How to Optimize Picking and Reduce Delivery Times by 30%

Picking: The Most Costly Link in the Logistics Chain

In any warehouse operation, the order preparation process -- known as picking -- is the most resource-intensive activity. This stage represents between 50% and 55% of a distribution center's total operating costs. A typical picking operator travels between 15 and 20 kilometers per eight-hour shift, and dedicates only between 15% and 20% of that time to the actual selection of products. The rest is consumed by travel, location searching and confirmation tasks.

The good news is that optimizing picking through specialized software -- specifically WMS solutions integrated with ERP platforms -- can reduce order preparation times by 30% or more, without requiring major physical infrastructure investments.

Why Traditional Picking Slows Down Deliveries

The three factors that most impact picking cost and speed are:

Travel distance. Travel time represents more than 50% of total picking time in most warehouses. Without a system that calculates optimal routes, operators walk redundantly through the same aisles multiple times to serve different orders.

Order complexity. Without ABC classification or dynamic slotting, high-turnover items may be located far from packing areas, generating unnecessary travel.

Labor flexibility. A picking strategy that works with 20 trained operators can collapse when 40 temporary workers are brought in during peak season. Methods that depend on individual warehouse knowledge do not survive demand spikes.

The 5 Picking Strategies Powered by WMS and ERP Software

1. Route optimization with intelligent algorithms. Advanced WMS systems use algorithms that analyze warehouse layout, each SKU's location and order volume to calculate the most efficient travel sequence. Through these technologies, it is possible to reduce travel distances by between 20% and 50%.

2. Batch and zone picking. Batch picking groups multiple orders into a single collection round, allowing the operator to serve several orders in one trip. Combined with zone picking, this minimizes internal traffic and reduces congestion. Doubling picking density can generate a minimum savings of 10 seconds per pick, translating into productivity gains of 38%.

3. Dynamic slotting. Dynamic slotting continuously adjusts item locations based on their demand. High-turnover products (Category A according to ABC analysis) are positioned in easily accessible locations near packing stations, significantly reducing search and travel time.

4. Real-time verification. Implementation of barcode scanners, RFID readers or voice systems connected to the WMS allows each selection to be validated at the moment it occurs. Warehouses using scan verification achieve accuracy rates above 99.7%, translating into fewer returns and greater customer satisfaction.

5. WMS-ERP integration. The real competitive advantage lies in bidirectional integration between the WMS and the ERP. When both systems share the same database, sales, inventory, purchasing and dispatch information flows without friction. The result: a synchronized supply chain from purchase order to last-mile delivery.

Measurable Results

Organizations that implement these strategies report consistent and measurable benefits:

Frequently Asked Questions

What is picking in a warehouse?

Picking is the process of selecting and collecting products from their storage locations to prepare an order. It is the most labor-intensive activity within a warehouse, representing between 50% and 55% of total operating costs.

By how much can delivery times be reduced by optimizing picking?

With the implementation of a WMS integrated with an ERP, organizations can reduce picking times by between 30% and 50%, which directly translates into shorter delivery cycles and greater customer satisfaction.

What are the most important KPIs in picking?

Key indicators include: picks per hour (productivity), picking accuracy rate (quality), order cycle time (speed), picking utilization rate (resource efficiency) and labor cost per processed unit (economic efficiency).