Decentralized Business Planning System for Real-Time Contingency Generation
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Solution Overview
Problem
Existing business continuity planning methods are often human-centric and struggle to validate data in real-time, leading to inefficiencies in generating effective contingency plans during disruptions, which can result in significant losses and safety risks for enterprises.
Innovation Solution
A machine learning-based decentralized business planning system that utilizes a distributed ledger-based verification system, such as Holochain, to authenticate and rank data sources, perform analytics, and generate contingency plans, while optimizing network resources and bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional human-centric methods are used to validate data sources and generate business continuity plans, then the process allows for human judgment and flexibility, but the system cannot validate data in real-time and generates plans too slowly to be effective during disruptions
Solution Approach 1:
The patent replaces human-centric manual validation and plan generation with an automated machine learning system. The ML model validates data sources and generates business continuity plans automatically, eliminating the bottleneck of human review while maintaining or improving accuracy through algorithmic validation of multiple data sources.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously validate data sources, rank their reliability, and generate contingency plans without human intervention. The system serves itself by automatically updating its knowledge base and improving its validation capabilities through continuous operation.
2Loss of information
If multiple data sources are utilized to improve the comprehensiveness of business continuity plans, then the quality of insights increases, but the complexity of validating and processing data from these sources increases significantly
Solution Approach 1:
The patent segments the complex validation task by having the machine learning model evaluate each data source independently and assign reliability scores. This breaks down the overwhelming complexity of validating multiple sources into manageable individual assessments, where each source is validated against specific criteria and weighted accordingly in the final plan generation.
Solution Approach 2:
The system changes the parameter of data validation from a binary valid/invalid determination to a continuous reliability scoring system. The ML model assigns numerical scores to each data source based on multiple factors, allowing for nuanced evaluation and automatic weighting in plan generation, thereby managing complexity through quantitative parameter transformation.
3Reliability
If real-time data validation and analytics are performed to ensure accuracy, then the reliability of business continuity plans improves, but the computational resources and bandwidth required increase
Solution Approach 1:
The patent applies partial action by having the machine learning model focus validation efforts on the most critical data sources and parameters. Rather than exhaustively validating every aspect of every data source, the system identifies and prioritizes validation of key factors that most impact plan accuracy, thereby reducing overall computational resource consumption while maintaining reliability.
4Reliability
If manual intervention is used to review and approve contingency plans, then the quality control improves, but the time required to deploy resources and mitigate disruptions increases
Solution Approach 1:
The patent implements preliminary action by having the machine learning model pre-validate data sources and pre-generate multiple contingency plan options before disruptions occur. The system maintains a ready pool of validated plans that can be automatically deployed when disruptions are detected, eliminating the need for time-consuming manual review and approval during critical response periods.
Data Source
AI summary
Aspects of the disclosure relate to a machine learning based decentralized business planning system. A computing platform may identify an event likely to impact one or more business operations. Subsequently, the computing platform may receive, for the event, data from one or more sources of data. Then, the computing platform may generate a data structure including a plurality of nodes, where the plurality of nodes corresponds to the received data. Then, the computing platform may authenticate, by utilizing a distributed ledger-based verification system, the plurality of nodes. Subsequently, the computing platform may perform, based on business rules applicable to the one or more business operations, analytics on the plurality of authenticated nodes. Then, the computing platform may generate, based on the analytics, a contingency plan to mitigate the impact to the one or more business operations, and may provide, via an interactive graphical user interface, the contingency plan.