Inventory early warning agent with correction by error correlation calculation

• US 20040186794A1
• Filed: 07/31/2002
• Published: 09/23/2004
• Est. Priority Date: 11/14/2001
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Chen, Ye, Renz, Alexander
• Granted Patent: US 7,222,786 B2
• Time in Patent Office: Days
• US Class Current: 705/28
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

An inventory agent operating on software includes instructions operable to cause a programmable processor to receive inventory data relating to stock in an inventory. The inventory data includes planned inventory data and actual inventory data. The instructions are operable to cause the programmable processor to use the inventory data to calculate one or more correlations in errors between the planned inventory data and the actual inventory data, use the one or more correlations in errors to estimate a probability density for one or more errors, calculate a predicted inventory level, calculate a likely error of the predicted inventory level using the estimate of the probability density, and adjust the predicted inventory level with the likely error to estimate a likely actual inventory level. The inventory agent may be implemented in a supply chain management system.

Claims

1. An inventory agent operating on software comprising instructions operable to cause a programmable processor to:

receive inventory data relating to stock in an inventory, wherein the data comprises planned inventory data and actual inventory data;

use the inventory data to calculate one or more correlations in errors between the planned inventory data and the actual inventory data;

use the one or more correlations to estimate a probability density for one or more errors;

calculate a predicted inventory level;

calculate a likely error of the predicted inventory level using the estimate of the probability density to; and

adjust the predicted inventory level with the likely error to estimate a likely actual inventory level.

2. The inventory agent of claim 1, further comprising instructions for defining confidence intervals around the likely actual inventory level.

3. The inventory agent of claim 2, wherein the confidence intervals include a high bound and a low bound.

4. The inventory agent of claim 3, wherein the probability density is the midpoint of the confidence intervals.

5. The inventory agent of claim 1, further comprising instructions to calculate an upside 10% confidence bound and a downside 10% confidence bound.

6. The inventory agent of claim 5, further comprising instructions to use the calculated upside 10% confidence bound and the downside 10% confidence bound in determining whether to order additional stock for the inventory.

7. The inventory agent of claim 1, wherein the instructions to calculate a predicted inventory level further comprise instructions for processing the inventory data with a conditional probabilistic predictive statistical algorithm.

8. The inventory agent of claim 7, wherein the statistical algorithm uses a conditional probabilistic model to process the data.

9. The inventory agent of claim 1, further comprising instructions for using the likely actual inventory level to determine whether to order additional stock for inventory.

10. The inventory agent of claim 1, wherein the inventory data is generated using a radio-frequency identification device.

11. The inventory agent of claim 1, wherein the inventory data is generated upon the sale of the stock in the inventory.

12. The inventory agent of claim 1, wherein the inventory data is generated upon the transfer of the stock in the inventory.

13. The inventory agent of claim 8, wherein the conditional probabilistic model uses a conditional Gaussian approximation.

14. The inventory agent of claim 13, wherein the conditional probabilistic model uses historical inventory data and current inventory data.

15. The inventory agent of claim 7, wherein the inventory data applied to the conditional probabilistic predictive statistical algorithm includes actual cumulative replenishment, forecast cumulative replenishment, actual cumulative consumption, and forecast cumulative consumption data.

16. The inventory agent of claim 1, further comprising instructions operable to cause the inventory agent to order a replenishment of the inventory.

17. The inventory agent of claim 1, further comprising instructions operable to record data related to inventory consumption and replenishment.

18. The inventory agent of claim 17, further comprising instructions operable to use the recorded data to calculate cumulative forecast consumption, cumulative forecast replenishment, cumulative actual consumption, and cumulative actual replenishment.

19. The inventory agent of claim 17, further comprising instructions operable to use the recorded data to calculate a second planned inventory and a second actual inventory.

20. The inventory agent of claim 19, further comprising instructions operable to use the inventory data to calculate one or more correlations in errors between the second planned inventory data and the second actual inventory data;

and

use the one or more correlations in errors to estimate a second probability density for one or more errors.

21. The inventory agent of claim 20, further comprising instructions operable to:

use the inventory data with the calculated cumulative forecast consumption, cumulative forecast replenishment, cumulative actual consumption, and cumulative actual replenishment to calculate a second predicted inventory level;

calculate a second predicted inventory level;

calculate a likely error of the second predicted inventory level using the estimate of the probability density; and

adjust the second predicted inventory level with the likely error to estimate a second likely actual inventory level.

