ASSET FORECASTING IN ASSET INTENSIVE ENTERPRISES

• US 20140278713A1
• Filed: 03/14/2014
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: ZIVELIN, Nadav, Lee, Moshin, Feldman, Bart, Subramanian, Balakrishnan, Fedorov, Vladimir, Bell, Margaret Laureen
• Application Number:
  US14/213,516
• Publication Number:
  US 20140278713A1
• Time in Patent Office: Days
• US Class Current: 705/7.25
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

A method, system, and computer program product for asset service and maintenance lifecycle management and supply chain planning. Some embodiments commence upon receiving a database record corresponding to an individually identified asset to be individually tracked through a corresponding asset lifecycle. Each individually identified asset has an asset-specific scheduled maintenance plan. During the performance of activities pertaining to the asset-specific scheduled maintenance plan, observations are made and events are recorded to generate a series of observations that are in turn collected into a learning model. The learning model and a predictor based on the learning model is used to predict a future demand or a forecast for items in quantities that are not given in the asset-specific scheduled maintenance plan. In exemplary cases, the forecast comprises items and/or quantities that are not given in the scheduled maintenance plan.

Claims

1. A method comprising:

using a computing system having at least one processor to perform a process, the process comprising;

receiving a database record corresponding to an individually identified asset to be individually tracked through a corresponding asset lifecycle;

receiving an asset-specific scheduled maintenance plan for the individually identified asset;

recording events pertaining to the individually identified asset over at least a portion of the asset-specific scheduled maintenance plan;

storing a series of recorded events pertaining to performance of the asset-specific scheduled maintenance plan of the individually identified asset into a learning model; and

using the learning model to predict a future demand, wherein the predicted future demand comprises a forecast for items in quantities that are not given in the asset-specific scheduled maintenance plan for the individually identified asset.

2. The method of claim 1, wherein the forecast for items comprises items that are not given in the asset-specific scheduled maintenance plan for the individually identified asset.

3. The method of claim 1, wherein the forecast for the quantities are less than quantities given in the asset-specific scheduled maintenance plan for the individually identified asset.

4. The method of claim 1, wherein the forecast for the quantities are greater than quantities given in the asset-specific scheduled maintenance plan for the individually identified asset.

5. The method of claim 1, further comprising using the learning model to predict a future demand, wherein the predicted future demand comprises a maintenance event for a maintenance event that is not given in the asset-specific scheduled maintenance plan for the individually identified asset.

6. The method of claim 1, wherein the asset-specific scheduled maintenance plan for the individually identified asset comprises at least one maintenance work order.

7. The method of claim 1, wherein the learning model is trained using observations retrieved from one or more history of maintenance work records.

8. The method of claim 1, wherein the learning model is trained using observations retrieved from a work in process dataset.

9. The method of claim 1, wherein the portion of the corresponding asset lifecycle comprises at least two maintenance cycles.

10. The method of claim 1, wherein the series of recorded events pertaining to the individually identified asset comprises events pertaining to operating conditions within a repair depot.

11. A computer program product embodied in a non-transitory computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process, the process comprising:

receiving a database record corresponding to an individually identified asset to be individually tracked through a corresponding asset lifecycle;

receiving an asset-specific scheduled maintenance plan for the individually identified asset;

recording events pertaining to the individually identified asset over at least a portion of the asset-specific scheduled maintenance plan;

storing a series of recorded events pertaining to performance of the asset-specific scheduled maintenance plan of the individually identified asset into a learning model; and

using the learning model to predict a future demand, wherein the predicted future demand comprises a forecast for items in quantities that are not given in the asset-specific scheduled maintenance plan for the individually identified asset.

12. The computer program product of claim 11, wherein the forecast for items comprises items that are not given in the asset-specific scheduled maintenance plan for the individually identified asset.

13. The computer program product of claim 11, wherein the forecast for the quantities are less than quantities given in the asset-specific scheduled maintenance plan for the individually identified asset.

14. The computer program product of claim 11, wherein the forecast for the quantities are greater than quantities given in the asset-specific scheduled maintenance plan for the individually identified asset.

15. The computer program product of claim 11, further comprising instructions for using the learning model to predict a future demand, wherein the predicted future demand comprises a maintenance event for a maintenance event that is not given in the asset-specific scheduled maintenance plan for the individually identified asset.

16. The computer program product of claim 11, wherein the asset-specific scheduled maintenance plan for the individually identified asset comprises at least one maintenance work order.

17. The computer program product of claim 11, wherein the learning model is trained using observations retrieved from one or more history of maintenance work records.

18. The computer program product of claim 11, wherein the learning model is trained using observations retrieved from a work in process dataset.

19. A system comprising:

a supply chain planning module to receive a database record corresponding to an individually identified asset to be individually tracked through a corresponding asset lifecycle;

a maintenance operations module to receive an asset-specific scheduled maintenance plan for the individually identified asset;

a demand management module to record events pertaining to the individually identified asset over at least a portion of the asset-specific scheduled maintenance plan, and to store a series of recorded events pertaining to performance of the asset-specific scheduled maintenance plan of the individually identified asset into a learning model; and

a predictor to predict a future demand, wherein the predicted future demand comprises a forecast for items in quantities that are not given in the asset-specific scheduled maintenance plan for the individually identified asset.

20. The system of claim 19, wherein the forecast for items comprises items that are not given in the asset-specific scheduled maintenance plan for the individually identified asset.