Raven is a manufacturing technology leader that helps manufacturers empower the frontline and improve operational performance with our continuous improvement and OEE monitoring software.

A third of capacity is lost each year to solvable inefficiencies, costing the manufacturing industry $650B. While manufacturers strive to measure and improve OEE, the fact is that 68% of manufacturers don’t trust their plant data — with key data points being lost as a result of manual processes for capturing downtime reasons. This missing data – or “context” – prevents manufacturers from driving productivity improvements, and by extension, millions of dollars in potential cost savings.

Join this webinar panel led by Dr. Paul Turner, COO at Raven and former Vice President I4.0 Applications & Analytics at Stanley Black and Decker, for a deep dive into why understanding downtime reasons is critical for root cause analysis and driving OEE improvements in the plant, and how automated contextualization can solve this critical gap on the factory floor.

The Missing Piece for OEE Optimization - Automated Contextualization

Leading manufacturers are accelerating their digital transformation efforts to improve performance, stay competitive and make their mark in the smart manufacturing ecosystem.  Today, those who are capturing machine and sensor data gain some understanding of their plant’s efficiency. But there’s so much more useful,  meaningful information to look at using this data. To tap into the details, context is key.

The transformative, smart manufacturing method for recording contextual information is role-based, intuitive and pinpoints the actual root cause of production inefficiencies and issues across your plant.

To help you get started with contextualizing data, Martin Cloake of Raven.ai and Mike Ungar of FocalPoint breakdown practical problem solving for manufacturers and how you can function with efficiency using Raven’s Contextual Time platform.

How to Accelerate Digital Transformation with Practical Problem Solving

Research shows that 68% of manufacturers don’t trust their data enough to get tangible, measurable value from it — and 92% of data sets that were analyzed had bad data which negatively impacted the accuracy of insights (Accenture). The result? Unreliable data, low OEE metrics, high labor costs, increased downtime, and unmotivated employees.

Improving OEE data with OEE monitoring software

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