Three Ways AI and Machine Learning Can Help Solve Your Labor Shortage Issues

Industries heavily dependent on manual labor, such as manufacturing and corrugated, are struggling to fill open positions. One reason for the shortage is the difficulty of finding and training new workers. Artificial Intelligence (AI) and Machine Learning (ML) are helping to address these challenges.

There are no magic bullets for staffing issues, but when used properly, AI and ML can help keep operations running smoothly by reducing the dependence on highly specialized staff that are increasingly spread thin and hard to replace.

It’s a myth that Artificial Intelligence and Machine Learning mean replacing human jobs with robots. In the corrugated space, there is still a considerable role for human oversight and direction, as well as the upkeep of machines. However, integrating AI and Machine Learning ultimately leads to greater efficiency in operations by maximizing output, minimizing downtime while intelligently monitoring routine equipment maintenance needs.

Machine Learning and AI for Corrugated

  1. While running thousands of kicks worth of throughput daily, each machine generates tens of thousands of data points. A purpose-built solution for the corrugated industry, Helios helps predict when machines are in need of maintenance or at risk of experiencing bigger problems. Within all these data points, certain idiosyncrasies, or anomalies, take place. Helios can, over time, begin to predict when a machine needs maintenance or is at risk of larger-scale failure.  With knowledge of potential issues coming to light before they happen, Helios effectively stops them from becoming real problems. The real-time insights Helios provides can make all the difference in keeping box plants on track and performing optimally.
  2. Access to these data and insights decrease an operation’s dependency on individuals with very specialized institutional knowledge, experience, and training. In an operation that is not outfitted with a corrugated manufacturing monitoring system, the pressure would fall squarely on the shoulders of experienced technicians to optimize machine throughput and to detect and adjust maintenance-related issues. With these individuals increasingly difficult to find, keep, and train, many box plants are not performing at the highest level, or are at risk for major malfunction.
  3. With real data to inform the replacement of parts and maintenance, the operation can save money by eliminating dependence on recommended maintenance intervals for components.  All of this can be monitored from anywhere, in real-time, meaning once again, no need for dozens of specialized on-site experts to constantly check each component of each machine to make sure it is functioning properly and at optimal capacity.

Each of these factors leads to overall increased operational efficiency and accurate maintenance monitoring means higher margins and a larger bottom-line.

Get Started Today

Help decrease your operation’s reliance on the hard-to-find specialized oversight staff and continue to support your dedicated team by leveling up your operational intelligence through Helios AI. This is an exciting time in the industry, especially when using data and leveraging machine learning to improve decision-making. Contact Helios AI today to learn more about how we tailor our insights to your operation.

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