Inventory Robot Digital Twins for Value Chain Decision Flow
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack efficient and integrated management of complex value chain networks involving multiple entities, leading to inefficiencies and challenges in data communication and decision-making across various stages of production, distribution, and consumption.
Innovation Solution
Implementing a system that configures secondary computing devices to communicate with a primary device, utilizing configured system services and intelligence services to manage and fulfill commands, enabling intelligent decision-making and data translation across the value chain network entities, including products, suppliers, and logistics processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a linear supply chain management system is used, then implementation is simple, but communication efficiency and decision-making capability deteriorate across multiple value chain entities
Solution Approach 1:
The system segments the value chain network into multiple hierarchical levels (primary computing device at enterprise operator level, secondary computing devices at entity level, and tertiary devices at operational level). Each level handles specific communication and decision-making functions, reducing information loss by localizing processing while maintaining overall system simplicity through modular architecture.
Solution Approach 2:
The patent introduces a hierarchical dimensional structure to the traditionally linear supply chain. By adding the vertical hierarchy dimension (primary-secondary-tertiary levels) to the horizontal linear flow, the system enables multi-dimensional communication paths that improve information efficiency without significantly increasing perceived complexity.
2Productivity
If multiple secondary computing devices are configured to manage value chain entities, then communication and decision-making efficiency improves, but system complexity increases
Solution Approach 1:
Secondary computing devices are designed with universal functionality to handle multiple value chain entities (suppliers, manufacturers, retailers, logistics providers) through standardized interfaces and protocols. This multi-functionality allows a single device type to manage diverse entities, improving decision-making efficiency across the network while reducing the variety of components needed, thereby controlling system complexity.
Solution Approach 2:
The primary computing device acts as an intermediary that coordinates between multiple secondary computing devices. It provides centralized management functions (enrollment, configuration, monitoring) that simplify the interactions between numerous secondary devices, enabling efficient multi-entity communication without requiring complex peer-to-peer coordination protocols.
3Productivity
If data translation and intelligence services are implemented across value chain entities, then operational optimization improves, but implementation complexity increases
Solution Approach 1:
Secondary computing devices automatically perform data translation and intelligence service functions without requiring manual configuration for each value chain entity. The system self-adapts to different entities through automated enrollment processes that detect and configure appropriate translation protocols and intelligence services based on entity type and capabilities, reducing implementation complexity while maintaining operational optimization.
Solution Approach 2:
The system dynamically adjusts translation parameters and intelligence service configurations based on the specific requirements of each value chain entity and communication context. Rather than implementing fixed complex translation rules, the system modifies parameters (data formats, communication protocols, intelligence levels) adaptively, achieving operational optimization with simpler implementation.
Data Source
AI summary
A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may configure a robotic process automation system to execute the task to facilitate an improvement in the value chain network.