SYSTEM AND METHOD FOR FORECASTING USING MONTE CARLO METHODS

• US 20160260109A1
• Filed: 03/04/2015
• Published: 09/08/2016
• Est. Priority Date: 03/04/2015
• Status: Active Grant

• Current Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Original Assignee: Wal-Mart Stores Texas LLC (WalMart Inc.)
• Inventors: Feng, Huijun, Ray, Shubhankar
• Granted Patent: US 10,445,689 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

A system and method for calculating demand forecasts is presented. Sales data for a set of SKUs is received. The sales data is filtered to contain only data for low-selling SKUs. A set of clusters of SKUs is created. A generalized dynamic linear model for use with each cluster in the set of clusters is generated. A set of random data points is generated. The dynamic linear model is fitted at each data point in the set of random data points using a Monte Carlo method. This fitting can be performed using an unscented Kalman filter method. Calculating a forecast for sales based on the fitting at each data point. Using the forecast for sales, inventory is ordered. Other embodiments are also disclosed herein.

Claims

1. A method comprising:

receiving sales data for a set of stock keeping units (SKUs);

filtering the sales data to contain only data for low-selling SKUs, within the set of SKUs that have sales within a bottom twenty percent of the set of SKUs;

creating a set of clusters of SKUs from the set of SKUs;

generating a dynamic linear model for use with each cluster in the set of clusters;

generating a set of random data points;

fitting the dynamic linear model at each data point in the set of random data points using a Monte Carlo method;

calculating a forecast for sales of the low-selling SKUs based on the fitting at each data point in the set of random data points; and

ordering inventory based on the forecast for sales of the low-selling SKUs.

2. The method of claim 1 wherein:

the set of random data points are chosen based on an expected value and covariance of the dynamic linear model.

3. The method of claim 2 wherein:

the set of random data points comprises approximately 1,000 data points.

4. The method of claim 1 wherein:

fitting the dynamic linear model at each data point in the set of random data points using the Monte Carlo method comprises using a Cholesky decomposition of an expected value matrix based on the dynamic linear model.

5. The method of claim 1 further comprising:

repeating calculating the forecast for a plurality of data points.

6. The method of claim 1 wherein:

fitting the dynamic linear model at each point in the set of random data points comprises using a Kalman filter to fit the dynamic linear model by calculating a value of an integral at each data point in the set of random data points.

7. The method of claim 1 wherein:

fitting the dynamic linear model at each data point in the set of random data points comprises using an unscented Kalman filter using the set of random data points in place of a set of sigma points.

8. A system comprising:

a user input device;

a display device;

one or more processing modules; and

one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform the acts of;

receiving sales data for a set of stock keeping units (SKUs);

filtering the sales data to contain only data for low-selling SKUs, within the set of SKUs that have sales within a bottom twenty percent of the set of SKUs;

creating a set of clusters of SKUs from the set of SKUs;

generating a dynamic linear model for use with each cluster in the set of clusters;

generating a set of random data points;

fitting the dynamic linear model at each data point in the set of random data points using a Monte Carlo method;

calculating a forecast for sales of the low-selling SKUs based on the fitting at each data point in the set of random data points; and

ordering inventory based on the forecast for sales of the low-selling SKUs.

9. The system of claim 8 wherein:

the set of random data points are chosen based on an expected value and covariance of the dynamic linear model.

10. The system of claim 9 wherein:

the set of random data points comprises approximately 1,000 data points.

11. The system of claim 8 wherein:

fitting the dynamic linear model at each data point in the set of random data points using the Monte Carlo method comprises using a Cholesky decomposition of an expected value matrix based on the dynamic linear model.

12. The system of claim 8 wherein the computing instructions further comprise:

repeating calculating the forecast for a plurality of data points.

13. The system of claim 8 wherein:

fitting the dynamic linear model at each point in the set of random data points comprises using a Kalman filter to fit the dynamic linear model by calculating a value of an integral at each data point in the set of random data points.

14. The system of claim 8 wherein:

fitting the dynamic linear model at each data point in the set of random data points comprises using an unscented Kalman filter using the set of random data points in place of a set of sigma points.

15. At least one non-transitory storage module having computing instructions stored thereon executable to perform the acts of:

receiving sales data for a set of stock keeping units (SKUs);

filtering the sales data to contain only data for low-selling SKUs, within the set of SKUs that have sales within a bottom twenty percent of the set of SKUs;

creating a set of clusters of SKUs from the set of SKUs;

generating a dynamic linear model for use with each cluster in the set of clusters;

generating a set of random data points;

fitting the dynamic linear model at each data point in the set of random data points using a Monte Carlo method;

calculating a forecast for sales of the low-selling SKUs based on the fitting at each data point in the set of random data points; and

ordering inventory based on the forecast for sales of the low-selling SKUs.

16. The at least one non-transitory storage module system of claim 15 wherein:

the set of random data points are chosen based on an expected value and covariance of the dynamic linear model.

17. The at least one non-transitory storage module system of claim 16 wherein:

the set of random data points comprises approximately 1,000 data points.

18. The at least one non-transitory storage module system of claim 15 wherein:

fitting the dynamic linear model at each data point in the set of random data points using the Monte Carlo method comprises using a Cholesky decomposition of an expected value matrix based on the dynamic linear model.

19. The at least one non-transitory storage module system of claim 15 wherein:

fitting the dynamic linear model at each point in the set of random data points comprises using a Kalman filter to fit the dynamic linear model by calculating a value of an integral at each data point in the set of random data points.

20. The at least one non-transitory storage module system of claim 15 wherein:

fitting the dynamic linear model at each data point in the set of random data points comprises using an unscented Kalman filter using the set of random data points in place of a set of sigma points.