E-commerce retailers digging out from five years of COVID and inflation got a rude awakening in April, with 10-50% U.S. tariffs on most imports from most countries, as well as elimination of the de minimis tariff exemption for low-value shipments.

Beyond the direct costs are indirect factors that affect sales: renewed concerns about inflation and a potential recession, demand and price volatility delaying purchasing and investment decisions; constrained supplies and higher prices for key inputs; and scaled-back carrier services at higher rates.

Mixed signals from the market and unpredictability from tariffs leave retailers with little ability to predict or plan, and few levers of real control. At most they can strengthen key performance indicators (KPIs) and build internal resilience to risk.

“Companies have pulled forward orders, buying in larger quantities because of the tariffs, but demand is nearly impossible to predict,” says Jennifer Katz, senior director of strategy and special products with cartonization software provider Paccurate. “They want to increase productivity to reduce their operating spend on labor, materials, freight — but their capex through 2025 is frozen due to uncertainty.”

Katz sees the warehouse as centrally positioned in the supply chain. As such it offers previously untapped potential for efficiencies and cost savings to offset across-the-board risk and complexity.

1 Enhance SKU Data to Improve Packing Outcomes

However tariffs play out, their impacts will be shared among suppliers, vendors, retailers and their customers. Where costs are absorbed, the priority will be finding savings elsewhere.

Retail’s thin margins, especially in e-commerce, require accurate stockkeeping unit (SKU) dimensions and weights to optimize cartonization and packing. This helps in managing parcel freight costs under complex dimensional weight (DIM) formulas, which vary by carrier and construct parcel freight rates by balancing offsetting volume and weight impacts on loading and handling efficiencies.

Identifying high-velocity SKUs for priority slotting close to packers helps shorten cycle time. Following SKU-specific packing rules for nesting, stacking, compressing or separating fragile items to avoid damage without excessive space or fill, also rely on data/SKU accuracy.

“SKU-level improvements can really make a difference,” Katz stresses. “The ultimate goal is to save on freight, which is where the real money is, but packing optimization also shortens cycle time, which can shorten or eliminate a second shift, meaning you may only need 16 packers versus 21 — a big savings before you even get to freight.”

2 Optimize and Automate the Pack

Despite concerns about a “skills gap” as older, experienced warehouse workers retire, a neutral, data-driven approach can remove personal biases and faulty assumptions from the packing process.

Analytics based on years of data gathered from shipments and clients allow warehouse operators to dynamically prioritize and optimize the pack against varying priorities like cost, speed or sustainability. Real-time packing guidance eliminates time spent choosing boxes, while reducing void or fill.

Seconds matter, especially for multi-item orders. Katz estimates typical improvements of around 40% in cycle-time can be gained by shifting from “tribal knowledge” to automated, 3D visualizations and instructions. An optimized pack also supports more efficient building of pallets and loading of trucks.

3 Connect and Integrate the Warehouse Ecosystem

The warehouse’s role has been elevated in the direct-to-consumer (D2C) retail environment, yet warehouse data management and integration haven’t kept pace. This has led to gaps in visibility and performance as demands on supply chains for time-definite delivery, SKU-level tracing and sustainability intensify.

Many APIs aren’t fully integrated with each other or with their WMS platforms. If, say, an automated storage and retrieval system (ASRS) and pack station aren’t connected, undetected inbound order or pick errors can create problems downstream, triggering incorrect orders, customer complaints, and added labor, materials and postage, or penalties for orders that need to be redone.

Prioritizing pick sequence and pack locations based on historic data cuts travel time for better performance. 3D visualization of the pack relieves less experienced or temporary workers of confusing carton, packing or labeling choices. Upstream order verification catches errors early to avoid repacking.

4 Leverage Data Analytics and AI for Actionable Insights

The right analytics deliver synthesized insights that quickly cut through the data clutter so companies can focus on relevant signals amid the noise. Managing the raw internal and external data needed to develop insights and balance complex tradeoffs in real time is increasingly beyond the capabilities of manual processes.

A fluid tariff landscape, for example, will necessitate close monitoring of rates across countries and products, a full understanding of total cost impacts, and an ability to quickly implement mitigating cost reductions in operations.

The same underlying analytic capabilities are also instrumental in managing compliance with sustainability initiatives, retail partners’ packing and loading practices, government regulations and other non-core objectives. Resulting efficiencies shave critical time and cost in the process.

5 Minimize Waste and Damage

Sustainability regulations will increasingly require new digital capabilities in automation and optimization to deliver on a broader supply chain value proposition.

Automating pack sequencing according to SKU-specific rules, for example, reduces the risk of damage. Determining the right mix of boxes, bags or mailers and optimizing packing reduces the need for void fill, corrugated consumption, protective packaging, and tape. Nesting, stacking and arranging products for a smaller carton means more efficient load-planning, fewer trips, and less fuel consumed.

Reliable data, reporting KPIs, and SKU-level traceability deliver offsetting performance efficiencies, along with cost savings in energy, materials, and labor.

Paccurate Integrates the Supply Chain’s ‘Messy Middle’

Paccurate, the packing intelligence platform, was launched in 2018, in response to the growing use of dimensional weight (DIM) pricing by parcel carriers.

Paccurate’s PacAPI cartonization engine moves beyond the “liquid fill” and “3D cube” algorithms used in conventional warehouse and order management systems, which are based on cubic volume alone. Rather, it factors in total cost-to-ship based on size, weight, labor and materials against DIM formula tiers to highlight breakeven points in shipment pricing.

Paccurate’s Platform turns this data into 3D packing visualizations that simplify and speed workflow for people and automation. Shippers can use historical data to simulate different packing strategies, helping identify their ideal mix of boxes, bags or mailers. Using the platform's prescriptive analytics, shippers are able to benchmark their operation and get a clear understanding of their packing health, to remain compliant, stay competitive and reduce risk.

Data collected by Paccurate from initial pick to last-mile delivery closes significant gaps in visibility, and reduces costs in e-commerce fulfillment, to build resilience and free up working capital for retailers, brands and 3PLs.

Resource Link: https://paccurate.io/