Flutura Case Studies Prevent Process Inefficiencies with Automated Root Cause Analysis

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	Prevent Process Inefficiencies with Automated Root Cause Analysis Flutura

Prevent Process Inefficiencies with Automated Root Cause Analysis

Flutura

Technology Category	Analytics & Modeling - Machine Learning
Applicable Functions	Maintenance
Use Cases	Predictive Quality Analytics
Services	Data Science Services
Challenge	Manufacturers mostly rely on on-site expert knowledge for root cause analysis. When the defective product is sent to lab for analysis, it is laborious and always a post-mortem one. Manufacturers that collect data from IT and OT also need a comprehensive understanding of a variety of professionals to make sense of it. This is not only time consuming, but also inefficiencient. Read More
Solution	Root cause analysis is getting revamped in light of the Industry 4.0 revolution. What was once a post-mortem analysis has become a predictive one. Advancements in machine learning algorithms and big data analytics aid to perform root cause analysis using automated methods. These methods are based on historic, real-time data from the production floor. For a fool-proof technology, algorithms also accept inputs provided by the manufacturers. This is pivotal as the traditional or institutional knowledge is also accounted for and the accuracy of the prediction improves for the next time. Data-driven Diagnostics model is trained to detect any deviation from the normal to declare it as an anomaly. Machine Learning algorithms capture the impact of 5Ms of manufacturing on the output quality. The algorithms add the criticality value to each of the identified causes and generate signals accordingly. “Alerts” the user of the most critical and parameter with the highest impact on the quality “Warns” the user of the parameters with medium impact on the quality “Cautions” the user of the parameters with a lower impact on the quality Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Applicable Functions

Maintenance

Use Cases

Predictive Quality Analytics

Services

Data Science Services

Challenge

Manufacturers mostly rely on on-site expert knowledge for root cause analysis. When the defective product is sent to lab for analysis, it is laborious and always a post-mortem one. Manufacturers that collect data from IT and OT also need a comprehensive understanding of a variety of professionals to make sense of it. This is not only time consuming, but also inefficiencient.

Solution

Root cause analysis is getting revamped in light of the Industry 4.0 revolution. What was once a post-mortem analysis has become a predictive one. Advancements in machine learning algorithms and big data analytics aid to perform root cause analysis using automated methods. These methods are based on historic, real-time data from the production floor. For a fool-proof technology, algorithms also accept inputs provided by the manufacturers. This is pivotal as the traditional or institutional knowledge is also accounted for and the accuracy of the prediction improves for the next time.

Data-driven Diagnostics model is trained to detect any deviation from the normal to declare it as an anomaly. Machine Learning algorithms capture the impact of 5Ms of manufacturing on the output quality. The algorithms add the criticality value to each of the identified causes and generate signals accordingly.

“Alerts” the user of the most critical and parameter with the highest impact on the quality
“Warns” the user of the parameters with medium impact on the quality
“Cautions” the user of the parameters with a lower impact on the quality

Impact #1	[Process Optimization - Real Time Alerts] Manufacturers can identify the causes and the criticality of the impact for any deviations in the process or product with just 1-click.

Impact #1

[Process Optimization - Real Time Alerts]

Manufacturers can identify the causes and the criticality of the impact for any deviations in the process or product with just 1-click.

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Overview

Prevent Process Inefficiencies with Automated Root Cause Analysis

Operational Impact