04/16/2025

Modern Warehouse Challenges and Optimization

Learn how GEODIS addresses modern warehouse challenges through integrated optimization solutions. We boost efficiency by 15-60% while reducing labor costs and improving throughput.

There's never been a greater need for efficient warehouse operations. Today's brands and logistics providers face huge pressure to meet escalating customer expectations while controlling operational costs. As fulfillment demands grow more complex, traditional warehouse management approaches fall short—creating inefficiencies that impact your bottom line and customer satisfaction.

 

At GEODIS, we've developed an integrated approach to warehouse optimization that addresses these fundamental challenges. By combining strategic inventory placement with intelligent order processing, we're helping clients achieve efficiency improvements of 15-60%—even in already well-organized facilities. We can optimize any of the warehousing facilities that we run on your behalf. 

Key takeaways

 

  • Warehouse pickers typically spend 60-70% of their time walking between locations, creating significant opportunity for efficiency improvements
  • Traditional warehouse management approaches create inefficiencies in inventory placement, order batching, and space utilization
  • Workforce challenges intensify these issues, with fluctuating demand requiring precise labor management
  • The GEODIS integrated approach combines slotting optimization (strategic inventory placement) with wave pick optimization (intelligent order grouping)
  • This data-driven solution delivers 15-60% efficiency improvements by reducing travel time and maximizing productivity
  • Implementation takes just 2-3 weeks per component with minimal disruption to ongoing operations

Understanding today's warehouse challenges

Modern warehouse operations face mounting pressures from multiple directions. Customer expectations continue to rise, demanding faster delivery, perfect order accuracy, and complete product availability. Meanwhile, operational pressures require greater cost control and workforce optimization in an increasingly competitive environment.

 

The labor efficiency challenge

The most striking inefficiency in warehouse operations? Pickers can spend up to 70% of their working time simply walking from one location to another. This excessive travel time translates directly into lost productivity and substantial labor costs.

 

Labor management becomes even more challenging when dealing with fluctuating demand. Warehouses need enough staff to handle peak periods without maintaining excessive labor during slower times. This balancing act grows increasingly difficult as:

 

  • Customer expectations for same-day and next-day fulfillment continue to rise
  • Order patterns become more volatile and less predictable
  • Competition for warehouse labor intensifies
  • Training and retaining skilled workers grows more challenging

 

Limitations of traditional picking strategies

Most warehouses use one or more common picking strategies, but each has significant limitations:

 

S-shape picking strategy

This approach requires pickers to walk up and down entire aisles that contain picks, following an S-shaped path through the warehouse. While straightforward, it results in unnecessary travel when picks are concentrated in specific areas of an aisle.

 

Return picking strategy

With this method, pickers enter and exit each aisle from the same end, only visiting aisles containing picks. This works well when most pick locations are concentrated at one end of each aisle, but creates inefficient back-and-forth movement when picks are distributed throughout the warehouse.

 

Mid-point strategy

This approach divides the warehouse into two halves. Picks closer to the bottom half of the aisle are retrieved from the bottom cross aisle, while picks from the top half are retrieved from the top cross aisle. This strategy can outperform S-shape picking when pick density is low but becomes inefficient for higher pick densities.

 

Largest gap strategy

Pickers avoid the largest gap between pick locations in an aisle. While this reduces unnecessary travel within aisles, it requires more complex routing logic and can confuse pickers.

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Space usage constraints

Beyond picking efficiency, logistics providers face significant challenges in optimizing warehouse space:

 

  • Prime picking locations are often occupied by slow-moving inventory
  • High-velocity items may be scattered throughout the facility rather than positioned for efficient access
  • Dead stock and slow-moving inventory takes up valuable real estate
  • New product introductions require continual repositioning of storage
  • Seasonal fluctuations create temporary space pressures

 

Without data-driven approaches to inventory placement, these space usage issues persist and worsen over time.

 

Impact on business performance

These warehouse challenges directly affect your performance across multiple areas:

 

Fulfillment speed

Inefficient picking routes and poor inventory placement extend order processing times, potentially causing missed delivery promises and reducing customer satisfaction. As delivery timeframes continue to shrink, this impact becomes increasingly significant.

