OUR PROVEN EXPERTISE IN MACHINE LEARNING & PREDICTIVE ANALYTICS

INCREASING STATION POPULARITY

Business success highly depends on predicting customer behaviors and patterns to figure out what they want, before they want it.

With over 850 live broadcast stations in 160 markets, iHeartMedia partnered with Aviana to optimize music programming operations using AI-based technologies. Aviana models predict song popularity and build recommended playlists tailored to specific genres, markets, and song mixes.

The result is an optimized, station-specific music programming model that reduces overhead and increases ratings.

AVOIDING COSTLY PRODUCTION DELAYS

Boeing’s quality of jetliners and other products partially depends on ensuring proper maintenance of the tools used to create them. Using tools that are beyond their safe tolerance range may require rework on airplane parts previously touched by those tools, causing expensive production rework and delays for customers.

Aviana designed a predictive analytics model for Boeing to identify out-of-tolerance and poor-performing tools. By applying the analytics model, Boeing was able to ‘pull ahead’ many out-of-tolerance tools that would have taken months to find. This saved Boeing potentially millions of dollars in rework if the tools had been used to their full calibration cycle.

ENSURING INTEGRITY OF SUPPLIER A/P PROCESS

Revenew provides Accounts Payable recovery and contract compliance audits for large manufacturing, energy and utility companies . They ensure compliance with commercial terms, recover lost monies and provide best practice recommendations for contractual and operational improvements.

Aviana implemented a machine learning model to use historical supplier audit results to detect patterns that indicate which invoices from the millions processed are most likely to result in a monetary recovery if audited. The model eliminates a manual review process for Revenew and at the same time increases monetary recoveries for Revenew’s clients.

“By applying the analytics model, we were able to ‘pull ahead’ a number of our out-of-tolerance tools that would have taken months to find. This saved the programs potentially millions of dollars in rework if the tools had been used to their full calibration cycle.”

– Martin Ohman, SOOT Mitigation Leader for Fabrication, Boeing.

FEATURED RESOURCES

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