Supply Chain Event Sequencing for Counterfeit Detection
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Solution Overview
Problem
The healthcare supply chain faces challenges in tracking and tracing products due to the high number of items and multiple parties involved, leading to complexity and prevalence of counterfeit goods, which existing systems struggle to address effectively.
Innovation Solution
A comprehensive system that uses electronic product codes (EPCs) and event sequencing to track and trace products across the supply chain, including commissioning, aggregation, disaggregation, and decommissioning events, with a rules engine to detect fraud and anomalies, ensuring data visibility and regulatory compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional tracking systems are used to monitor products in the supply chain, then basic product movement tracking is achieved, but the system cannot effectively handle the high volume of items and multiple parties, leading to tracking failures and counterfeit goods
Solution Approach 1:
The system segments the supply chain into discrete events (commissioning, aggregation, disaggregation, decommissioning, shipping, receiving) and tracks each event individually using electronic product codes. This segmentation allows the system to manage high volumes of products and parties by breaking down continuous tracking into manageable discrete data points, resolving the contradiction between tracking reliability and system complexity.
2Measurement precision
If detailed tracking of individual products is implemented, then counterfeit detection capability is improved, but the complexity of managing high volume products across multiple parties increases significantly
Solution Approach 1:
The system introduces an intermediary data structure called 'event sequence' that mediates between individual product identifiers and the complex network of supply chain parties. Each product is associated with a sequence of events rather than direct tracking of all interactions, reducing data management complexity while maintaining precise product identification and counterfeit detection capabilities.
3Reliability
If comprehensive event sequencing is implemented to detect fraud, then fraud detection capability is improved, but the computational resources and system complexity increase
Solution Approach 1:
The system performs preliminary actions by establishing expected event sequences and relationships before fraud detection is needed. Commissioning events create initial product identities, aggregation events establish parent-child relationships, and disaggregation events prepare for potential counterfeit detection. This preliminary structuring of data reduces computational complexity during actual fraud detection by having the framework already in place.
4Reliability
If real-time data visibility is provided across the supply chain, then patient safety is enhanced, but the system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential tracking information needed for patient safety from the complex supply chain data. Rather than processing all supply chain interactions, it extracts key events (commissioning, aggregation, disaggregation, decommissioning, shipping, receiving) and their relationships, providing real-time visibility for patient safety while reducing data processing complexity by filtering out non-essential information.
Data Source
AI summary
Systems and methods are directed to supply chain management. In particular, the tracking, tracing, authenticating, and reporting of supply chain events for products, is disclosed. Various embodiments can store, analyze, and track supply chain events and help to coordinate and maintain trading partner connections. Various embodiments also help to enhance patient safety, secure the supply chains for pharmaceuticals, medical devices, and other healthcare products, and help users to follow regulatory requirements.