If you’ve ever noticed that your car gets substantially worse mileage per gallon than was advertised at the dealership, then you understand why predictive and preventative maintenance are so essential to warehouse operations.
In the past, warehouses and distribution centers would rely upon scheduled and reactive maintenance to keep equipment humming, according to Bruce Muir, director of distribution automation at MHI member Sick, Inc.
Based on manufacturers’ recommendations, warehouses would automate their scheduled maintenance, having work orders sent to technicians when equipment reached predetermined points in its service life. It’s the equivalent of changing your car’s oil every 3,000 miles, Muir said.
But just like in the automotive world, a warehouse’s mileage may vary when it comes to material-handling equipment. Like cars, warehouse equipment may have been tested under ideal circumstances, skewing its performance results.
Under real-world conditions, like operating in a busy warehouse or driving on public roadways, the manufacturers’ recommendations might prove wildly off base.
Key pieces of equipment may be serviced too often, increasing costs and burdening short-staffed maintenance teams, or they might be serviced too rarely, leading to extra wear and tear on components and increasing the likelihood of a breakdown that results in costly downtime.
When scheduled maintenance proves insufficient, warehouses often end up in reactive mode. Technicians repair or replace equipment once it’s broken down or its performance has deteriorated noticeably, while warehouse managers stare at the clock and calculate in their head how much that downtime is costing the company.
That’s why a growing number of warehouses are turning to sensor technology that enables predictive and preventative maintenance, an important strategy for reducing maintenance costs and maximizing uptime, Muir said.
Instead of scheduling maintenance around equipment’s mean time to failure, warehouses are using sensors to perform condition monitoring of their equipment, he said. A simple example: Sensors tied into a maintenance dashboard can monitor the temperature and vibration of the gearbox inside a piece of equipment.
If those data points deviate from historical norms, suggesting there’s a problem and failure may be on the horizon, technicians will be alerted so they can take action immediately. Over time, the company likely can establish a mean time to failure for its equipment that is much more accurate than the manufacturer’s recommendations because it takes into account that particular warehouse’s operations instead of relying on generic testing.
Muir said Sick has clients that run conveyor systems nonstop, using three shifts of labor each day, while others run only one shift except for peak season. Not surprisingly, those warehouses have different maintenance needs, and sensors monitoring the performance of those conveyor systems will detect slight changes that are imperceptible to the human eye.
“Predictive maintenance is allowing us to customize your maintenance paradigm based upon the actual usage and site conditions,” Muir said. “We put temperature gauges and accelerometers on equipment and collect all that data in order to make systems smarter.”
Small operations can monitor a dashboard with this information and simply respond to it as issues arise. But for large operations, artificial intelligence and machine learning provide a meaningful advantage, sorting through data and establishing a list of system priorities so maintenance supervisors can best allocate their resources to keep the system running, Muir said.