Manufacturing Cloud Customization Using Generative AI
Find Innovative SolutionsGenerate Solutions
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 in data collection and analytics services, allowing industrial customers to easily customize databases, data collection templates, and reporting fields through extensibility tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based industrial solutions use predefined data collection and analytics services, then system reliability and standardization are improved, but adaptability to specific customer needs deteriorates
Solution Approach 1:
The system enables dynamic customization of cloud-based industrial services through a configuration interface that allows customers to modify data collection parameters, analytics settings, and reporting templates in real-time without redeployment. This transforms the static predefined services into dynamically adaptable services that maintain reliability through standardized core functionality while achieving adaptability through runtime configuration changes.
Solution Approach 2:
The patent implements parameter-based customization where customers can modify specific service parameters such as data collection frequencies, analytics algorithms, reporting formats, and threshold values through a configuration interface. This allows the system to maintain standardized service architecture while adapting to customer-specific requirements by changing operational parameters without affecting system reliability.
2Adaptability or versatility
If cloud-based services are highly customizable to meet specific customer needs, then adaptability is improved, but device complexity and implementation difficulty worsen
Solution Approach 1:
The system implements a universal configuration interface that serves multiple customization functions through a single unified mechanism. This interface handles diverse customization requirements (data collection, analytics, reporting) using consistent interaction patterns and configuration methods, reducing perceived complexity while maintaining high adaptability across different service areas.
Solution Approach 2:
The patent enables customers to perform self-service customization through an intuitive configuration interface that allows direct modification of service parameters without requiring system administrator intervention or complex programming. This self-service capability reduces implementation complexity by empowering end-users to adapt services to their needs independently.
3Productivity
If cloud-based industrial computing systems leverage broader data scope and integrate wider range of tools, then service capability and productivity are improved, but ease of operation deteriorates
Solution Approach 1:
The system segments the broader data scope and wider range of tools into distinct, organized categories that are presented through a structured configuration interface. This segmentation allows customers to access and configure specific data sources and analytical tools in a systematic manner, maintaining ease of operation while leveraging extended capabilities for enhanced productivity.
Data Source
Figure 1
Figure 2
Figure 3
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.