Supply Chain Network Similarity Using Complex Node Vectors
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Solution Overview
Problem
Conventional supply chain analysis methods fail to determine the similarity between companies in a network and do not facilitate evaluation on the boundary of company communities, and existing community breakdown techniques do not provide a nuanced index for company affiliation.
Innovation Solution
An information processing system and method that calculates the similarity between companies in a supply chain network by assigning complex vectors to nodes based on distance and flow amount, extracting subnetworks, and determining similarity using these vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional supply chain analysis methods are used, then basic network structure can be obtained, but the similarity between companies cannot be determined
Solution Approach 1:
The patent transforms the network analysis by introducing complex vectors as a new parameter representation. Each company node is assigned a complex vector where the phase angle represents the distance to the target node and the absolute value represents the flow amount. This parameter transformation enables precise similarity measurement between companies based on their positional and flow characteristics in the supply chain network.
Solution Approach 2:
The patent adds dimensional information to the network analysis by using complex vectors with both magnitude and phase components. This two-dimensional representation (absolute value and phase angle) captures both the flow amount and distance relationships, transforming the one-dimensional network structure into a multi-dimensional representation that enables similarity calculation.
2Measurement precision
If community breakdown technique is used, then binary community assignment can be obtained, but nuanced evaluation on company boundary affiliation is not facilitated
Solution Approach 1:
The patent replaces binary community assignment with continuous similarity measurement using complex vector dot products. The similarity value between two companies can take any value between 0 and 1, allowing nuanced evaluation of company affiliation strength. This parameter change enables differentiation between companies that are deeply embedded in the same community versus those on the boundary.
Solution Approach 2:
The patent applies local quality by calculating similarity between companies based on their specific complex vectors, which capture their individual positional and flow characteristics. Each company's similarity score is computed locally based on its unique vector representation, allowing differentiated evaluation of each company's community affiliation status rather than uniform binary classification.
3Measurement precision
If complex vectors with phase and absolute value are assigned to nodes, then similarity between companies can be calculated, but computational complexity increases
Solution Approach 1:
The patent substitutes complex computational vector processing with a mathematical approach using complex number theory. The similarity calculation is reduced to computing the absolute value of the dot product of two complex vectors, which can be efficiently calculated using standard complex arithmetic operations. This substitution simplifies the computational mechanism while maintaining precise similarity measurement.
Data Source
AI summary
An information processing system includes, a supply-chain-network obtaining unit configured to obtain a supply chain network in which a plurality of nodes each corresponding to one of a plurality of companies are connected together through links, a subnetwork extracting unit configured to extract a subnetwork including at least one of an upstream subnetwork and a downstream subnetwork, the upstream subnetwork including an upstream company that supplies the product to a company corresponding to a processing target node among the plurality of companies, the downstream subnetwork including a downstream company that is supplied with the product from the company corresponding to the processing target node, a vector obtaining unit configured to determine a complex vector representing the processing target node, and a similarity calculating unit configured to calculate a similarity between two of the plurality of nodes in accordance with the two complex vectors corresponding to the two nodes.