Systems and methods for meeting a service level at a probable minimum cost

• US 9,875,452 B2
• Filed: 12/19/2013
• Issued: 01/23/2018
• Est. Priority Date: 06/07/2004
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Giacobbe, Robert A., Derrick, R. Douglas
• Primary Examiner(s): ADE, OGER GARCIA
• Application Number:
  US14/134,514
• Publication Number:
  US 20140172493A1
• Time in Patent Office: 1,496 Days
• Field of Search: 705 28
• 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

Abstract

An inventory of service parts may be managed by assessing a company'"'"'s capability, optimizing the inventory and implementing a management program based on the assessment and optimization. Optimizing target stock levels for the inventory of parts may be accomplished by calculating an inventory baseline for understanding information about the currently held inventory; developing a service strategy for a set of segments; quantifying a service level for each of the segments; analyzing the segments and their service levels for identifying at least one logistically distinct business; assigning each of the segments to a “best-fit” planning model for indicating each segment'"'"'s deployment, replenishment, forecasting and review characteristics; identifying a probability distribution function for estimating a demand process of each of the segments; and calculating a target stock level for each segment.

Claims

1. A method comprising:

identifying, using at least one hardware processor and based on receiving baseline inventory information, a service strategy for assigning service levels to a plurality of segments,

the baseline inventory information reducing a collection time, and usage of the at least one hardware processor, associated with collecting information associated with a current inventory of service parts,

each service level being associated with the service parts,

each service level being associated with one of the plurality of segments,

each of the plurality of segments being associated with a different classification of the service parts,

identifying the service strategy including;

selecting a segmentation criteria to identify the plurality of segments, and

utilizing empirical data to quantify the service levels for the plurality of segments;

receiving, using the at least one hardware processor, information indicating an assignment of best-fit planning models to the plurality of segments,

each best-fit planning model being associated with one of the plurality of segments;

identifying, using the at least one hardware processor and based on receiving the information indicating the assignment of the best-fit planning models to the plurality of segments, a probability distribution function that matches a demand associated with the plurality of segments,

the probability distribution function representing the demand;

performing a simulation, using the at least one hardware processor and based on the best-fit planning models, to test the probability distribution function against historical usage data and to determine the service levels,

the service levels being determined based on the service strategy;

adjusting, using the at least one hardware processor, a deployment algorithm based on performing the simulation,

the deployment algorithm being used to calculate target stock levels for the plurality of segments;

selectively repeating, using the at least one hardware processor, identifying the probability distribution function, performing the simulation, and adjusting the deployment algorithm until the target stock levels cause the service levels to be satisfied;

providing for display, using the at least one hardware processor, the target stock levels for the plurality of segments when the target stock levels cause the service levels to be satisfied;

receiving, using the at least one hardware processor, information associated with a demand for a particular service part, of the service parts, and a variation in the demand for the particular service part;

determining, using the at least one hardware processor, an inventory plan for the particular service part based on the target stock levels, the demand for the particular service part, and the variation in the demand for the particular service part,

the inventory plan increasing savings, associated with inventory costs, for a supplier of the particular service part; and

providing for display, using the at least one hardware processor, the inventory plan for the particular service part.

2. The method of claim 1, where identifying the probability distribution function comprises:

identifying a first possible probability distribution function using a fitting test;

identifying a second possible probability distribution function using the fitting test; and

choosing to use the first possible probability distribution function or the second possible probability distribution function based on a deviation from expected outcomes.

3. The method of claim 1, where at least one of the service levels comprises a cycle service level.

4. The method of claim 1, where at least one of the service levels comprises a fill rate service level.

5. The method of claim 1, where at least one of the service levels comprises a ready rate service level.

6. The method of claim 1, further comprising:

selectively repeating identifying the probability distribution function, performing the simulation, and adjusting the deployment algorithm until an optimum service level, as a function of stock-out costs and carrying costs, is satisfied for each of the service levels.

