Supply Chain Design Visual Analytics for Cost Optimization
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Solution Overview
Problem
Conventional supply chain design and analysis tools are complex, reactive to historical data, and unable to anticipate future changes, making them ineffective in addressing volatility, uncertainty, and complexity in dynamic business environments, especially due to the need for prior knowledge of algorithms and limited customization.
Innovation Solution
The system and method for supply chain design and analysis involve obtaining supply chain data, performing value stream mapping, identifying flow constraints, determining decision parameters, generating experimental designs, and using visual analytics to optimize costs and inventory, allowing for intuitive decision-making without requiring expertise in complex algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional supply chain design tools are used, then analysis can be performed with historical data, but the tools are complex and require prior knowledge of algorithms
Solution Approach 1:
The patent introduces an intermediary processing layer that transforms complex supply chain data into simplified visual representations. This intermediary layer (visual analytics system) mediates between the complex analytical algorithms and the end-user, making the system usable without requiring expertise in the underlying complex algorithms while maintaining analytical accuracy.
2Loss of information
If conventional supply chain tools are used, then historical data can be analyzed, but they cannot anticipate future changes or handle volatility
Solution Approach 1:
The system performs preliminary actions by pre-processing and visualizing supply chain data in advance, creating ready-to-analyze visual representations that can quickly adapt to new scenarios. This allows the system to maintain historical data analysis capabilities while being prepared to anticipate and respond to future changes through pre-established visual analytics frameworks.
Solution Approach 2:
The patent implements dynamics by creating a flexible visual analytics system that can adapt its analysis approaches and visualizations based on changing market conditions and volatility. The system transitions from static historical analysis to dynamic, adaptive analysis that responds to new information and future scenarios.
3Productivity
If detailed algorithmic analysis is performed, then supply chain optimization can be achieved, but decision-making time increases
Solution Approach 1:
The patent creates simplified visual copies and representations of complex supply chain data and relationships. These visual copies maintain the essential information needed for optimization decisions while being much faster to process and interpret than the underlying detailed algorithms, thus reducing decision-making time while preserving optimization capability.
4Ease of manufacture
If conventional tools are used, then standard analysis can be performed, but customization for variable market conditions is limited
Solution Approach 1:
The visual analytics system is designed with universality to perform multiple functions - it can handle standard supply chain analysis as well as customized analysis for variable market conditions. The same visual analytics platform adapts to different analysis needs through configurable parameters and visual representations, eliminating the need for separate specialized tools while maintaining ease of use.
Data Source
AI summary
Systems and methods for supply chain design and analysis to optimize costs associated with a supply chain are described. According to an embodiment, the supply chain management system comprises a data extraction module, an analysis module, and a presentation module coupled to a processor. The data extraction module obtains supply chain data from one or more data sources. The analysis module analyzes a plurality of parameters and at least one future state map to ascertain at least one business scenario. Further, the analysis module identifies flow constraints in the at least one business scenario based on a flow analysis. Further, the analysis module selects decision parameters from amongst the plurality of parameters based on the flow constraints and a simulation feedback. Further, the analysis module simulates at least one experimental design based on the decision parameters. Furthermore, the presentation module generates, a plurality of maps based on visual analytics.