Generative AI Supply Chain Planning for Multi-Tenant Customization
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Solution Overview
Problem
Existing cloud-based industrial solutions face challenges in easily customizing services to meet the specific needs of each industrial customer and have limitations in data integration and tool capabilities.
Innovation Solution
A multi-tenant Software-as-a-Service (SaaS) manufacturing platform that leverages generative AI to collect, analyze, and customize industrial applications, including MES, ERP, and supply chain management, allowing customers to easily tailor databases and reporting features, and dynamically adjust production schedules to optimize business metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud-based industrial solutions are implemented, then scalability and multi-tenancy are improved, but customization capability deteriorates
Solution Approach 1:
The patent implements dynamic customization where cloud-based industrial solutions can adapt their configuration and functionality based on specific customer needs. The system allows dynamic adjustment of parameters, workflows, and interfaces without requiring custom software development, enabling each tenant to customize the platform while maintaining the underlying standardized architecture.
Solution Approach 2:
The system enables customization through parameter changes rather than structural modifications. Customers can modify operational parameters, data formats, reporting metrics, and process configurations within the cloud platform without changing the core system architecture. This allows tailored solutions while preserving system simplicity and scalability.
2Measurement precision
If data scope is limited to single-tenant, then data security is improved, but analytical capability and optimization deteriorate
Solution Approach 1:
The patent merges data from multiple tenants while maintaining logical separation through anonymized aggregation. The system combines insights from across the industrial ecosystem to improve forecast accuracy and optimization capabilities, allowing each tenant to benefit from collective intelligence without compromising their data security or privacy.
Solution Approach 2:
The system introduces an intermediary data layer that processes and anonymizes multi-tenant data before making it available for analytical purposes. This intermediary layer enables cross-tenant analysis and benchmarking while preserving data security boundaries, allowing tenants to access aggregated insights without direct access to other tenants' sensitive information.
3Productivity
If manual schedule adjustment is used, then customization to specific needs is improved, but productivity and response time deteriorate
Solution Approach 1:
The system implements self-service automation where production schedules are automatically adjusted based on real-time data from suppliers, manufacturers, and logistics partners. The platform autonomously processes disruptions, reoptimizes schedules, and coordinates actions across the supply chain without manual intervention, dramatically improving response time while maintaining alignment with each tenant's specific needs through configured priorities and constraints.
Data Source
AI summary
A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.