Image-based analytics of supply chain optimization problems

1. A system of image analysis of supply chain planning problems modeled as linear programing (LP) problems, comprising:

a computer, comprising a processor and memory, the computer configured to:

receive an LP matrix representing constraints and variables of at least a portion of a supply chain planning problem;

identify functions of the variables and the constraints;

color pixels of a supply chain problem image based, at least in part, on logic that maps a link between each pixel and a corresponding individual variable or constraint to determine a color of each pixel;

determine locations of the pixels on the supply chain problem image based, at least in part, on a mapping between each pixel and a column matrix location of a sorted variable index and a row matrix location of a sorted constraint index; and

display a visualization of the supply chain problem image based on the colored pixels.

2. The system of claim 1 , wherein a color that is applied to each pixel is based on the presence or absence of a variable in a particular constraint.

3. The system of claim 1 , wherein a pixel is not plotted when an absolute value of a row-column coefficient is less than a selected threshold value.

4. The system of claim 1 , wherein the computer is further configured to:

index the variables and constraints according to time-based indexing, LP-based indexing or time-bucket-variable indexing.

5. The system of claim 1 , wherein the computer is further configured to:

sort all variables prior to sorting all constraints; and

generate a sorted variable index and sorted constraint index.

6. The system of claim 1 , wherein the supply chain problem image comprises one or more of changes in consume yield, changes in produce yield, changes in material availability and changes in capacity constraints.

7. The system of claim 1 , wherein the supply chain problem image identifies supply chain components and connecting flows among them for a given supply chain structure.

8. A computer-implemented method of image analysis of supply chain planning problems modeled as linear programing (LP) problems, comprising:

receiving an LP matrix representing constraints and variables of at least a portion of a supply chain planning problem;

identifying functions of the variables and the constraints;

coloring pixels of a supply chain problem image based, at least in part, on logic that maps a link between each pixel and a corresponding individual variable or constraint to determine a color of each pixel;

determining locations of the pixels on the supply chain problem image based, at least in part, on a mapping between each pixel and a column matrix location of the sorted variable index and a row matrix location of the sorted constraint index; and

display a visualization of the supply chain problem image based on the colored pixels.

9. The computer-implemented method of claim 8 , wherein a color that is applied to each pixel is based on the presence or absence of a variable in a particular constraint.

10. The computer-implemented method of claim 8 , wherein a pixel is not plotted when an absolute value of a row-column coefficient is less than a selected threshold value.

11. The computer-implemented method of claim 8 , further comprising:

indexing, by the computer, the variables and constraints according to time-based indexing, LP-based indexing or time-bucket-variable indexing.

12. The computer-implemented method of claim 8 , further comprising:

sorting, by the computer, all variables prior to sorting all constraints; and

generating, by the computer, a sorted variable index and sorted constraint index.

13. The computer-implemented method of claim 8 , wherein the supply chain problem image comprises one or more of changes in consume yield, changes in produce yield, changes in material availability and changes in capacity constraints.

14. The computer-implemented method of claim 10 , wherein the supply chain problem image identifies supply chain components and connecting flows among them for a given supply chain structure.

15. A non-transitory computer-readable medium embodied with software providing image analysis of supply chain planning problems modeled as linear programing (LP) problems, the software when executed:

receives an LP matrix representing constraints and variables of at least a portion of a supply chain planning problem;

identifies functions of the variables and the constraints;

colors pixels of a supply chain problem image based, at least in part, on logic that maps a link between each pixel and a corresponding individual variable or constraint to determine a color of each pixel;

determines locations of the pixels on the supply chain problem image based, at least in part, on a mapping between each pixel and a column matrix location of the sorted variable index and a row matrix location of the sorted constraint index; and

display a visualization of the supply chain problem image based on the colored pixels.

16. The non-transitory computer-readable medium of claim 15 , wherein a color that is applied to each pixel is based on the presence or absence of a variable in a particular constraint.

17. The non-transitory computer-readable medium of claim 15 , wherein a pixel is not plotted when an absolute value of a row-column coefficient is less than a selected threshold value.

18. The non-transitory computer-readable medium of claim 15 , wherein the software is further configured to:

index the variables and constraints according to time-based indexing, LP-based indexing or time-bucket-variable indexing.

19. The non-transitory computer-readable medium of claim 15 , wherein the software is further configured to:

sort all variables prior to sorting all constraints; and

generate a sorted variable index and sorted constraint index.

20. The non-transitory computer-readable medium of claim 15 , wherein the supply chain problem image comprises one or more of changes in consume yield, changes in produce yield, changes in material availability and changes in capacity constraints.