The corrugated industry is experiencing rapid growth and increased demand as a result of the rise of e-commerce, and box plants are looking for more efficient ways to maintain output. In the short term, increasing uptime through longer operational hours and other traditional methods can help, but investing in a long-term, intelligent solution is required to truly impact productivity and profitability.
The corrugated industry is experiencing rapid growth and increased demand as a result of the rise of e-commerce, and box plants are looking for more efficient ways to maintain output. In the short term, increasing uptime through longer operational hours and other traditional methods can help, but investing in a long-term, intelligent solution is required to truly impact productivity and profitability.
Since the invention of corrugated box manufacturing machines nearly 150 years ago, optimizing machine performance has relied heavily on the experience, guesswork, and intuition of its operators. Machine learning-based predictive analytics can now predict when and how corrugated equipment will fail. The promise of advanced downtime notification allows plant management to make more informed decisions to improve the reliability and efficiency of their fleet.
One corrugated manufacturer with over 100 box plants around the world is poised for growth by reducing operational inefficiencies and improving its approach to reliability and maintenance through the use of machine learning. The industry-leading corrugated manufacturer recently piloted advanced predictive analytics powered by machine learning, with savings of nearly $125,000 per machine expected by mitigating only 20% of predicted machine downtime. Download the case study to learn more!
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