Automatic cost generator for use with an automated supply chain optimizer

• US 20070143131A1
• Filed: 11/27/2006
• Published: 06/21/2007
• Est. Priority Date: 12/01/2005
• Status: Abandoned Application

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: Braun, Heinrich, Goebelt, Mathias, Kasper, Thomas, Schlueter, Frank
• Application Number:
  US11/605,046
• Publication Number:
  US 20070143131A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

An automatic cost generation apparatus is provided for automatically converting user supplied definitions/requirements into cost parameters for use by a cost-based supply chain optimizer. In one example, this is achieved by generating a linear programming model that incorporates the requirements/definitions as a set of linear constraints. The linear programming model is automatically solved so as to yield a cost model, from which costs are extracted for use by the cost-based optimizer. By first formulating requirements/definitions as linear constraints within a linear programming model, the solution to the model therefore yields a cost model that satisfies the constraints, i.e. a cost model that satisfies the requirements. Hence, requirements/definitions initially provided by the user are thereby automatically converted into a cost model that incorporates costs that can be used during supply chain optimization. The user can therefore use the cost-based optimizer without first having to try to determine the various costs that correspond to his or her business requirements, which can be difficult.

Claims

1. A method for use with an automated supply chain optimizer that optimizes a supply chain based on costs, the method automatically converting non-cost-based definitions into costs for use by the optimizer, the method comprising:

providing a set of non-cost-based definitions;

generating a linear programming model that incorporates the definitions as a set of linear constraints;

solving the linear programming model to yield a cost model; and

extracting costs from the cost model for use with the optimizer.

2. The method of claim 1, wherein the linear programming model is configured to include one or more of:

production cost variables (LO), transportation cost variables (LT), procurement cost variables (LP), safety stock penalty variables (LC), storage cost variables (LS), late delivery cost variables (LL), non-delivery cost variables (NLP), maximum location-product supply chain cost (LN) variables, maximum slack supply chain cost (LNS) variables and minimum location-product supply chain cost (LM) variables.

3. The method of claim 2, wherein solving the linear programming model to yield a cost model includes:

solving the linear programming model by maximizing a sum over all minimum location-product supply chain cost (LM) variables subject to the set of linear constraints to provide an initial set of values for all cost variables;

modifying the linear programming model by setting storage cost (LS) variables based on the initial set of values;

solving the linear programming model by minimizing a sum over all location-product supply chain cost (LN) variables plus a sum over all maximum slack supply chain cost (LNS) variables having a high coefficient subject to the set of linear constraints to provide a set of values of all cost variables representative of the cost model to be used with the optimizer.

4. The method of claim 2, wherein extracting costs from the cost model for use with the optimizer includes extracting costs from the cost variables of the cost model.

5. The method of claim 2, further including determining late delivery and non-delivery costs based on demand priorities and location-product priorities.

6. The method of claim 2, wherein the set of constraints further include one or more of:

production or transportation costs are generated only in response to a predetermined demand; and

non-delivery penalties are high enough to trigger production if there is a demand.

7. The method of claim 1, further comprising:

providing cost master data;

determining if an automatic generation of the cost model is requested or if else the cost master data are to be used by the optimizer; and

ignoring the cost master data and generating the cost model immediately before calling the optimizer if the automatic generation of the cost model is requested.

8. The method of claim 1, further comprising, controlling a bandwidth between the highest cost and the lowest cost in the cost model when generating the cost model.

9. The method of claim 8, wherein controlling the bandwidth comprises:

maintaining that bandwidth such that a bandwidth threshold is not exceeded.

10. The method claim 1, wherein providing a set of non-cost-based definitions comprises:

inputting business requirements including one or more of;

demand priorities;

safety stock priorities;

product priorities;

production priorities;

transport priorities; and

product values.

11. The method of claim 1, wherein extracting costs from the cost model comprises:

extracting one of more of;

non-delivery penalty costs;

late delivery penalty costs;

safety stock penalties;

storage costs;

production costs;

product-specific transport costs; and

procurement costs.

13. The machine-accessible medium of claim 7, further comprising instructions causing the machine to:

provide cost master data;

determine if an automatic generation of the cost model is requested or if else the cost master data are to be used by the optimizer; and

ignore the cost master data and generating the cost model immediately before calling the optimizer if the automatic generation of the cost model is requested.

12. A machine-accessible medium containing instructions that when executed cause a machine to:

provide a set of non-cost-based definitions;

generate a linear programming model that incorporates the definitions as a set of linear constraints;

solve the linear programming model to yield a cost model; and

extract costs from the cost model for use with the optimizer.

14. An automatic cost generation apparatus for use with a supply chain optimizer that optimizes a supply chain based on costs, the apparatus automatically converting non-cost-based definitions into costs for use by the optimizer, the apparatus comprising:

a definitions unit operative to provide a set of non-cost-based definitions;

a model generation unit operative to generate a linear programming model that incorporates the definitions as a set of linear constraints;

a linear programming model solution unit operative to solve the linear programming model to yield a cost model; and

a cost extraction unit operative to extract costs from the cost model for use with the optimizer.