Manufacturing

Querri uses AI to help automate labor-intensive data cleaning and analysis processes, and makes optimizing maintenance strategies easier through predictive modeling.

Implementing RCM with Querri

Implementing Reliability Centered Maintenance requires significant data collection, cleaning, and analysis. The collection process is often very manual, resulting in a messy data set. Read our related blog here.

Querri can:

Take this messy data, clean it up
Automate the process of creating an RCM framework
And develop PdM models

Failure Mode and Effects Analysis (FMEA)

Querri's Natural Language Process and data processing capabilities can help to clean up human-entered data, such as closing comments and filling in missing data in intelligent ways.

This makes it easier to analyze an asset's past failures and potential failure modes, causes of those modes, and their component, asset, and plant-level impact.

You can also read our blog "Supercharging Six Sigma with Querri" here.

Optimizing Maintenance Strategies with Predictive Modeling

Querri can use its data exploration and visualization tools to uncover patterns and relationships between operating conditions, failure modes, and their effects.

It uses machine learning techniques to determine optimal scheduling for time-directed or condition-based maintenance activities.

Querri can also examine element failures and look for the most important causes, improving forecasting potential failures and their modes based on historical data.

Why Querri?

Simple ways to do hard

things

Talk to your data through a chat interface and watch it transform in a spreadsheet view.

Reliable, repeatable data

workflows

Clean, merge, and analyze once. Then set up your data workflows to run on your schedule.

Designed for humans, not

machines

It’s not a black box. See an explanation of the data workflows behind every Querri.