Production and distribution supply chain optimization software

• US 7,627,493 B1
• Filed: 08/25/2000
• Issued: 12/01/2009
• Est. Priority Date: 08/25/2000
• Status: Expired due to Term

• Current Assignee: SCA Holdings
• Original Assignee: SCA Holdings
• Inventors: Visweswaran, Viswanathan, Sarthi, Samarth
• Primary Examiner(s): Frenel; Vanel
• Application Number:
  US09/648,861
• Time in Patent Office: 3,385 Days
• Field of Search: 705/7, 705/8, 705/30, 700/95, 700/99, 700/100
• US Class Current: 705/7.22
• 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 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

The present invention is directed to a method of modeling a complex system, the model produced by such a method, and a method of optimizing a complex system by optimizing such a model. The first step in the modeling method is to identify the activities that comprise the process. Measurable drivers for each activity are identified. The costs associated with each driver are identified as fixed and, where appropriate, variable components. Each activity is then represented as a function of its driver'"'"'s cost components. Relationships between the drivers that are relevant to more than one activity are derived. A function that represents the process is built using the derived relationships. The model (function) thus produced is an expression of the entire process in terms of variables that are drivers for more than one activity within the process. Optimizing the modeled process is accomplished by optimizing the model constructed as described above for certain selected objective(s). The model can be modified by changing constraints thus enabling the user to run through a large number of “what if” scenarios to determine what is the best solution under varying conditions.

Claims

1. A computer-implemented method of managing a process, said computer implemented method comprising:

identifying activities that comprise the process, wherein the process is a series of activities, wherein an input of at least one subsequent activity is dependent on an output of at least one previous activity;

identifying drivers associated with at least one metric, reflecting an efficiency of said process, for each of the activities;

identifying, in a computer, bridge variables from said identified drivers, wherein each bridge variable is a driver that is relevant to more than one of said activities;

establishing, in a computer, a relationship between various drivers by representing at least one non-bridge variable driver in terms of one or more of said bridge variables;

using said relationship, representing activities at least as a function of one or more of said bridge variables, thereby reflecting interdependence between said activities to represent the entire series of activities of said process, in a computer;

generating, in a computer, a model of said process at least as a function of said bridge variables by combining representations of activities comprising said process; and

outputting, from said model in a computer, a predictive metric reflecting an efficiency of the total process.

2. The computer-implemented method of claim 1, further comprising:

selecting a plurality of constraints,

wherein generating said model of said process includes generating said model as a function of said bridge variables and said plurality of constrains.

3. The computer-implemented method of claim 2, further comprising:

optimizing said model in view of said plurality of constraints using one of the following;

a linear programming algorithm,

a mixed-integer linear programming algorithm, and

a mixed-integer nonlinear programming algorithm; and

reconstructing a representation of said activities and said drivers using said optimized model.

4. The new computer-implemented method of claim 3, wherein said reconstructing includes calculating a value of at least one non-bridge variable driver using values of corresponding bridge variables, and calculating a value of activities using values calculated for bridge variable driver and non-bridge variable driver of respective activities.

5. The computer-implemented method of claim 3, further comprising:

revising said model using the results from said optimization step.

6. The computer-implemented method of claim 2, wherein selecting said plurality of constraints includes setting economic and non-economic constraints.

7. The computer-implemented method of claim 1, wherein identifying measurable drivers include identifying economic and non-economic drivers.

8. The computer-implemented method of claim 1, wherein identifying said drivers includes identifying at least one of fixed and variable components of each said driver, and wherein said method further comprising:

determining said metric of each said driver based on said at least one of fixed and variable components thereof.

9. A system comprising:

a computer;

input and output devices in communication with said computer; and

a memory encoded with a computer program, which, when executed by said computer, causes said computer to perform the following;

allow a user to identify activities that comprise a process, wherein the process is a series of activities, wherein an input of at least one subsequent activity is dependent on an output of at least one previous activity,

further allow said user to identify measurable drivers associated with a at least one metric, reflecting an efficiency of said process, for each of the activities;

identify bridge variables from said identified drivers, wherein each bridge variable is a driver that is relevant to more than one of said activities;

establish a relationship between various drivers by representing at least one non-bridge variable driver in terms of one or more said bridge variables;

using said relationship, represent activities at least as a function of one or more of said bridge variables, thereby reflecting interdependence between said activities to represent the entire series of activities of said process;

generate a model of said process at least as a function of said bridge variables by combining representations of activities comprising said process; and

output, from said model, a predictive metric reflecting an efficiency of the total process.

10. The system of claim 9, wherein said computer program, upon execution by said computer, causes said computer to further perform the following:

further allow said user to select a plurality of constraints;

incorporate said plurality of constraints in said model of said process;

optimize said model in view of said plurality of constraints using one of the following;

a linear programming algorithm,

a mixed-integer linear programming algorithm, and

a mixed-integer nonlinear programming algorithm; and

reconstruct a representation of said activities and said drivers using said optimized model.

11. The system of claim 10, wherein said computer program, upon execution by said computer, causes said computer to perform said reconstruction by calculating a value of at least one non-bridge variable driver using values of corresponding bridge variables and by calculating a value of activities using values calculated for bridge variable drivers and non-bridge variable driver of respective activities.

12. A computer-readable data storage medium containing program instructions, which, when executed by a processor, cause said processor to perform the following:

allow a user to identify activities that comprise a process, wherein the process is a series of activities, wherein an input of at least one subsequent activity is dependent on an output of at least one previous activity;

further allow said user to identify drivers associated with a metric reflecting an efficiency of said process for each of the activities;

identify bridge variables from said identified drivers, wherein each bridge variable is a driver that is relevant to more than one of said activities;

establish a relationship between various drivers by representing at least one non-bridge variable driver in terms of one or more of said bridge variables;

using said relationship, represent activities at least as a function of one or more of said bridge variables, thereby reflecting interdependence between said activities to represent the entire series of activities of said process;

generate a model of said process at least as a function of said bridge variables by combining representations of activities comprising said process; and

output, from said model, a predictive metric reflecting on efficiency of the total process.

13. The storage medium of claim 12, wherein said program instructions, upon execution, cause said processor to further perform the following:

further allow said user to select a plurality of constraints;

include said plurality of constraints in said model of said process; and

optimize said model in view of said plurality of constraints using one of the following;

a linear programming algorithm,

a mixed-integer linear programming algorithm, and

a mixed-integer nonlinear programming algorithm; and

revise said model using the results from optimizing said model.

14. The storage medium of claim 12, wherein said program instructions, upon execution, cause said processor to reconstruct a representation of said activities and said drivers by calculating a value of at least one non-bridge variable driver using values of corresponding bridge variables and by calculating a value of activities using values calculated for bridge variable driver and non-bridge variable driver of respective activities.

15. The storage medium of claim 12, wherein said program instructions, upon execution, cause said processor to cost each said driver identified by said user.

16. A computer-implemented method of managing a process, said computer implemented method comprising:

identifying activities that comprise the process, wherein the process is a series of activities, wherein an input of at least one subsequent activity is dependent on an output of at least one previous activity;

identifying drivers associated with at least one metric, reflecting an efficiency of said process, for each of the activities, wherein identifying said drivers includes identifying at least one of fixed components and variable components of each said driver;

determining, in a computer, a metric for each said driver based on said at least one of fixed components and variable components thereof;

generating, in a computer, a model of said process at least as a function of said drivers by combining representations of activities comprising said process; and

outputting, from said model in a computer, a predictive metric reflecting an efficiency of the total process.

17. A method of claim 16, wherein the fixed and variable components of each said driver are the fixed and variable costs of each said driver.