“We have areas that during certain shifts, depending on what the operation is, we don’t need 100 percent light in the area. So, we are able to program the lights in those areas and bring them down to 20 percent or 50 percent—or completely off,” he said.
Atlas uses the occupancy sensors to automatically turn lights on and off.
“The lights are smart enough that they know if there’s activity,” Tavares said. “You can program them to turn on if there’s activity in the area.”
The occupancy sensors and the reports generated from the data collected have also improved Atlas’ distribution. Tavares can track how much activity is in each aisle of the warehouse and see where the traffic is—where employees are going to pull product for distribution. Tavares positioned those in-demand products together in the warehouse.
Tracking occupancy “gave me the ability to consolidate product that is picked more often than others,” he said. “And rather than having people going from one side of the warehouse to the other, I can consolidate all of the busy products into a small number of aisles so all of the activity is in the same area.”
Doing that means Tavares lights only a section of the warehouse instead of multiple sections, further helping to reduce energy costs. Plus, it improves employee productivity and helps get the product out faster because workers don’t have to go all over the warehouse to get it.
With the lighting systems in all of the facilities, Atlas has saved 75 percent in lighting energy and gained 20 percent in productivity.
Monitoring equipment usage
Tavares also uses the data collected from the sensors to track equipment use.
“We have meters on each piece of equipment, and we track each one to see how often it is physically running. It allows us to see if we need a new piece of equipment or not,” he said.
That type of tracking prevented Tavares from having to buy a $500,000 piece of equipment. The team originally thought, based on employee reports, a new machine was needed to keep up with demand. The monitoring discovered, however, that the machine in the facility wasn’t being used to its full potential. There were spikes in usage followed by long periods of downtime, Tavares said. What they actually needed to do was shift some of the work and do better planning when assigning work to the machine.
“It’s easy for someone to say, ‘We’re busy. We’re full. We need a new piece of equipment to keep up.’ But once you look at the data on what the machine actually runs, you can see what is really happening. You can see opportunities to do a better job planning and make better use of that piece of equipment,” Tavares said.
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