 

Operating costs

Excessive travel time translates directly to higher labor costs. When pickers spend most of their time walking rather than picking, costs per order increase substantially. In today's competitive environment, these inefficiencies can make the difference between profitability and loss.

 

Service level compliance

Suboptimal warehouse operations make it difficult to consistently meet service level agreements, especially during peak periods or when dealing with unexpected surges in demand. As customer expectations rise, maintaining service level compliance becomes increasingly challenging with inefficient warehouse operations.

 

Scalability limitations

Scaling warehouse operations to handle growth requires significant increases in labor and space when relying on traditional approaches. These limitations can constrain your business growth and ability to adapt to market changes.

 

The warehouse optimization opportunity

These challenges represent a significant opportunity for improvement. By addressing the fundamental inefficiencies in warehouse operations, you can:

 

  • Dramatically reduce picker travel time
  • Improve labor utilization and productivity
  • Optimize space allocation for inventory
  • Enhance order fulfillment speed and accuracy
  • Increase warehouse throughput without expanding physical infrastructure

 

Achieving these improvements requires moving beyond siloed optimization efforts toward an integrated approach that addresses both inventory placement and order processing together.

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The GEODIS integrated approach to warehouse optimization

Traditional warehouse management often treats inventory placement and order processing as separate functions. This siloed approach limits the potential for efficiency gains. At GEODIS, we've developed an integrated approach that combines two powerful, complementary optimization components.

 

Slotting optimization: The right item in the right location

Slotting optimization focuses on placing the right item in the right location at the right time. We use advanced data analysis to assign locations to SKUs based on movement patterns, order frequency, and operational needs. Our system:

 

  • Analyzes 52 weeks of rolling historical order data, refreshed weekly
  • Develops individual machine-learning models for each SKU
  • Identifies dead inventory (no orders in 6+ months) and slow movers
  • Ranks items based on movement velocity and operational importance
  • Ranks locations based on accessibility and proximity to outbound areas

 

This strategic approach to inventory placement ensures that your most frequently picked items are in the most accessible locations, significantly reducing travel time and improving picking efficiency.

 

Wave pick optimization: Minimizing picker travel time

While slotting optimization focuses on where items are placed, wave pick optimization addresses how orders are grouped and picked. Our system intelligently groups orders to minimize the total distance traveled by pickers. The process:

 

  1. Analyzes incoming orders and their line items
     
  2. Assigns pick types (batch, order, line) based on item characteristics and warehouse zones
     
  3. Groups orders based on parameters like carrier, delivery service, and ship date
     
  4. Creates optimal batches that minimize aisle visits and travel distance

 

Our pilot implementations show that this approach can reduce aisle visits per task by up to 47%, dramatically improving picking efficiency.

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The power of integration

Implementing these components together creates a complementary effect that dramatically improves overall warehouse performance. Good slotting without good waving still results in suboptimal picking groups, while good waving without good slotting means items aren't optimally placed. This integrated approach delivers improvements across several critical areas.

 

Inventory placement and management

Our slotting optimization identifies movement patterns and strategically positions inventory to:

 

  • Place high-velocity items in prime picking locations near outbound areas
  • Group complementary items that are frequently ordered together
  • Relocate slow and dead movers from valuable pick-front real estate
  • Adapt to seasonal changes and new product introductions

 

Order processing and fulfillment

Our wave pick optimization creates intelligent batches that:

 

  • Minimize aisle visits for each picking route
  • Maximize the items picked per location visited
  • Balance workload across available pickers
  • Adapt to changing order patterns throughout the day

 

Labor utilization

By reducing the time pickers spend walking, our solution directly improves labor efficiency:

 

  • Reduces unproductive travel time between pick locations
  • Increases the units per hour processed
  • Decreases task count while increasing items per task
  • Enables more predictable workforce planning

 

Space optimization

Our approach helps maximize the value of your warehouse space:

 

  • Identifies and relocates dead/slow inventory occupying prime space
  • Optimizes location assignments based on item characteristics
  • Supports multiple storage types and picking zones
  • Reduces congestion in high-traffic areas

 

Technology foundation and continuous improvement

Our warehouse optimization combines advanced analytics with our deep operational knowledge:

 

  • SKU forecasting models: Individual machine learning models for each SKU predict future demand patterns and optimize inventory placement
  • Location ranking methodologies: Sophisticated algorithms rank warehouse locations based on accessibility, proximity to outbound areas, and operational factors
  • Wave optimization algorithms: Advanced mathematical models group orders to minimize total travel distance while meeting operational constraints
  • Performance dashboard: A comprehensive PowerBI dashboard provides deep insight into SKU-level data, weekly predictions, and optimization results

 

GEODIS builds continuous improvement into everything we do. Our optimization system continuously analyzes order patterns and inventory movement, strategically positions inventory, intelligently groups orders, tracks performance, and adapts as conditions change.

