Scrap Reduction Solution

Reduce excess scrap in your manufacturing process with scrap prediction models that allow operators to understand if machines will run within scrap targets.

Oden combines scrap reduction analytics with your production management experience to reduce scrap and material waste throughout your production process.

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Analyze Data

Analyze live and historical production data along with process data to determine factors in your manufacturing process that typically result in high scrap rates or material waste.
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Predict Scrap

Scrap prediction models are run against live production data to project scrap rates if operators continue with current process settings and output.
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Alert Teams

Real-time alerts are generated if projected scrap rates exceed scrap targets so operators or supervisors can quickly take action.

The Benefits of Scrap Reduction Technology

Provide A Live View Of Scrap

Provide a live view of scrap and allow operators, supervisors and engineers to understand the impact of decisions they make during the manufacturing process.

Minimize Scrap And Rework

Minimize scrap and rework by projecting scrap rates in real-time and making process adjustments throughout the manufacturing line.

Reduce Material Waste

Reduce material waste by minimizing variability in production processes and promoting material usage efficiency.

Maximize Your Bottom Line

Maximize your bottom line by eliminating inefficiencies within your standard operating procedures (SOPs) and alerting operators before scrap rates exceed unprofitable levels.

Avoid Overproduction

Avoid overproduction by consistently hitting yield targets and quickly identifying high scrap rates to reduce the amount of time spent producing defects.

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How Scrap Reduction Solutions Work

Machine learning algorithms leverage factory data from CAD drawings, machines and sensors along with quality systems to create a digital thread of your manufacturing processes.

Scrap is aligned with process data because these areas are typically measured differently and at different granularities. Data is then formatted for consistency, organized into a taxonomy and aligned with metadata such as product, shift or quality state.

Machine learning algorithms analyze live production data and compute real-time calculations to project the scrap of a run if operators continue production with current process settings and output targets.

Predictive scrap analytics can be used to identify excessive material waste or quality failures before they happen and highlight opportunities for improvement to improve efficiency and maximize the profitability of each production run.

Operators or shift supervisors are alerted when scrap predictions are trending outside of set targets minimizing human error. Engineers or shift supervisors can quickly identify the root cause of high projections and make adjustments in real time to eliminate scrap before rates increase to unprofitable levels.

Processes, data and bill of materials (BOM) change over time so predictive models must be updated to adapt to these changes. Oden’s patented platform supports continuous evolution of such models automatically to ensure that projections and recommendations are always accurate with little lag time.

A cloud-edge hybrid solution with minimal iteration duration – the amount of time it takes to build, validate and deploy new models – enables you to get the most value out of your scrap reduction solution.

Supervisors

Gain operational visibility into current and projected scrap rates based on current production settings to make more informed decisions.

Engineers

Monitor production analytics and conduct root-cause analysis faster to identify process optimizations that reduce waste.

Operators

Understand if production runs will stay within scrap targets and receive actionable alerts if scrap rates are trending too high.

Quality Assurance

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