Predictive Quality Control

Prevent defects with predictive quality analytics that provide real-time alerts and recommended actions and allow factory teams to proactively resolve potential quality issues.

Oden combines predictive quality analytics with your quality management experience to help you minimize defects and overall scrap rates.

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Analyze Data

Live and historical production data from machines, ERP, MES and quality systems is analyzed to detect patterns.

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Predict Quality

Predict quality in real-time by identifying and monitoring process conditions that have previously led to defects.
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Alert Teams

Notify operators and supervisors of potential quality failures with sufficient lead time to proactively resolve problems.

The Benefits Of Predictive Quality Management

Prevent Quality Failures

Prevent quality failures with real-time alerts that enable operators to proactively adjust process parameters to increase first-pass yield and maintain compliance. 

Optimize Material Usage

Optimize material usage by predicting scrap rates based on live production conditions and alert supervisors if desired rates are about to be exceeded. 

Quickly Isolate Defects

Quickly isolate defects by identifying when and where in the production process defects occurred to limit the overall number of pieces scrapped. 

Increase Contribution Margins

Increase contribution margins by minimizing scrap, reducing production variability to limit material waste and improving labor effectiveness.

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How Predictive Quality Control Works

Centralize your OT and IT data to create a digital thread of your production processes using machine learning. Data is cleaned and contextualized into a consistent format, organized into a taxonomy that assigns semantics to the data and aligned with metadata such as the product, shift or quality state.

As a map for your predictive quality solution, the digital thread translates your manufacturing and quality expertise into an actionable representation of your production line.

Machine learning algorithms analyze live and historical production data to identify patterns in behavior that have previously led to quality failures. Relationships between key process variables, such as line speed, pressures and temperatures and the quality measurements such as dimensional data of the product, are determined to build a predictive quality model.

When models are deployed in a production environment, they look for patterns or sets of conditions that provide early indications of inefficiencies, scrap rates or quality failures.

Alerts are sent in real-time to operators, shift supervisors or engineers when such indications are detected on the production line. These alerts allow your teams to take corrective action in advance to avoid producing defective products for an extended period of time.

Alerts can be customized based on factory floor conditions or a chain of command. Alerts are also sent with interpretable supporting evidence and can be tied to recommended courses of action to improve the response.

Over time models change, new processes, new people and new data all contribute to model evolution. Models must be retrained consistently to ensure predictions are accurate.

A cloud-edge hybrid solution with rapid iteration reduces the amount of time it takes to build, validate and deploy new models and enables you to get the most value out of your predictive quality solution.

Supervisors

Gain operational visibility into production line quality through interactive dashboards and predictive alerts on quality parameters, scrap generation and excess raw material usage.

Engineers

Monitor production analytics, interpret and validate the supporting evidence and conduct root-cause analysis faster and identify process optimizations to increase efficiency on the factory floor.

Operators

React to actionable insights through predictive quality analytics allowing them to prioritize actions to prevent issues.

Quality Assurance

Rely on quality analytics to accelerate product testing by analyzing results faster and communicating to stakeholders more efficiently.
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