Machine Learning Model for Maintenance Priority Order
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Solution Overview
Problem
Existing maintenance techniques for devices, such as printers, often require multiple visits and manual judgment by repair service providers, leading to inefficiencies and increased costs due to the variability in device states and environments.
Innovation Solution
An information processing apparatus that utilizes machine learning to create a model associating device status and installation environment information with required maintenance, enabling the prioritization and automation of maintenance tasks, thereby reducing the need for revisits and improving work efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual judgment by repair service providers is used to determine maintenance tasks, then flexibility in handling various device states is improved, but work efficiency decreases and multiple visits are required
Solution Approach 1:
The system enables automatic determination of maintenance tasks by having the device itself provide status information and the system automatically generates maintenance plans, reducing reliance on manual repair provider judgment and improving work efficiency
Solution Approach 2:
The patent replaces the mechanical system of manual judgment with an information processing system that automatically analyzes device status information and determines maintenance tasks, thereby improving efficiency while maintaining flexibility
2Reliability
If multiple visits are performed for maintenance, then thorough error fixing is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The system performs preliminary analysis of device status information before the repair provider arrives, pre-determining the maintenance tasks that need to be performed, which ensures thorough error fixing while reducing the time spent on multiple visits
Solution Approach 2:
The system continuously receives status information from the device and adjusts maintenance plans based on feedback, ensuring that all necessary maintenance tasks are identified in advance and can be completed in a single visit
3Measurement precision
If comprehensive status information and installation environment information are collected, then maintenance accuracy is improved, but device complexity increases
Solution Approach 1:
The system is designed to handle multiple types of information (device status information and installation environment information) through a unified information processing approach, improving maintenance determination accuracy without significantly increasing system complexity
Solution Approach 2:
The system changes the parameters being measured from simple operational status to comprehensive status information including installation environment, thereby improving maintenance accuracy while managing complexity through systematic parameter management
Data Source
AI summary
Provided is an information processing apparatus including a storage that stores a machine-learned model created through machine learning using teacher data in which at least one of status information indicating a status of a device to be maintained and installation environment information indicating an environment where the device to be maintained is installed is associated with maintenance to be performed on the device to be maintained, and a processor that acquires maintenance to be performed on the device to be maintained using at least one of the status information of the device to be maintained and the installation environment information, and the machine-learned model, and displays a maintenance priority order.