Refrigeration unit usage diagnostic and evaluation
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Solution Overview
Problem
Transport refrigeration units (TRUs) are often operated inefficiently by customers, leading to reduced performance and increased environmental impact, as sellers lack understanding of how their products are utilized post-sale.
Innovation Solution
A diagnostic tool that analyzes customer usage data and business operations, along with environmental data, to generate a usage scorecard and recommend actions such as maintenance, upgrades, or downgrades, utilizing sensors and surveys to optimize TRU performance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If customers operate TRUs without guidance or monitoring, then operational flexibility is maintained, but efficiency and performance deteriorate
Solution Approach 1:
The system continuously collects usage data from sensors and customer surveys, analyzes this data through a processor, and provides feedback via usage scorecards and recommendations. This closed-loop feedback mechanism guides customers toward more efficient operations without removing their autonomy, as they can choose to implement the recommendations at their discretion
Solution Approach 2:
The diagnostic tool acts as an intermediary between the TRU manufacturer and the customer. It translates raw usage data into actionable insights and recommendations, mediating the interaction by providing expert guidance while preserving customer independence in decision-making
2Reliability
If TRUs are operated without diagnostic monitoring, then system complexity is reduced, but performance optimization and lifespan extension are limited
Solution Approach 1:
The diagnostic tool integrates multiple functions into a single system: data collection from various sensors, customer survey management, data analysis, scorecard generation, and recommendation provision. This multi-functional approach improves TRU reliability through comprehensive monitoring while minimizing the need for separate complex systems
Solution Approach 2:
The system enables self-service through automated data collection via sensors, automatic analysis by the processor, and generation of usage scorecards without requiring manual intervention. This reduces the operational complexity for customers while maintaining high reliability through continuous automated monitoring
3Loss of information
If detailed usage data is collected and analyzed, then operational insights and recommendations are improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the most relevant usage parameters from the collected data for analysis and scorecard generation. By focusing on key metrics rather than processing all possible data points, it maintains comprehensive usage information while avoiding unnecessary processing complexity
Solution Approach 2:
The system transforms raw usage data into meaningful parameters and metrics that are relevant for evaluating TRU operation. This parameter transformation allows comprehensive information capture while simplifying the analysis process by converting complex raw data into actionable insights
Data Source
AI summary
Methods and systems for evaluation and diagnostic for a customer product are provided. Aspects include receiving, by a processor, customer usage data associated with the customer product, obtaining customer data associated with a customer business operation, analyzing, by the processor, the customer usage data and the customer data to generate a usage score card associated with the customer product, and determining an action for the customer product based at least in part on the usage score card.