System and method of decoding supply chain signatures
A system and method for automated machine learning supply chain planning having a computer with a processor and memory and configured to receive a first supply chain network model having one or more material constraints for operations of a first supply chain network. Embodiments include transforming the first supply chain network model into a digital image, training an auto-encoder model to reduce the dimensionality of an input vector, and locating one or more items in the first supply chain network.
1. A computer-implemented method for automated machine learning supply chain planning, comprising:
receiving, by a computer comprising a processor and a memory, a first supply chain network model comprising one or more material constraints for operations of a first supply chain network;
transforming, by the computer, the first supply chain network model expressed as an integer array into a digital image;
training, by the computer, an auto-encoder model to reduce a dimensionality of an input vector corresponding to the digital image by iteratively adjusting weights of one or more layers of the auto-encoder model using a back propagation of error until the auto-encoder model reconstructs the digital image from a supply chain signature; and
locating, by the computer, one or more items in the first supply chain network based at least in part on an item location combination matrix.
2. The computer-implemented method of claim 1 , further comprising:
receiving, by the computer, a second supply chain data model, the second supply chain data model comprising one or more material constraints for operations of a second supply chain network; and
calculating, by the computer, a difference between the first supply chain network and the second supply chain network by comparing the supply chain signature of the first supply chain network with a supply chain signature of the second supply chain network using a t-Distributed Stochastic Neighbor Embedding method.
3. The computer-implemented method of claim 1 , wherein the supply chain signature comprises a vector space model having a lower dimensionality than the digital image, the vector space model indicating dynamic data and structure of one or more supply chain components.
4. The computer-implemented method of claim 1 , wherein one or more supply chain components comprises one or more of:
the one or more material constraints;
one or more capacity constraints;
lead times;
yield rates;
inventory levels;
safety stock; and
demand dates.
5. The computer-implemented method of claim 1 , wherein the supply chain signature comprises one or more transportation routes of a transportation network.
6. The computer-implemented method of claim 1 , wherein the supply chain signature comprises stocking locations of one or more warehouses.
7. The computer-implemented method of claim 1 , wherein the supply chain signature comprises a Hilbert space.
8. A system of automated machine learning supply chain planning, comprising:
a computer, comprising a processor and memory, the computer configured to:
receive a first supply chain network model comprising one or more material constraints for operations of a first supply chain network;
transform the first supply chain network model expressed as an integer array into a digital image;
train an auto-encoder model to reduce a dimensionality of an input vector corresponding to the digital image by iteratively adjusting weights of one or more layers of the auto-encoder model using a back propagation of error until the auto-encoder model reconstructs the digital image from a supply chain signature; and
locate one or more items in the first supply chain network based at least in part on an item location combination matrix.
9. The system of claim 8 , wherein the computer is further configured to:
receive a second supply chain data model, the second supply chain data model comprising one or more material constraints for operations of a second supply chain network; and
calculate a difference between the first supply chain network and the second supply chain network by comparing the supply chain signature of the first supply chain network with a supply chain signature of the second supply chain network using a t-Distributed Stochastic Neighbor Embedding method.
10. The system of claim 8 , wherein the supply chain signature comprises a vector space model having a lower dimensionality than the digital image, the vector space model indicating dynamic data and structure of one or more supply chain components.
11. The system of claim 8 , wherein one or more supply chain components comprises one or more of:
the one or more material constraints;
one or more capacity constraints;
lead times;
yield rates;
inventory levels;
safety stock; and
demand dates.
12. The system of claim 8 , wherein the supply chain signature comprises one or more transportation routes of a transportation network.
13. The system of claim 8 , wherein the supply chain signature comprises stocking locations of one or more warehouses.
14. The system of claim 8 , wherein the supply chain signature comprises a Hilbert space.
15. A non-transitory computer-readable medium embodied with software, the software when executed:
receives a first supply chain network model comprising one or more material constraints for operations of a first supply chain network;
transforms the first supply chain network model expressed as an integer array into a digital image;
trains an auto-encoder model to reduce a dimensionality of an input vector corresponding to the digital image by iteratively adjusting weights of one or more layers of the auto-encoder model using a back propagation of error until the auto-encoder model reconstructs the digital image from a supply chain signature; and
locates one or more items in the first supply chain network based at least in part on an item location combination matrix.
16. The non-transitory computer-readable medium of claim 15 , wherein the software when executed further:
receives a second supply chain data model, the second supply chain data model comprising one or more material constraints for operations of a second supply chain network; and
calculates a difference between the first supply chain network and the second supply chain network by comparing the supply chain signature of the first supply chain network with a supply chain signature of the second supply chain network using a t-Distributed Stochastic Neighbor Embedding method.
17. The non-transitory computer-readable medium of claim 15 , wherein the supply chain signature comprises a vector space model having a lower dimensionality than the digital image, the vector space model indicating dynamic data and structure of one or more supply chain components.
18. The non-transitory computer-readable medium of claim 15 , wherein one or more supply chain components comprises one or more of:
the one or more material constraints;
one or more capacity constraints;
lead times;
yield rates;
inventory levels;
safety stock; and
demand dates.
19. The non-transitory computer-readable medium of claim 15 , wherein the supply chain signature comprises one or more transportation routes of a transportation network.
20. The non-transitory computer-readable medium of claim 15 , wherein the supply chain signature comprises stocking locations of one or more warehouses.