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.
Gain operational visibility into current and projected scrap rates based on current production settings to make more informed decisions.
Monitor production analytics and conduct root-cause analysis faster to identify process optimizations that reduce waste.