Welcome to the New Peak Season Logistics Battleground: Decision Speed

If there’s one thing warehouses don’t lack, it’s data. In fact, e-commerce giants such as Amazon.com ingest upward of 50 million data points per week. Management can now access every statistic they could possibly want. Every pick, every pack, every movement, every location, recorded. What’s the average pick speed? Who is meeting productivity goals? Where are these 10 SKUs located in a facility of 10,000?

What was once a visibility problem is now a decision speed problem. Peak season winners aren't necessarily the companies that use data to create the most accurate forecasts or dashboards. They're the companies that can pivot fastest when reality diverges from the plan.

Why Decision Speed is Critical

The traditional approach to data analysis focuses on lagging indicators. For example, a retailer gets to the end of peak season, looks back on productivity by warehouse and discovers Facility A moved 5 percent slower than Facility B, leading to more stockouts in the region Facility A serves. Had that lag been caught before peak season sales started, or even early in the season, adjustments could have been made to support staff at Facility A, reduce their physical burden, and improve their productivity.

Hopefully those findings result in additional budget to increase efficiency next season — more staff, additional autonomous mobile robots (AMRs), etc. — assuming the findings don’t get lost in the deluge of data between January and July. Still, a new peak season comes with new dynamics. Forecasts are almost always wrong to some degree, whether demand spikes unexpectedly, labor productivity falls short, or inventory ends up in the wrong location.

Rather than simply trying to correct prior mistakes, retailers should glance into the future and plan for a range of outcomes. Once projections for next peak season are out, they should ask: How would I need to adjust operations in the event demand is 10 percent higher than expected? What if it’s 20 percent higher? Or even 10 percent lower? Logistics teams need to stress test for these contingencies well before demand picks up, and plan for how workers and flexible technology need to be moved throughout the facility — and even across the network — in each scenario.

When retailers gain this discipline during normal operations, they’re also better prepared to handle sudden disruption. The supply chain is fragile, and anything from hurricanes to flu season can impact product and worker availability. Businesses with predefined response plans are better positioned to quickly reallocate labor, inventory and automation resources before service levels suffer.

How Can Retailers Get Up to Speed?

Eliminating data silos is the first step. When the various solutions in a retailer’s tech stack can’t communicate with each other, it’s difficult for logistics teams to layer statistics and identify connections.

Retailers that don’t already have a data lake serving all their logistics tech should focus on adopting one. A data lake houses those thousands, even millions, of data points collected every day and gives retailers control over how the data is cleaned, structured and optimized. Teams can more easily collaborate on next quarter’s strategies, and artificial intelligence and machine learning solutions can leverage clean information as they assist in decision-making.

Collecting data alone is no longer enough because addressing problems after they’ve occurred can’t recover lost productivity or revenue. Retailers planning their 2027 strategy should consider how actionable their data is. Is it connected across systems? How is it being used to plan for contingencies? With those answers they can look for ways to quickly manage pivots with minimal disruption.

Jason Minghini is senior vice president of supply chain solutions at Kenco, a leading North American 3PL delivering custom, integrated logistics solutions that maximize supply chain efficiency.