Method And System For Orders Planning And Optimization With Applications To Food Consumer Products Industry

• US 20140058794A1
• Filed: 08/27/2012
• Published: 02/27/2014
• Est. Priority Date: 08/27/2012
• Status: Abandoned Application

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Malov, Denis, Ayres De Castro, Gustavo
• Application Number:
  US13/595,625
• Publication Number:
  US 20140058794A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 10/083 Shipping
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

A system, a computer program product, and a method for order planning and optimization are disclosed. A first data is received, where the first data represents historical shipment data of an item from a distributor to a location. The received first data is processed and a model for at least one shipping pattern of the item from the distributor to the location is determined based on the processed received first data. A forecast for a future shipping demand of the item by the location is generated based on the determined model. At least one shipping pattern of the item from the distributor to the location is optimized based on the generated forecast.

Claims

1. A computer-implemented method, comprising:

receiving a first data, the first data representing historical shipment data of an item from a distributor to a location;

processing the received first data and determining, based on the processed received first data, a model for at least one shipping pattern of the item from the distributor to the location;

generating, based on the determined model, a forecast for a future shipping demand of the item by the location; and

optimizing, based on the generated forecast, the at least one shipping pattern of the item from the distributor to the location, the at least one shipping pattern optimized based on a plurality of control parameters including at least an out of stock event probability and a safety stock value, the optimizing performed using the plurality of control parameters for the item at the location during a predetermined period of time,

wherein the receiving, the processing, the determining, the generating, and the optimizing are performed on at least one processor.

2. The method according to claim 1, wherein the first data includes at least one of the following:

a historical point-of-sale data of the item, an inventory of the item at the location, a return data representing returns of the item from the location, and at least one business rule concerning shipment of the item from the distributor to the location.

3. The method according to claim 1, wherein the model is determined based on at least one of the following:

a foot traffic at the location, price sensitivity of the item at the location, at least one promotion with regard to the item as determined at the location, a seasonality of the item at the location, a substitution policy of the item at the location, a competitor of the location information with regard to the item, and at least one calendar at the location.

4. The method according to claim 1, wherein the forecast is generated for a predetermined period of time.

5. The method according to claim 1, wherein the optimizing of the at least one shipping pattern of the item further comprises optimizing the at least one shipping pattern based on at least one unforeseen event.

6. The method according to claim 1, wherein the future shipping demand is determined based on a simulation of at least one sale of the item at the location.

7. The method according to claim 6, further comprising:

determining, based on the simulation, a starting date for shipping of the item to the location.

8. A computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:

receiving a first data, the first data representing historical shipment data of an item from a distributor to a location;

processing the received first data and determining, based on the processed received first data, a model for at least one shipping pattern of the item from the distributor to the location;

generating, based on the determined model, a forecast for a future shipping demand of the item by the location; and

optimizing, based on the generated forecast, the at least one shipping pattern of the item from the distributor to the location, the at least one shipping pattern optimized based on a plurality of control parameters including at least an out of stock event probability and a safety stock value, the optimizing performed using the plurality of control parameters for the item at the location during a predetermined period of time.

9. The computer program product according to claim 8, wherein the first data includes at least one of the following:

a historical point-of-sale data of the item, an inventory of the item at the location, a customer purchasing pattern with regard to the item, a return data representing returns of the item from the location, and at least one business rule concerning shipment of the item from the distributor to the location.

10. The computer program product according to claim 8, wherein the model is determined based on at least one of the following:

a foot traffic at the location, price sensitivity of the item at the location, at least one promotion with regard to the item as determined at the location, a seasonality of the item at the location, a substitution policy of the item at the location, a competitor of the location information with regard to the item, and at least one calendar at the location.

11. The computer program product according to claim 8, wherein the forecast is generated for a predetermined period of time.

12. The computer program product according to claim 8, wherein the optimizing of the at least one shipping pattern of the item further comprises optimizing the at least one shipping pattern based on at least one unforeseen event.

13. The computer program product according to claim 8, wherein the future shipping demand is determined based on a simulation of at least one sale of the item at the location.

14. The computer program product according to claim 13, wherein the operations further comprise:

determining, based on the simulation, a starting date for shipping of the item to the location.

15. A system comprising:

at least one programmable processor; and

a machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising;

receiving a first data, the first data representing historical shipment data of an item from a distributor to a location;

processing the received first data and determining, based on the processed received first data, a model for at least one shipping pattern of the item from the distributor to the location;

generating, based on the determined model, a forecast for a future shipping demand of the item by the location; and

optimizing, based on the generated forecast, the at least one shipping pattern of the item from the distributor to the location, the at least one shipping pattern optimized based on a plurality of control parameters including at least an out of stock event probability and a safety stock value, the optimizing performed using the plurality of control parameters for the item at the location during a predetermined period of time.

16. The system according to claim 15, wherein the first data includes at least one of the following:

a historical point-of-sale data of the item, an inventory of the item at the location, a customer purchasing pattern with regard to the item, a return data representing returns of the item from the location, and at least one business rule concerning shipment of the item from the distributor to the location.

17. The system according to claim 15, wherein the model is determined based on at least one of the following:

a foot traffic at the location, price sensitivity of the item at the location, at least one promotion with regard to the item as determined at the location, a seasonality of the item at the location, a substitution policy of the item at the location, a competitor of the location information with regard to the item, and at least one calendar at the location.

18. The system according to claim 15, wherein the forecast is generated for a predetermined period of time.

19. The system according to claim 15, wherein the optimizing of the at least one shipping pattern of the item further comprises optimizing the at least one shipping pattern based on at least one unforeseen event.

20. The system according to claim 15, wherein the future shipping demand is determined based on a simulation of at least one sale of the item at the location, and

wherein a starting date for shipping of the item to the location is determined based on the simulation.