Digital Tag Error Prioritization for Targeted Defect Resolution
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Solution Overview
Problem
Current digital tag systems fail to effectively diagnose and correct errors, often leading to unnecessary replacement of functional tags and rails, resulting in waste and increased costs, as they do not account for the underlying causes of errors and lack mechanisms for addressing digital rail issues.
Innovation Solution
A system and method that includes an error manager on a processor to receive error indications from digital tags and rails, prioritize errors based on item type and location, predict error types, and provide corrective actions through a user interface, utilizing machine learning to improve error prediction and reduce resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital tag systems replace tags or rails when errors occur, then error resolution is achieved, but resource usage increases and waste occurs
Solution Approach 1:
The system performs preliminary diagnostic actions before replacement is considered. The error manager analyzes error indications to determine root causes, allowing for targeted corrective actions that may resolve errors without replacement, thus preventing unnecessary resource consumption.
Solution Approach 2:
The system implements a feedback mechanism where error indications from tags and rails are continuously monitored and analyzed. This feedback loop enables the error manager to identify patterns, predict failures, and implement corrective actions based on actual system state rather than predetermined replacement schedules.
2Reliability
If all error indications are addressed immediately, then system reliability improves, but time and resource allocation becomes inefficient
Solution Approach 1:
The error manager performs preliminary analysis and prioritization of error indications before full resolution actions are initiated. By pre-assessing error severity and impact, the system can allocate time and resources efficiently, addressing critical errors first while deferring or consolidating less urgent corrections.
Solution Approach 2:
The system maintains continuous monitoring and management of error indications, allowing for progressive resolution rather than interruptive batch processing. This continuous action enables the system to address errors in an optimized sequence, maintaining operational reliability while minimizing disruption and time loss.
3Device complexity
If digital tag systems lack error diagnosis capabilities, then system complexity is reduced, but error resolution accuracy deteriorates
Solution Approach 1:
The error manager serves as an intermediary component that adds diagnostic intelligence without requiring complexity changes in the individual tags or rails. This centralized mediator analyzes error indications from multiple sources, implementing sophisticated diagnosis logic while keeping the underlying hardware simple and manageable.
Data Source
AI summary
Systems and methods for resolving defects with digital tag systems include receiving, from a digital rail, an indication of a plurality of tags having unresolved errors, prioritizing, by an error manager implemented on a processor, the plurality of tags having unresolved errors based at least in part on a type of item assigned to each tag in the plurality of tags, wherein the plurality of tags are associated with a plurality of modular displays, each modular display including at least one digital rail, the at least one digital associated with at least one tag of the plurality of tags, identifying a highest priority tag from the prioritized plurality of tags, generating a corrective action for the highest priority tag, and generating instructions for implementing the generated corrective action. The generated instructions are presented on a user interface (UI) of a user device.