Store-Level Diagnostics GUI With Automated Equipment Remediation
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Solution Overview
Problem
Existing systems lack an efficient method for providing store-level diagnostics and remediation, leading to suboptimal performance of physical equipment such as computer systems, ice machines, and coffee machines, which can indirectly affect store operations.
Innovation Solution
A system comprising one or more processors and memories that generates a graphical user interface to assign hierarchical levels to stores based on computed metric values. The system executes mitigation operations when metric values fall below thresholds, dynamically updating the interface with improved metric values and hierarchical levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual monitoring and maintenance of store equipment is used, then operational control is maintained, but response time to equipment failures is delayed and productivity decreases
Solution Approach 1:
The system enables automated self-monitoring and self-diagnosis of equipment status. Sensors continuously collect equipment data, the system automatically computes metric values, identifies performance issues, and executes mitigation operations without human intervention, allowing the system to serve itself in detecting and responding to equipment problems.
Solution Approach 2:
The system implements continuous feedback loops where equipment performance data is collected, analyzed, and used to trigger automated responses. The feedback mechanism compares actual metric values against thresholds and performance hierarchies, automatically initiating remediation actions when degradation is detected, thereby reducing response time and maintaining productivity.
2Reliability
If comprehensive equipment monitoring is implemented, then equipment performance is improved, but system complexity increases
Solution Approach 1:
The monitoring system is segmented into modular components: data collection modules at equipment level, computation modules for metric calculation, analysis modules for hierarchy determination, and execution modules for mitigation. This segmentation allows comprehensive monitoring to be implemented in manageable, independent units that can be deployed and maintained separately, reducing overall system complexity.
Solution Approach 2:
The system employs universal monitoring mechanisms that can assess multiple equipment types (computer systems, ice machines, coffee machines, drive-thru terminals) using a common framework. The same metric computation and hierarchy evaluation logic applies across different equipment, reducing complexity by avoiding equipment-specific monitoring systems while maintaining comprehensive coverage.
3Reliability
If automated mitigation operations are executed, then equipment uptime is increased, but operational control is reduced
Solution Approach 1:
The system performs preliminary actions by pre-defining mitigation operations for various equipment failure modes. When metric values indicate potential issues, the system executes pre-planned remediation steps automatically, increasing uptime by addressing problems before they cause complete failure. This preliminary action approach maintains operational control through pre-approved automated responses.
Solution Approach 2:
The system enables automated self-service through which equipment monitoring and basic remediation occur without human intervention. The automated execution of mitigation operations increases uptime by responding immediately to detected issues, while operational control is maintained through configurable thresholds, performance hierarchies, and the ability to override or adjust automated actions as needed.
Data Source
AI summary
In some examples, a system can generate a graphical user interface indicating that a first hierarchical level is assigned to a store based on metric values computed for the store using a set of data related to the store. The first hierarchical level can be a level in a predefined performance hierarchy. In response to determining that a value for a metric is below a predefined threshold, the system can execute a mitigation operation configured to improve the metric value (e.g., relative to a baseline). Subsequent to executing the mitigation operation, the system can generate an updated graphical user interface indicating that a second hierarchical level is assigned to the store based on updated metric values computed for the store using an updated set of data. The second hierarchical level may be higher in the predefined performance hierarchy than the first hierarchical level.