Drive efficiency with AI and machine learning.

Paper, wood and metal fabricating producers are amongst some of the biggest sectors in the manufacturing industry. Global paper consumption is is approximately 500 million tons, and industries within wood manufacturing include a variety of products such as lumber, plywood, containers, flooring, homes, and buildings, are generating a constant demand for the product. The demand for metal has a forecasted worth over $200 billion by 2030, and its growth is largely driven by the growth in automotive manufacturing and electrical products.

In particular, wood and paper manufacturers face pressure to continuously reduce costs, as the demand for products are either stable or declining. This can be explained by the fact that many paper products are being recycled, thus decreasing demand to produce at a larger scale. As a result, the demand for manufacturers in these industries has significantly decreased, but the need to have efficient processes in place has increased.

AegisOne™ plays a crucial role in their efforts to improve production processes: having the ability to predict and prevent process-driven losses with AI-powered root cause analysis, and predictive analytics to continually optimize processes that translate to improved ROI.

Results and Outcomes

Improve the reliability and performance of your assets. Dynamically adjust to changing operating conditions and economics.

Our predictive insights inform sales leads for parts and services, increase revenue, improve asset performance, and drive customer satisfaction.

Drive operational efficiency and productivity.

Improve Overall Equipment Effectiveness (OEE), drive world-class productivity and enable continuous improvement with Industrial AI and machine learning. Further reduce process and product variation and leverage better insights for immediate action.

Improve the reliability of your product.

Detect and resolve anomalies and defects earlier in the process, ensuring world-class quality leaves the dock. Machine learning anomaly detection is a closed-loop detection process that improves first-pass yield, reducing raw material spend, scrap and non-value-add rework costs.

Optimize your labor and energy costs.

Decrease unplanned downtime and hit production targets, reducing the need for overtime or resources for production capacity surges. Predictable production drives more confidence in capacity planning and an optimized financial operating model.

Increase asset availability and reliability.

Improve the work balance of assets and reduce costs associated with idling, starving or blocking equipment. Financially optimize maintenance strategy, applying preventive, predictive and prescriptive maintenance where it drives the most value for the operation.

Turn Data into Outcomes

Want to drive intelligent operations?

Learn how AegisOne™ can help your business thrive with AI. Request a demo today!