Secure AI Query Translation for Supply Chain Configuration
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Solution Overview
Problem
Configuring optimal supply chain parameters is challenging due to numerous variables and complexities, often resulting in suboptimal planning that leads to issues like service failures, over/under-stocks, factory inefficiencies, and high waste costs, which existing systems fail to address effectively.
Innovation Solution
A secure AI-driven supply chain optimization system that processes natural language queries, converts them into function and parameter statements, and executes API calls to generate optimized supply chain configurations while ensuring confidentiality and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual configuration of supply chain parameters is used, then system complexity is reduced and ease of operation is maintained, but manufacturing precision and reliability of supply chain optimization deteriorate due to inability to evaluate millions of configuration options
Solution Approach 1:
An AI assistant acts as an intermediary between the planner and the supply chain optimization system. The planner provides natural language queries about supply chain scenarios, and the AI assistant translates these into appropriate API calls and parameter settings, enabling precise configuration without requiring the planner to manually evaluate millions of options.
Solution Approach 2:
The system enables self-service through AI-driven automated configuration. The optimization system automatically processes natural language queries, determines appropriate parameters and settings, and generates optimized supply chain configurations without requiring manual intervention for each configuration decision, while still allowing planners to guide the process through queries.
2Productivity
If AI transformation system is used to process natural language queries, then productivity of supply chain analysis is improved, but loss of information increases due to potential exposure of sensitive data to external AI systems
Solution Approach 1:
The AI assistant serves as a secure intermediary that processes natural language queries without requiring sensitive data to leave the client's environment. The system translates queries into API calls and parameter settings while keeping proprietary supply chain information confidential, enabling productive analysis without data exposure risks.
3Manufacturing precision
If comprehensive supply chain optimization is performed evaluating all variables, then manufacturing precision is improved, but device complexity increases making the system difficult to operate and configure
Solution Approach 1:
The AI assistant mediates between the complex optimization system and the user, handling the complexity of evaluating millions of configuration options internally. The planner simply asks natural language questions about supply chain scenarios, and the AI assistant manages the complex API calls and parameter evaluations needed to provide accurate optimization results.
Solution Approach 2:
The system uses API calls to access optimization functions and retrieve results without requiring the user to understand or manage the underlying complex optimization engine. The AI assistant copies and translates between the simple natural language query interface and the complex optimization system, shielding users from complexity while maintaining access to comprehensive optimization capabilities.
Data Source
AI summary
The present invention relates to systems and methods for query-based analysis of optimization of a supply chain. In some embodiments, a query is received within a trusted environment of an optimization system, wherein the query includes a natural language expression. The query is supplied to an Artificial Intelligence (AI) transformation system, wherein the AI transformation system converts the query into a function and parameter statement. The function and parameter statement are translated into at least one API call and function, which are executed to generate results. The results are then outputted. The AI transformation system is locally trained on the optimization system. In some embodiments, the query may be augmented with information. The information includes at least one of query source information and query intent information. Additionally, sensitive data in the query may be identified. The sensitive information is identified by blacklist, whitelist or semantic analysis.