7. The method of claim 1, further comprising:

accessing demand data associated with a plurality of stock keeping units;

generating a histogram to represent a distribution of demand based on the demand data;

performing a distribution fitting test based on the demand data; and

selecting the probability distribution function based on the histogram and the distribution fitting test.

8. The method of claim 7, where the plurality of stock keeping units represent a logistically distinct business.

9. The method of claim 1, further comprising:

classifying the probability distribution function according to a class of statistical distribution.

10. The method of claim 9, where adjusting the deployment algorithm based on performing the simulation comprises:

adjusting the deployment algorithm based on the class of statistical distribution.

11. A computer-based system comprising:

a memory to store instructions; and

a hardware processor to execute the instructions to;

identify, based on receiving baseline inventory information, a service strategy for assigning service levels to a plurality of segments,

the baseline inventory information reducing a collection time, and usage of the hardware processor, associated with collecting information associated with a current inventory of service parts,

each service level being associated with the service parts,

each service level being associated with one of the plurality of segments,

each of the plurality of segments being associated with a different classification of the service parts,

the service strategy being identified based on selecting a segmentation criteria to identify the plurality of segments, and utilizing empirical data to quantify the service levels for the plurality of segments;

receive information indicating an assignment of best-fit planning models to the plurality of segments,

each best-fit planning model being associated with one of the plurality of segments;

identify, based on receiving the information indicating the assignment of the best-fit planning models to the plurality of segments, a probability distribution function that matches a demand associated with the plurality of segments,

the probability distribution function representing the demand;

perform a simulation, based on the best-fit planning models, to test the probability distribution function against historical usage data and to determine the service levels,

the service levels being determined based on the service strategy;

adjust a deployment algorithm based on performing the simulation,

the deployment algorithm being used to calculate target stock levels for the plurality of segments;

selectively identify the probability distribution function, perform the simulation, and adjust the deployment algorithm repetitively until the target stock levels cause the service levels to be satisfied;

provide, for display, the target stock levels for the plurality of segments when the target stock levels cause the service levels to be satisfied;

receive, information associated with a demand for a particular service part, of the service parts, and a variation in the demand for the particular service part;

determine an inventory plan for the particular service part based on the target stock levels, the demand for the particular service part, and the variation in the demand for the particular service part,

the inventory plan increasing savings, associated with inventory costs, for a supplier of the particular service part; and

provide, for display, the inventory plan for the particular service part.

12. The computer-based system of claim 11, where the hardware processor, when identifying the probability distribution function, is to:

identify a first possible probability distribution function using a fitting test;

identify a second possible probability distribution function using the fitting test; and

choose to use the first possible probability distribution function or the second possible probability distribution function based on a deviation from expected outcomes.

13. The computer-based system of claim 11, where each of the service levels comprises a cycle service level.

14. The computer-based system of claim 11, where each of the service levels comprises a fill rate service level.

15. The computer-based system of claim 11, where each of the service levels comprises a ready rate service level.

16. The computer-based system of claim 11, where the hardware processor is further to:

selectively identify the probability distribution function, perform the simulation, and adjust the deployment algorithm repetitively until an optimum service level, as a function of stock-out costs and carrying costs, is satisfied for each of the service levels.

17. The computer-based system of claim 11, where the hardware processor is further to:

access demand data associated with a plurality of stock keeping units;

generate a histogram to represent a distribution of demand based on the demand data;

perform a distribution fitting test based on the demand data; and

select the probability distribution function based on the histogram and the distribution fitting test.

18. The computer-based system of claim 17, where the plurality of stock keeping units represent a logistically distinct business.

19. The computer-based system of claim 11, where the hardware processor is further to:

classify the probability distribution function according to a class of statistical distribution.

20. The computer-based system of claim 19, where the hardware processor, when adjusting the deployment algorithm based on performing the simulation, is to:

adjust the deployment algorithm based on the class of statistical distribution.