Control Tower Platform Using AI Digital Twins for Logistics Decisions
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Solution Overview
Problem
Organizations face challenges in managing complex and voluminous data from smart devices and IoT systems, leading to overwhelmed users and missed opportunities for timely and informed decision-making in value chain networks.
Innovation Solution
A cloud-based management platform with a micro-services architecture, incorporating interfaces, network connectivity, adaptive intelligence, data storage, and monitoring facilities, along with robotic process automation, to manage value chain network entities from origin to customer use, automating processes and translating data into actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If organizations implement comprehensive data collection systems with IoT sensors and smart devices, then the amount of available data increases dramatically, but users become overwhelmed and miss opportunities for timely decision-making
Solution Approach 1:
The patent introduces an intermediary layer (control tower platform with AI/ML systems) between the raw data sources and human users. This intermediary automatically processes, analyzes, and translates vast amounts of IoT and operational data into actionable insights and recommendations, enabling timely decision-making without overwhelming users with raw data volumes
Solution Approach 2:
The patent replaces manual data analysis and decision-making processes with automated AI/ML systems. Machine learning models automatically process data streams, identify patterns, and generate insights, substituting the mechanical process of human data review with intelligent automated systems that can handle voluminous data without causing user overload
2Loss of information
If organizations manually manage and analyze voluminous data from multiple sources, then comprehensive insights can be obtained, but decision-making timeliness is reduced and opportunities are missed
Solution Approach 1:
The patent implements continuous automated data processing and analysis through AI/ML systems that operate without interruption. The control tower platform continuously ingests data streams from IoT sensors, ERP systems, and other sources, maintaining uninterrupted analysis to provide timely insights without the delays associated with manual batch processing
Solution Approach 2:
The system enables self-service automated analysis where AI/ML models independently process data, generate insights, and update recommendations without human intervention. The robotic process automation systems autonomously manage data workflows, allowing the organization to obtain comprehensive insights automatically without manual analysis time
3Device complexity
If traditional linear supply chain management is used, then operational simplicity is maintained, but efficiency and responsiveness to demand changes are reduced
Solution Approach 1:
The patent transforms the static linear supply chain into a dynamic, adaptive system. The control tower platform with AI/ML continuously monitors demand signals, inventory levels, and operational data, automatically adjusting supply chain parameters and recommendations in real-time to optimize efficiency while maintaining manageable complexity through automated adaptation
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.