a composite machine learning model, the screened and tested component procurement cost data, and the screened and tested historical data; and
transmit the component procurement cost forecast to the client device via the communication subsystem.

### Claim 13 (depends on 12)

The procurement cost forecasting platform of claim 12, wherein the one or more historical data sources comprises one or more of a market data server and an economic data server, and wherein the historical data comprises one or more of market data, trade data, and economic data.

### Claim 14 (depends on 12)

The procurement cost forecasting platform of claim 12, wherein, when executing the instructions, the logic subsystem is further configured to:
determine a confidence level and error rate of the component procurement cost forecast; and
transmit the confidence level to the client device via the communication subsystem.

### Claim 15

A method for a procurement cost forecasting platform comprising:
collecting first component procurement cost data for a first component from an enterprise device of a client;
pre-processing the first component procurement cost data to produce screened and tested first component procurement cost data;
mapping the screened and tested first component procurement cost data to a first plurality of component procurement cost forecasts using a plurality of models;
aggregating the first plurality of component procurement cost forecasts to produce a first component procurement cost forecast; and
displaying the first component procurement cost forecast to the client via a graphical user interface of a client device.

### Claim 16 (depends on 15)

The method of claim 15, wherein each of the first plurality of component procurement cost forecasts comprises a plurality of predicted component procurement costs over a pre-determined future duration, wherein the first plurality of predicted component procurement costs are separated by a pre-determined interval of time.

### Claim 17 (depends on 15)

The method of claim 15, wherein aggregating the first plurality of component procurement cost forecasts to produce the first component procurement cost forecast comprises:
determining a weighted average of the first plurality of component procurement cost forecasts by multiplying the first plurality of component procurement cost forecasts by a plurality of associated weights to produce a plurality of weighted component procurement cost forecasts; and
averaging the plurality of weighted component procurement cost forecasts to produce the first component procurement cost forecast.

### Claim 18 (depends on 15)

The method of claim 15, the method further comprising:
collecting second component procurement cost data for a second component from the enterprise device of the client;
pre-processing the second component procurement cost data to produce screened and tested second component procurement cost data;
mapping the screened and tested second component procurement cost data to a second plurality of component procurement cost forecasts using the plurality of models;
aggregating the second plurality of component procurement cost forecasts to produce a second component procurement cost forecast; and
adding the second component procurement cost forecast with the first component procurement cost forecast based on a bill-of-materials level (BOM level) to produce a BOM level procurement cost forecast.

### Claim 19 (depends on 18)

The method of claim 18, when the BOM level procurement cost forecast comprises a first BOM level procurement cost forecast, and wherein the method further comprises:
aggregating the first BOM level procurement cost forecast with at least a second BOM level procurement cost forecast to produce a product procurement cost forecast.

### Claim 20 (depends on 19)

The method of claim 19, wherein the product procurement cost forecast comprises a first product procurement cost forecast, and wherein the method further comprises:
aggregating the first product procurement cost forecast with at least a second product procurement cost forecast to produce a product category forecast, wherein the first product and the second product belong to a same product category.

### Claim at position 21

-22. (canceled)