Supply Chain Scenario Planning With Reverse Network Traversal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing scenario planning solutions for supply chain management are inflexible, time-consuming, prone to errors, require expert knowledge, and are prohibitively expensive, lacking the ability to generate multiple risk and reward perspectives, practical user interfaces, and support timely decision-making.
Innovation Solution
A low-touch decision support framework using stochastic and heuristic techniques to iteratively refine input variables, combining simulation-optimization with a reverse network traversal approach to reduce computational costs and improve scenario planning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing scenario planning solutions are used, then scenario evaluation can be performed, but the process is time-consuming and requires numerous manual inputs across multiple screens
Solution Approach 1:
The system enables self-service scenario planning by allowing users to define scenarios through high-level business objectives and constraints without manual screen-by-screen configuration. The automated scenario generation engine creates multiple scenarios automatically based on user-defined parameters, eliminating the need for extensive manual inputs across multiple screens while maintaining planning accuracy
Solution Approach 2:
The patent replaces manual mechanical operations (typing, navigating screens, configuring parameters) with an automated computational system. The scenario generation engine uses algorithms to automatically create, evaluate, and compare scenarios based on business objectives, substituting manual configuration processes with automated computational logic that achieves the same outcomes faster and with fewer errors
2Reliability
If existing scenario planning solutions are used, then scenarios can be evaluated, but expert knowledge is required to set thresholds and parameters
Solution Approach 1:
The system provides self-service capability by allowing users to define scenarios through intuitive high-level parameters rather than requiring expert knowledge of underlying thresholds and computational parameters. Users can specify business objectives and constraints in plain language, and the system automatically translates these into accurate scenario evaluations without requiring user expertise in complex planning algorithms
Solution Approach 2:
The patent introduces an intermediary layer between user inputs and scenario evaluation. The automated scenario generation engine acts as a mediator that translates user-friendly high-level parameters into the detailed computational models needed for accurate evaluation. This intermediary layer hides the complexity from users while maintaining evaluation accuracy, eliminating the need for users to understand or configure complex thresholds and parameters
3Productivity
If cloud computing infrastructure is used to execute scenario planning solutions, then scenarios can be evaluated, but the cost becomes prohibitive
Solution Approach 1:
The patent applies parameter changes by adjusting computational approaches to reduce cloud computing costs. The system uses heuristic methods and selective simulation techniques that require fewer computational resources compared to traditional comprehensive scenario evaluation approaches. By changing the parameters of the evaluation process (using approximations, selective sampling, and optimized algorithms), the system maintains scenario evaluation capability while significantly reducing cloud computing expenses
4Adaptability or versatility
If existing scenario planning solutions are used, then single scenarios can be analyzed, but the ability to generate multiple risk and reward perspectives is limited
Solution Approach 1:
The system segments the scenario analysis process into distinct modular components: scenario generation module, risk analysis module, reward analysis module, and comparison module. Each module independently processes specific aspects of scenario evaluation, allowing the system to generate multiple risk and reward perspectives by running these segmented modules in parallel across different scenarios without proportionally increasing overall system complexity
Solution Approach 2:
The patent implements a universal scenario generation engine that can create multiple types of scenarios (base case, best case, worst case, alternative cases) and evaluate them across multiple dimensions (risk, reward, financial performance, operational metrics). This multi-functional approach allows the system to generate diverse perspectives from a single unified platform without requiring separate specialized systems for each analysis type
Data Source
AI summary
A system and method are disclosed for performing rough cut capacity planning. The method includes receiving supply chain transaction data as transaction tables, generating a base plan and generating updated transaction tables, denormalizing the base plan and the updated transaction tables, generating supply chain network flow paths and supply chain network data, solving a rough cut capacity planning problem based at least in part on the supply chain network flow paths, the supply chain network data and simulation data, repeating at least the generating and solving until business goals of the rough cut capacity planning meet a threshold, and updating the simulation data based on an upsert process. The method further includes relaxing supply chain network constraints, inverting the supply chain network, and traversing a perturbation in the supply chain network constraints as demands in a reverse direction towards customer nodes.