Helios IIoT Gets Introduced to the Market by Corrugated and Packaging Industry Press

The corrugated industry’s innovative AI and Machine Learning platform, Helios, has seen an impressive “pick-up” by the trade media covering the global packaging, corrugated converting, and printing industries.

Tailored specifically for corrugated, the OEM-agnostic platform is engineered to provide manufacturers access to robust, actionable insights into the performance of their machines. When building the platform, Helios was looking for a way to identify in “real-time”, and act upon, difficult-to-spot anomalies for human operators typically relying on intuition and experience alone.

The unique technology is built to minimize downtime, maximize profitability, and decrease the cost associated with unnecessary downtime and repairs. The user-friendly, accessible dashboard features remote monitoring, predictive maintenance, and anomaly detection. Remote monitoring provides deep insights into current and historical machine operation and performance that can be seen and accessed in “real-time” from any device. Predictive maintenance optimizes machine maintenance intervals using AI that adapts based on the machine operation and usage. Anomaly detection notifies users about abnormal machine states that allow operators to react to a potential issue before the failure occurs.

Additionally, Helios features preventative/proactive parts ordering, information about the exact time and cost of parts replacements, and the ability for operators to pinpoint the source of slowdowns and other issues. The operator-efficiency training helps machine operators learn and adapt to best practices. Additionally, the machine learning capabilities will mean that the platform only gets smarter the more data and user reactions it is able to process. More robust predictive analytics will be phased into the platform over time.

We are excited to see our new AI platform featured in world-renowned industry publications. To read our news, click on the links below.

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