22. The inventory agent of claim 21, wherein the instructions to calculate a second predicted inventory level further comprise processing the inventory data with a conditional probabilistic predictive statistical algorithm.

23. The inventory agent of claim 22, wherein the statistical algorithm uses a conditional probabilistic model to process the data.

24. The inventory agent of claim 1, wherein the inventory agent is implemented in a supply chain management system.

25. A system comprising one or more computer systems and an inventory agent computer coupled to the computer systems over a network, the inventory agent computer being operable to:

receive inventory data relating to stock in an inventory, wherein the data comprises planned inventory data and actual inventory data;

use the inventory data to calculate one or more correlations in errors between the planned inventory data and the actual inventory data;

use the one or more correlations to estimate a probability density for one or more errors;

calculate a predicted inventory level;

calculate a likely error of the predicted inventory level using the estimate of the probability density; and

adjust the predicted inventory level with the likely error to estimate a likely actual inventory level.

26. The system of claim 25, further comprising instructions for defining confidence intervals around the likely actual inventory level.

27. The system of claim 26, wherein the confidence intervals include a high bound and a low bound.

28. The system of claim 27, wherein the probability density is the midpoint of the confidence intervals.

29. The system of claim 25, further comprising instructions to calculate an upside 10% confidence bound and a downside 10% confidence bound.

30. The system of claim 29, further comprising instructions to use the calculated upside 10% confidence bound and the downside 10% confidence bound in determining whether to order additional stock for the inventory.

31. The system of claim 25, wherein the instructions to calculate a predicted inventory level further comprise instructions for processing the inventory data with a conditional probabilistic predictive statistical algorithm.

32. The system of claim 31, wherein the statistical algorithm uses a conditional probabilistic model to process the data.

33. The system of claim 25, further comprising instructions for using the likely actual inventory level to determine whether to order additional stock for inventory.

34. The system of claim 25, further comprising instructions for using the probability density to define confidence intervals around the likely actual inventory level.

35. The system of claim 25, further comprising instructions to generate the inventory data upon receipt from a radio-frequency identification device.

36. The system of claim 25, further comprising instructions to generate the inventory data upon the sale of the stock in the inventory.

37. The system of claim 25, further comprising instructions to generate the inventory data upon the transfer of the stock in the inventory.

38. The system of claim 31, wherein the conditional probabilistic model uses a conditional Gaussian approximation.

39. The system of claim 38, further comprising instructions to use historical inventory data and current inventory data in the conditional probabilistic model.

40. The system of claim 39, wherein the inventory data applied to the conditional probabilistic predictive statistical algorithm includes actual cumulative replenishment, forecast cumulative replenishment, actual cumulative consumption, and forecast cumulative consumption data.

41. The system of claim 25, further comprising instructions operable to cause the inventory agent computer to order a replenishment of the inventory.

42. The system of claim 25, further comprising instructions operable to record data related to inventory consumption and replenishment.

43. The system of claim 42, further comprising instructions operable to use the recorded data to calculate cumulative forecast consumption, cumulative forecast replenishment, cumulative actual consumption, and cumulative actual replenishment.

44. The system of claim 42, further comprising instructions operable to use the recorded data to calculate a second planned inventory and a second actual inventory.

45. The system of claim 44, further comprising instructions operable to use the inventory data to calculate one or more correlations in errors between the second planned inventory data and the second actual inventory data;

and

use the one or more correlations in errors to estimate a second probability density for one or more errors.

46. The system of claim 45, further comprising instructions operable to:

use the inventory data with the calculated cumulative forecast consumption, cumulative forecast replenishment, cumulative actual consumption, and cumulative actual replenishment to calculate a second predicted inventory level;

calculate a second predicted inventory level;

calculate a likely error of the second predicted inventory level using the estimate of the probability density; and

adjust the second predicted inventory level with the likely error to estimate a second likely actual inventory level.

47. The system of claim 46, wherein the instructions to calculate a second predicted inventory level further comprise processing the inventory data with a conditional probabilistic predictive statistical algorithm.

48. The system of claim 47, wherein the statistical algorithm uses a conditional probabilistic model to process the data.

49. The system of claim 25, wherein the inventory agent computer is implemented in a supply chain management system.