Supply Chain Risk Visualization for Bayesian KPI Uncertainty
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Solution Overview
Problem
Existing supply chain planning systems struggle to effectively present large amounts of data related to input variability and risk, failing to account for individual tolerances, which hinders optimal decision-making.
Innovation Solution
A system and method that utilizes a risk management visualization system to generate risk profiles based on input variables, incorporating a Bayesian optimization process to model KPI responses and provide interactive visualizations for adjusting input values and risk ranges, while identifying key inputs influencing KPIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a supply chain plan is generated to maximize or minimize a business objective given input variables, then the optimal KPI is provided for given input values, but the system is unable to present large amounts of data related to input variability and risk
Solution Approach 1:
The patent segments the supply chain planning system into multiple functional modules: an optimization engine for KPI calculation, a risk analysis module for variability assessment, and a visualization system for data presentation. This segmentation allows each module to handle specific tasks efficiently, with the visualization system specifically designed to present large amounts of risk data in an accessible format through charts, graphs, and interactive displays.
Solution Approach 2:
The patent introduces an intermediary visualization layer between the optimization engine and the user. This intermediary translates complex numerical data about input variability and risk into intuitive visual representations, allowing users to comprehend and interact with large datasets without being overwhelmed by raw numbers.
2Ease of operation
If the system provides optimal KPI for given input values, then decision-making is supported, but the system is unable to present data with regard to individual tolerances for risk
Solution Approach 1:
The patent applies local quality by providing customized risk information tailored to individual users' tolerance levels. The system allows each user to set their own risk tolerance parameters and presents data specifically formatted to their needs, rather than providing a single uniform view. This is achieved through user profiles and personalized visualization settings that adapt the data presentation to individual preferences and risk appetites.
Solution Approach 2:
The patent implements dynamic data presentation where the system adapts its output based on user interactions and preferences. Users can dynamically adjust their risk tolerance settings, and the system responds by reconfiguring the visualization to reflect these changes. This dynamic approach allows the same underlying data to be presented in multiple ways suited to different individual needs.
3Reliability
If the system visualizes risk and probabilistic nature of inputs, then supply chain planning is optimized, but complex data processing and visualization requirements increase
Solution Approach 1:
The patent replaces complex mechanical data processing systems with computational algorithms and software-based visualization techniques. Instead of relying on cumbersome manual analysis and physical data representation methods, the system uses computer algorithms to process probabilistic data and generate visual representations automatically, significantly reducing the operational complexity despite the advanced processing requirements.
Solution Approach 2:
The patent designs a universal visualization platform that can handle multiple types of data representations simultaneously. The same system infrastructure supports various visualization modes (charts, graphs, tables, interactive displays) and can present different aspects of risk data depending on user needs. This multi-functionality consolidates what would otherwise require multiple separate systems into a single integrated platform.
Data Source
AI summary
A system and method for a risk management visualization system having a computer comprising a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process comprising an exploration phase and a learning phase, and display a visualization of the risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.