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Mind the gap: Finding hidden efficiencies in warehouse management

In a warehouse, time and space are valuable assets. The process of tracking, analysing and improving those assets is a constant cycle, but without the right data is impossible. Data blind spots take a toll on efficiency, but one of the most obvious remains the most untapped: the time during a pallet move.

Visibility is valuable
Motorway speed checks used to suffer from a similar lack of visibility. To track a vehicle’s speed, law enforcement would record a vehicle’s speed at one checkpoint and measure it again at a following checkpoint. Theoretically, a driver could pass the checkpoint at the legal limit, accelerate once they had passed and decelerate before the next checkpoint. From the – inevitably limited – point of view of law enforcement, that driver would not have breached the speed limit. There was simply no way to know the driver’s actions between those two checkpoints. The same is true of pallet management. If you only monitor the point of pickup and deposit, you are blind to what happens in between.

Modern speed cameras have changed this process. So-called ‘average speed’ cameras spaced along a road monitor each vehicle’s licence plate before calculating the average speed and determining if a driver has broken the legal limit. The data exists, the visibility is there. For law enforcement, average speed cameras have become a critical part of road safety infrastructure. For warehouse managers, this level of visibility holds substantial potential for performance improvements, but only a few have recognised its prospects.

The cost of driver inefficiencies
For example, in a medium sized warehouse it could take one driver one minute to pick up a pallet, drive across the floor and deposit the pallet in a racking shelf. You can’t be certain of optimal performance because there is no data from the driver’s activities in that time; you can only be sure the pallet has been picked up and put down.

In that minute, any number of scenarios could have affected the driver’s efficiency. They could have:

  • taken a scenic route
  • had to manoeuvre around something that was blocking them
  • had to wait for barcodes to scan properly
  • incorrectly placed pallet in wrong shelf and had to move
  • stopped to speak to a colleague

Without data about that minute, one or all of the above could have happened. And there would be no way to know. If that driver goes on to shift 160 pallets a day, you might face around 2.7 hours where performance improvements are possible, but unknown. Multiply that by just three drivers and that’s a shift’s worth of time to optimise.

Add in the Impossible Triangle, the truly ‘lost’ hours, and you face a wealth of performance improvement possibilities.

How to track performance
Collecting data in the gap between pallet pick up and deposit needs more than just location data from warehouse vehicles. By placing nodes around the warehouse, on the forks of trucks, racking shelves, and drivers themselves, a central system can monitor not only where each vehicle is, but also whether the driver is where they should be, if they are prohibited by anything else, and monitor where exactly pallets are placed. The data, fed back to a system integrated tightly with the Warehouse Management System, can help you identify where and when performance is affected.

For example, if drivers are routinely held up in one location, you might discover some issues with your warehouse layout. If pallets are routinely placed in the wrong racking shelf, you might investigate whether you need additional signage or more driver training. Problem areas can be fixed quickly and with a constant feed of real-time data, you can quickly see which changes have worked and which have not. Consider using the metrics of the Impossible Triangle to track the reduction of lost hours as you close the data gaps and break open blind spots.

Find your data blindspots
No matter how seemingly invisible, there are always performance improvements to be made. As efficiency pressures increase, it’s vital to look at what you can’t see, as well as the standard metrics and performance indicators that are all too familiar. Visibility is valuable, but precision location is profitable – provided you know where to look.

Author Details
Independent Author
Author Details

Amarnath Gupta is a visionary digital transformation leader with over two decades of experience guiding Fortune 500 organizations through enterprise-wide innovation. He has built and scaled Microsoft Dynamics 365 practices into $7.5 million revenue engines, rescued high-risk global implementations, and delivered 35 percent operational efficiency gains, 40 percent faster go-lives, and 30 percent cost optimizations across industries from manufacturing to healthcare and construction.

His passion for marrying deep technical command in Dynamics 365, Azure AI/ML, and Power Platform with strategic P&L governance has spawned proprietary IP solutions like JewelPro™ and OmniClaim Sentinel™. A catalyst for modern AMS frameworks, he leverages predictive KQL analytics and intelligent support automation to slash incident resolution times by 30 percent and cut costs by up to 30 percent.

Amarnath writes about practical strategies for data-driven decision making, end-to-end ERP/CRM implementation best practices, and the future of cloud-native architectures. His work empowers readers to transform underperforming units into high-growth engines while embedding Agile/DevOps and Zero Trust security into every layer.

  • Microsoft Dynamics 365 F&O, CE, Commerce, Field Services
  • Azure AI/ML integration and predictive analytics
  • Enterprise Application Maintenance & Support (AMS)
  • Agile/DevOps delivery and operational excellence
  • Data modernization and cloud transformation

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Independent Author

Amarnath Gupta is a visionary digital transformation leader with over two decades of experience guiding Fortune 500 organizations through enterprise-wide innovation. He has built and scaled Microsoft Dynamics 365 practices into $7.5 million revenue engines, rescued high-risk global implementations, and delivered 35 percent operational efficiency gains, 40 percent faster go-lives, and 30 percent cost optimizations across industries from manufacturing to healthcare and construction.

His passion for marrying deep technical command in Dynamics 365, Azure AI/ML, and Power Platform with strategic P&L governance has spawned proprietary IP solutions like JewelPro™ and OmniClaim Sentinel™. A catalyst for modern AMS frameworks, he leverages predictive KQL analytics and intelligent support automation to slash incident resolution times by 30 percent and cut costs by up to 30 percent.

Amarnath writes about practical strategies for data-driven decision making, end-to-end ERP/CRM implementation best practices, and the future of cloud-native architectures. His work empowers readers to transform underperforming units into high-growth engines while embedding Agile/DevOps and Zero Trust security into every layer.

  • Microsoft Dynamics 365 F&O, CE, Commerce, Field Services
  • Azure AI/ML integration and predictive analytics
  • Enterprise Application Maintenance & Support (AMS)
  • Agile/DevOps delivery and operational excellence
  • Data modernization and cloud transformation

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