Proposal for examining the causal relationship between predictive maintenance and quality and equipment management in IoT.
Recommended technical proposal for customers considering the measurement of the effectiveness of predictive maintenance and equipment management operations, as well as quality improvement! Utilizing large-scale data and machine learning as well!
This document explains, based on our company's implementation results and experience, the necessary considerations for threshold examination, which is always a concern when conducting predictive maintenance, as well as what indicators should be used when considering and implementing predictive maintenance. Additionally, we focus on the analysis of causal relationships with quality-related issues, which we have received many inquiries about in recent years, in conjunction with equipment maintenance. When building IoT and predictive maintenance systems, it is essential not to implement a perfect system from the start, but rather to gradually improve the system from a completion level of around 60 to 70 points towards the desired state. This document introduces some of the essence of that approach.
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basic information
This document briefly explains the steps necessary to consider how to achieve predictive maintenance by combining the following systems we offer: - Calendar-type equipment management system: FLiPS - Reliability/Safety/Availability/Maintainability evaluation tool: RWB/AWB - Machine learning tool: SPM Please note that some details have been omitted due to page constraints, but if you would like a more detailed explanation, please feel free to reach out.
Price range
Delivery Time
※It will vary depending on the data you have and the scope you wish to implement.
Applications/Examples of results
Achievements - Assembly manufacturing industry - Semiconductor manufacturing equipment manufacturer - Railway industry - Chemical plants - Power-related companies
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Company information
Our company develops and sells a "Maintenance Management System" for managing and operating various plants, factories, and other facilities and assets. Currently, this system is undergoing significant evolution into a system that incorporates IoT technologies, such as sensor information and input from tablet devices, as well as AI technologies like machine learning, featuring functions for failure prediction and automatic scheduling. Additionally, as part of the recent trend of digital transformation (DX), there is a growing movement to digitize and automate manufacturing processes and research and development sites in factories to improve operational efficiency. In line with this trend, our company provides a solution aimed at enhancing efficiency in research and development environments, which is the Laboratory Information Management System (LIMS). This software includes features such as workflow management, data tracking, data management, data analysis, and integration of electronic lab notebooks.
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