Decision Scenario Optimization System for Supply Chain Cost
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Solution Overview
Problem
Conventional systems for managing decision scenarios in supply chain performance are cumbersome and time-consuming, making it difficult to apply them to tactical opportunities due to the lack of advanced technology for quick updates and integration of changing inputs, and they require significant expert involvement and lack tracking and utilization of learning from prior scenario runs.
Innovation Solution
A processor-implemented method and system that utilize optimization and simulation techniques, along with a Neural Network AutoRegressive with exogenous input (NNARX) model, to analyze and optimize supply chain performance by selecting optimal decision scenarios, creating ordering schedules, and modifying inputs and constraints to reach target sourcing costs, facilitating rapid decision-making and tracking of scenario outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional scenario analysis tools are used for strategic planning, then decision-making accuracy is improved, but time consumption and system complexity increase significantly
Solution Approach 1:
The patent segments the scenario analysis process into distinct modules: data input module, scenario generation module, optimization module, and result analysis module. Each module handles specific tasks independently, allowing parallel processing and reducing overall time consumption while maintaining analytical depth and accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-defining multiple scenario templates with common constraints and parameters before actual decision-making occurs. These pre-configured scenarios can be quickly activated and customized, eliminating the need to build scenarios from scratch and significantly reducing analysis time while preserving strategic depth.
2Stability of the object's composition
If conventional scenario tools are used with pre-structured data, then data consistency is improved, but system complexity and expert involvement requirements increase
Solution Approach 1:
The system implements self-service capabilities through automated data validation rules, constraint verification mechanisms, and consistency checking algorithms that operate without expert intervention. The system automatically detects and corrects data inconsistencies, manages scenario constraints, and validates results, reducing both system complexity and dependency on expert users while maintaining data consistency.
3Adaptability or versatility
If conventional scenario analysis is performed manually, then scenario customization flexibility is improved, but productivity and automation level decrease
Solution Approach 1:
The system employs dynamic scenario configuration where constraints, parameters, and objectives can be modified in real-time without redefining the entire scenario structure. The optimization algorithms dynamically adjust to changing inputs and automatically recalculate results, providing both high customization flexibility and automated productivity. Users can interactively modify scenario parameters and immediately view updated outcomes, combining flexibility with rapid processing.
4Measurement precision
If comprehensive scenario analysis is performed with multiple constraints, then decision quality is improved, but computational complexity and resource requirements increase
Solution Approach 1:
The patent replaces complex manual mechanical analysis processes with automated computational algorithms including linear programming, simulation models, and optimization techniques. These algorithmic systems handle multiple constraints simultaneously through mathematical formulations, providing high decision quality while managing computational complexity through efficient algorithms and automated processing rather than manual analysis.
Data Source
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AI summary
Organizations/manufacturers have used scenarios to make business decisions. It has been difficult to apply scenarios in dealing with tactical opportunities due to lack of integration of changing inputs for consistent decision making. Conventionally, tools are cumbersome and depend on pre-structured and individually validated data requiring significant expert involvement. Present disclosure manages decision scenarios and optimizes total sourcing cost by obtaining various inputs and retrieving decision scenarios from a database. An optimization technique and/or a simulation technique is performed on the decision scenarios to obtain the total sourcing cost that is based on a quantity filled for each source-entity-destination combination and a corresponding unit lane cost. A decision scenario is selected from the pre-defined decision scenarios based on the total sourcing cost and an ordering schedule for associated demands is created accordingly. The selected decision scenario is further fine-tuned such that the total sourcing cost reaches close to a target cost.