Supply Chain Scenario Planning via Network Inversion
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Solution Overview
Problem
Existing scenario planning solutions lack the ability to quickly generate multiple risk and/or reward perspectives, require manual inputs across multiple screens, are prone to errors, need expert knowledge, are inflexible, and can be prohibitively expensive, making them undesirable for timely supply chain decision making.
Innovation Solution
A low-touch decision support framework using stochastic and heuristic techniques to iteratively refine input variables, reducing computational costs and enabling rapid evaluation of supply chain scenarios through a combination of simulation optimization and network inversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing scenario planning solutions are used to evaluate supply chain scenarios, then scenario evaluation can be performed, but the process is time consuming and requires manual inputs across multiple screens
Solution Approach 1:
The system enables self-service scenario evaluation by allowing users to define scenarios through high-level business objectives and constraints without manual screen-by-screen input. The automated scenario generation system creates multiple scenarios automatically based on the defined objectives, eliminating the need for extensive manual data entry and screen navigation while maintaining evaluation accuracy.
Solution Approach 2:
The patent replaces the mechanical manual input process with an automated computational system. Instead of requiring users to manually input data across multiple screens, the system uses algorithms to automatically generate scenarios, evaluate them against business objectives, and present results, thereby substituting manual mechanical operations with automated digital processing.
2Reliability
If existing scenario planning solutions are used, then scenario evaluation is possible, but they require expert knowledge and are prone to errors
Solution Approach 1:
The system provides self-service functionality that enables users without expert knowledge to perform accurate scenario evaluations. Users can define scenarios by specifying business objectives and constraints in a user-friendly interface, and the automated system handles the complex calculations and evaluations, eliminating the need for expert intervention while maintaining high accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms that automatically validate scenario definitions against business objectives and constraints. The automated evaluation process provides immediate feedback on scenario feasibility and performance, allowing users to refine their scenarios without requiring expert knowledge, thereby improving both reliability and ease of operation.
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 system applies partial action by selectively executing computational operations only when and where needed. Instead of continuously running expensive cloud computing resources, the system optimizes computations based on actual scenario evaluation requirements, using computational resources efficiently to achieve scenario evaluation capabilities without incurring prohibitive costs.
Solution Approach 2:
The patent changes computational parameters by adapting the level and type of computation based on scenario complexity and business objectives. The system dynamically adjusts computational resource allocation, using more intensive cloud computing only when necessary and relying on lighter local processing for simpler scenarios, thereby reducing overall computational costs while maintaining evaluation capability.
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.