 

This data-driven approach delivers remarkable results. We've seen efficiency improvements ranging from 15%-60% depending on current warehouse organization. Even well-organized facilities see 15-20% gains, while less optimized operations experience even more significant improvements.

 

For a deeper dive into our optimization methodologies, read our article "Warehouse Optimization: Slotting & Wave Pick Improvement," where we explore the technical aspects of our solution in greater detail.

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Explore our warehouse optimization series

Want to learn more about our warehouse optimization approach? Explore these in-depth articles:

 

How GEODIS can help

Our warehouse optimization solution delivers significant efficiency improvements with minimal implementation effort. We can optimize any of the warehouses that we operate on your behalf: 

 

  • Quick implementation: Each component (slotting and wave optimization) takes just 2-3 weeks to implement
  • Minimal disruption: Implementation designed to work alongside existing operations
  • Continuous refinement: Ongoing analysis and adjustment to maintain optimal performance
  • Comprehensive support: Our team guides you through each step of the process

 

We tailor our approach to your specific needs:

 

  • Custom business rules for wave planning
  • Adjustable batch sizes matched to your picking process
  • Flexible volume thresholds aligned with packaging requirements
  • Custom pick zone prioritization based on your warehouse layout

 

The result? Reduced labor costs, increased throughput, improved service level compliance, and better space utilization—all without the need for significant physical changes to your warehouse. Get in touch with GEODIS. 

Contact us

FAQs on warehouse challenges and optimization

Warehouse pickers typically spend 60-70% of their working time walking between pick locations. This excessive travel time represents one of the most significant opportunities for efficiency improvement in warehouse operations.

Traditional strategies like S-shape, return, mid-point, and largest gap picking all have significant limitations. S-shape and return methods often result in unnecessary travel, the mid-point strategy becomes inefficient with higher pick densities, and the largest gap strategy can confuse pickers with its complex routing logic.

Poor inventory placement leads to excessive travel time, inefficient space utilization, and reduced picking productivity. When high-velocity items are scattered throughout the warehouse rather than positioned for efficient access, pickers spend more time traveling and less time picking.

Slotting optimization is the process of strategically placing inventory based on movement patterns, order frequency, and operational needs. It ensures that high-velocity items are in prime picking locations, complementary items are grouped together, and slow/dead movers are relocated from valuable pick-front real estate.

Wave pick optimization is the process of intelligently grouping orders to minimize picker travel time. It creates batches that maximize picking density and minimize aisle visits, significantly reducing order fulfillment time and improving labor efficiency.

An integrated approach combining both slotting and wave optimization delivers complementary benefits. Good slotting without good waving still results in suboptimal picking groups, while good waving without good slotting means items aren't optimally placed. Maximizing efficiency requires both systems working together.

Each component (slotting and wave optimization) typically takes just 2-3 weeks to implement. The system is designed to work alongside existing operations with minimal disruption, allowing for quick realization of benefits.

Our pilot implementations have shown efficiency improvements ranging from 15% to 60%, depending on current warehouse organization. Even well-organized facilities see 15-20% gains, with less optimized operations experiencing even more significant improvements.

Our system analyzes 52 weeks of rolling historical order data, refreshed weekly. This continuous analysis allows the system to adapt to seasonal changes, new product introductions, and evolving order patterns, ensuring optimal performance even as conditions change.

No. Our solution works with our existing warehouse layout and infrastructure. While it may recommend relocating some inventory based on movement patterns, it doesn't require significant physical changes to warehouse setups.

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Paul Maplesden

Lead Content Strategist

Paul deeply researches logistics and supply chain topics to create helpful, informative content for our US audience. Read Paul's work in the GEODIS blog, our in-depth GEODIS Insights reports, and our case studies and white papers.
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