Evaluation of the relationship between equipment management using data mining and IoT technology and quality.
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 discussions that inevitably arise 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 to start with a system that is around 60 to 70 points in completeness, rather than aiming for a perfect 100-point system from the beginning, and to gradually improve the system towards the desired state. This document introduces some of the essence of that approach.
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 are omitted due to page constraints, but feel free to reach out if you would like a more detailed explanation.
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Applications/Examples of results
Achievements - Assembly manufacturing industry - Semiconductor manufacturing equipment manufacturer - Railway industry - Chemical plants - Power-related companies
