Control Tower Platform With Adaptive Intelligence for Logistics Design
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Solution Overview
Problem
The increasing complexity and volume of data from IoT devices and various data sources overwhelm organizations, making it difficult to convert data into actionable insights for timely and efficient operations in value chain network management.
Innovation Solution
A cloud-based management platform with a micro-services architecture, incorporating interfaces for feature access, network connectivity, adaptive intelligence, data storage, and monitoring facilities, along with applications for demand and supply chain management, enables enterprises to manage value chain network entities from origin to customer use, utilizing technologies like 5G networks, IoT systems, cognitive networking, and digital twins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If organizations collect and store large amounts of data from IoT devices and various data sources, then the availability of information for decision-making is improved, but the complexity and volume of data management increases, making it difficult to convert data into actionable insights
Solution Approach 1:
The patent introduces an adaptive intelligence layer with robotic process automation systems that act as intermediaries between raw data sources and decision-making processes. This layer includes natural language processing capabilities, machine learning models, and automated workflow systems that translate complex multi-modal data into actionable insights without requiring direct human intervention in data processing
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with intelligent systems including natural language processing, machine learning algorithms, and automated reasoning engines. These systems automatically interpret unstructured data from IoT devices, convert it into structured insights, and execute decisions without manual mechanical processing steps
2Adaptability or versatility
If traditional linear supply chain management is used, then operational simplicity is maintained, but the ability to respond to complex market demands and coordinate multiple value chain entities is limited
Solution Approach 1:
The patent creates a universal control tower platform that can manage diverse value chain entities including manufacturing facilities, logistics operations, retail outlets, and service providers through a single integrated system. The platform provides multi-functional capabilities for demand forecasting, supply chain optimization, inventory management, and real-time monitoring across different industries and operational contexts
Solution Approach 2:
The patent transitions from traditional two-dimensional supply chain management (supply and demand) to multi-dimensional coordination that includes time, space, multiple entities, and various data modalities. The system orchestrates operations across geographic locations, time zones, and organizational boundaries while integrating structured and unstructured data from diverse sources
3Productivity
If manual data processing and decision-making processes are used, then system simplicity is maintained, but the speed and efficiency of operations decrease
Solution Approach 1:
The patent implements self-service capabilities where the adaptive intelligence system automatically monitors operational parameters, detects anomalies, generates insights, and executes corrective actions without human intervention. The system autonomously adjusts supply chain operations, optimizes inventory levels, and responds to market changes based on real-time data analysis
Solution Approach 2:
The patent establishes closed-loop feedback mechanisms where operational data is continuously collected, analyzed by adaptive intelligence systems, and used to automatically adjust processes. The system monitors outcomes, compares them against targets, and implements corrective actions to maintain optimal performance across value chain operations
4Loss of information
If comprehensive monitoring of value chain entities is implemented, then operational visibility is improved, but the resources required for data collection and processing increase
Solution Approach 1:
The patent applies local quality by processing and analyzing data at the source or closest to it using edge computing capabilities embedded in IoT devices and local systems. This approach filters, aggregates, and pre-processes data locally before transmitting only essential information to central systems, reducing overall data processing requirements while maintaining comprehensive monitoring
Data Source
AI summary
A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.