Logistics Turnover, Staffing Costs Driving Demand For Intelligent Robots

By Herbert ten Have

According to the U.S. Bureau of Labor Statistics’ most recent data, the average salary of U.S. workers in the warehousing and storage industry (just one area where automation can ease labor concerns) is $43,820 USD; the organization estimates annual warehouse turnover rates at 43%. The cost to replace an employee can reach 25% of their annual salary, meaning these costs add up quickly, which is why more companies are turning to automation.

Sean Wallingford, president and chief executive of the Americas region for warehouse technology company Swisslog Holding AG, told the Wall Street Journal, “You’ve got the combination of not enough people and very expensive people. It makes the automation much easier to justify. So I think we’ll continue to see that push toward it.”

Similar staffing problems are happening across a wide variety of industries, which explains why attendance at Manifest—an event with a heavy automation focus—doubled year-over-year; more than 3,000 people attended the most recent show. According to panelists in one event session, the industries most interested in robotic automation include “apparel, e-commerce, food and beverage, and fulfillment.”

Part of the difficulty attracting talent for these positions is the type of work. It’s often physically taxing with heavy, repetitive lifting, and in the case of trailer unloading, the working environment is often poor, with overly warm or cold temperatures and uncomfortable surfaces in which much of the work takes place. Workers and employers have rightly come to prioritize their long-term health. Robots are perfectly suited for this type of work.

How you implement the change is critical:

There’s more to embracing automation than purchasing and installing a few robots. Successful automation implementations require forethought and planning, not just in terms of identifying tasks that robots can handle, but also determining how best to integrate them with current and future employees and systems. Saskia Nijs, an expert on the future of work and advisor on combining human potential and technology, explained this concept in an interview posted to her website:

“Don’t ‘just’ start automating because it is possible to do so, because it’s easy and because it saves on people. If there are so many tasks that technology can take over just fine, then you need to think about what you want your people to do. That can really work out very well, by the way, because nine times out of ten the tasks that ‘remain’ after automation are much more fun for people to do.”

Once those issues have been settled, companies can turn their attention to selecting the right automation tools, including the robots, grippers, cameras, sensors, and most importantly, the brain.

The brains of the operation:

Those exploring automation may believe that whatever robots they choose for their jobs will be perfectly suited for those tasks. But, unless the robot will do the exact same task in the exact same position every time, unlocking their robots’ full potential requires a brain capable of seeing, perceiving, accounting for variances, learning, and acting effectively. Not all brains are created equal.

The brain must be able to process images to determine object dimensions, the best place to grab the object, the right gripper settings (vacuum force, number of suction cups, acceleration and deceleration, etc.) to grab and place the object, and the order in which to pick objects that are bundled together. Humans are outstanding in these scenarios. We are innately capable of generalizing objects, situations, exceptions, grasp capabilities and constraints. We understand inertia and dynamics, the difference between fixed and deformable objects, and can decipher weight distribution via force feedback from our hands. Robots do not have these innate abilities and must be given the appropriate instructions.

The best vision AI offerings use advanced methodologies and algorithms to enhance the experience through supervised machine learning. They run algorithms on state-of-the-art neural networks to extract essential information gathered by the system’s cameras and deliver highly precise instructions to the picking robots for unparalleled picking capabilities.

Many robotic adopters view a 75%-85% successful pick rate as acceptable, but that’s far from optimized and requires too much human intervention. A 95% successful pick rate on even the most complex picking tasks should be the minimum threshold for choosing a brain to operate autonomous systems. Afterall, to move beyond the labor limitations currently hampering many industries, automation needs to all but eliminate the need for human intervention.

If it’s worth doing, it’s worth doing right:

The potential for automation to relieve staffing problems clearly has piqued the interest of many industries. While many automation companies offer promises of quick, seamless implementation, the stakes are high; buyers should take the time to lay a solid foundation for success. After all, if automation is worth doing, it’s worth doing right. Proper automation can help reduce turnover rates to reduce the revolving door reality taking place today and provide new opportunities for workers. New adopters must have a plan in place for implementation and optimization before settling on their automation solution of choice. These two often-overlooked challenges can hamper any automation initiative’s success.

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Herbert ten Have is Co-founder and Director, Strategic Alliances at Fizyr, which provides the smartest and fastest vision AI available for robotic systems. Contact Herbert at h.tenhave@fizyr.com.

Quelle: www.fizyr.com