Decision Support Model for Supply Chain Strategy
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Solution Overview
Problem
Users face challenges in selecting effective deployment strategies for product replacement projects due to a lack of sufficient information to balance long-term and short-term costs, which hinders consistent decision-making and efficient product lifecycle management.
Innovation Solution
A device and method that provide a user interface for inputting project variables, determining deployment strategies based on these variables, calculating end-to-end cost scores, and ranking strategies to identify the most cost-effective options for product replacement, upgrade, or redesign, considering geographical scope and timeframe.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple deployment strategies are analyzed comprehensively, then decision-making quality improves, but system complexity and analysis time increase
Solution Approach 1:
The patent segments the deployment strategy analysis into distinct modules: input module for project variables, processing module for generating multiple deployment strategies, evaluation module for calculating end-to-end costs, and output module for presenting ranked strategies. This modular segmentation allows comprehensive analysis while managing system complexity through organized functional breakdown.
Solution Approach 2:
The patent introduces an intermediary computational framework that acts as a mediator between raw project variables and final deployment decisions. This framework automatically processes variables through standardized algorithms to generate, evaluate, and rank deployment strategies, reducing the complexity burden on decision-makers while maintaining high decision-making quality.
2Ease of manufacture
If end-to-end cost analysis is performed for all deployment strategies, then cost-effectiveness improves, but computational time and resources increase
Solution Approach 1:
The patent performs preliminary filtering of deployment strategies based on key criteria before conducting full end-to-end cost analysis. By pre-screening strategies to identify those most likely to be cost-effective, the system reduces the number of strategies requiring comprehensive analysis, thereby maintaining cost-effectiveness while reducing computational time and resources.
3Measurement precision
If comprehensive project variables are collected, then strategy accuracy improves, but data collection complexity increases
Solution Approach 1:
The patent implements a universal data collection framework that handles multiple project variables through standardized input mechanisms. This multi-functional approach allows the same interface and processing logic to handle diverse variables (technical specifications, cost parameters, timeline constraints, etc.), improving strategy accuracy while reducing data collection complexity through consolidation.
Data Source
AI summary
A device may provide a user interface for receiving a set of project variables for a replacement project. The device may receive the set of project variables for the replacement project via the user interface. The replacement project may include a decision regarding replacing a product. The device may determine a set of deployment strategies associated with the replacement project based on the set of project variables. Each deployment strategy, in the set of deployment strategies, may be associated with a type of replacement, a timeframe for deployment of the type of replacement, and a geographical scope for deployment of the type of replacement. The device may determine a set of end-to-end cost scores for the set of deployment strategies. The device may provide information identifying a deployment strategy of the set of deployment strategies based on the set of end-to-end cost scores for the set of deployment strategies.