Manufacturing Cloud Customization Using AI and Tenant Segmentation
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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 there are limitations in the capabilities of cloud-based industrial computing systems that can be addressed by leveraging a broader scope of data and integrating a wider range of tools.
Innovation Solution
A multi-tenant Software-as-a-Service (SaaS) manufacturing platform that utilizes generative artificial intelligence (AI) to infer and implement customer-specific customizations based on natural language inputs, offering extensibility tools for database customization, data collection templates, and reporting fields, and integrates data from various sources for advanced analytics and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based industrial solutions use predefined data collection and analytics services, then system reliability and data security are improved, but the ability to customize services to specific customer needs deteriorates
Solution Approach 1:
The system segments the multi-tenant cloud platform into isolated customer-specific instances, where each customer receives a customized instance of data collection and analytics services. This segmentation allows predefined services to maintain reliability while enabling customization through instance-specific configurations, directly resolving the contradiction between system reliability and adaptability.
2Productivity
If cloud-based services serve multiple enterprises simultaneously, then productivity and resource utilization are improved, but the complexity of managing diverse customer requirements increases
Solution Approach 1:
The system implements a universal multi-tenant architecture where a single cloud platform serves multiple enterprises simultaneously through isolated instances. This universal design enables the system to handle diverse customer requirements while maintaining efficient resource utilization, resolving the contradiction between productivity and complexity by providing a standardized yet customizable framework.
3Measurement precision
If cloud-based industrial computing systems integrate broader data sources and tools, then analytical capability and decision-making quality are improved, but system complexity and integration challenges increase
Solution Approach 1:
The system introduces an intermediary layer in the form of a standardized data collection and analytics framework that mediates between diverse data sources and customer-specific analytical requirements. This intermediary architecture enables integration of broader data sources and tools while managing complexity through standardized interfaces and pre-configured analytics templates.
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.