IoT Orchestration System for Supply Chain Coordination
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Solution Overview
Problem
Current supply chain processes operate in silos, lacking interaction and structured coordination, which limits end-to-end impact analysis and proactive assessment of key performance indicators (KPIs), leading to delayed responses due to unstructured coordination across teams and business processes.
Innovation Solution
An orchestration system that utilizes Internet of Things (IoT) sensor data to determine process events and identify process orchestrators based on predictive models, executing workflows to address these events according to pre-defined rules, thereby integrating and coordinating supply chain processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If supply chain processes are executed in silos with limited coordination, then individual process execution is simple and independent, but end-to-end supply chain impact analysis and proactive assessment of KPIs are not possible
Solution Approach 1:
An orchestration system acts as an intermediary layer between siloed supply chain processes, enabling structured coordination and information flow without requiring fundamental changes to individual processes. The orchestration system receives events from multiple processes, analyzes them using predictive models, and coordinates responses across the supply chain, thus improving information flow while maintaining process independence.
2Loss of time
If unstructured coordination is used across business teams, then implementation is simple and flexible, but response time is delayed due to lack of structured coordination
Solution Approach 1:
Predictive models are pre-configured with supply chain knowledge and relationships before events occur. When events are detected by the orchestration system, these pre-established models enable immediate impact analysis and proactive identification of affected KPIs, eliminating the need for time-consuming ad-hoc analysis and reducing response delays.
3Reliability
If there is no predictive modeling, then system simplicity is maintained, but proactive assessment of affected KPIs is not available leading to inbuilt delay in response
Solution Approach 1:
Predictive models are pre-configured with supply chain knowledge and relationships before events occur. When events are detected by the orchestration system, these pre-established models enable immediate impact analysis and proactive identification of affected KPIs, eliminating the need for time-consuming ad-hoc analysis and reducing response delays.
4Loss of information
If IOT sensors are deployed to monitor supply chain processes, then real-time visibility is improved, but data processing complexity and infrastructure requirements increase
Solution Approach 1:
The orchestration system serves as an intermediary that consolidates and processes data from multiple IOT sensors. Rather than requiring complex processing at each sensor node or centralizing all raw data, the orchestration system receives structured events from sensors, correlates them with process impact data, and generates coordinated responses, thus improving real-time visibility while managing processing complexity.
Data Source
AI summary
In one embodiment, a method for orchestration of supply chain processes is disclosed. The method includes obtaining, by an orchestration system, Internet of Things (IOT) sensor data from IOT sensors, wherein the IOT sensors monitor a plurality of supply chain processes. The method includes determining, by the orchestration system, a process event corresponding to a supply chain process of the plurality of supply chain processes by analyzing the IOT sensor data and process impact data. Further, the method includes identifying, by the orchestration system, one or more process orchestrators for the process event based on a predictive model and executing, by the orchestration system, one or more workflows for each of the one or more process orchestrators based on pre-defined rules.