Order Batching: What It Is, Common Methods, and Benefits

Order picking eats up more than half of labor time in most warehouse operations. Ignoring this drains productivity, inflates labor costs, and frustrates teams trying to meet rising customer expectations. Acting on that inefficiency matters.

Order batching solves this by grouping orders in such a way that reduces movement, improves the picking process, and helps maintain a steady order processing and fulfillment process. This article explains how the batching process works, breaks down its strategic advantages, and shows how to apply it for better results.

Introduction to Order Fulfillment

Order fulfillment is a cornerstone of supply chain management, encompassing the entire process from receiving customer orders to delivering the final product. Efficient order fulfillment strategies are essential for meeting customer expectations and controlling operational costs. Within this process, the picking process stands out as one of the most labor-intensive activities, often accounting for more than half of all labor activities in a warehouse. Many warehouses rely on manual order picking systems, where workers physically collect items from storage locations. By implementing effective order batching procedures, warehouses can group multiple orders into the same batch, significantly reducing travel distance and minimizing total picking time. This approach not only streamlines manual order picking but also enhances overall warehouse productivity, ensuring that customer orders are processed quickly and accurately.

Understanding Order Picking

Order picking is the process of collecting specific quantities of products from a warehouse to fulfill customer orders. As a critical part of warehouse operations, order picking directly impacts the speed and accuracy of order fulfillment. The most common approach in many warehouses is manual order picking systems, where pickers move through the facility to collect items for customer orders. To improve efficiency, various order batching methods are used. Batch picking involves grouping multiple orders into the same batch, allowing pickers to collect items for several orders in a single trip. Wave picking, another popular method, releases batches of orders at scheduled intervals, helping to balance workloads and optimize the picking process. By leveraging these order batching methods, warehouses can reduce unnecessary movement, streamline operations, and ensure that customer orders are collected and processed efficiently.

What Is Order Batching?

Order batching groups multiple orders into a single batch for the picking process, rather than handling each order one by one. This process of batching orders allows more orders to be processed per trip, increasing throughput and efficiency. By forming an order batch, different orders are grouped together to improve operational performance and reduce redundant movement. That shortens total travel time in a warehouse by reducing back-and-forth movement along left and right aisle paths, with minimizing travel distance being a key benefit of batching.

Pickers follow a routing strategy based on order data, collecting items for all assigned orders in one trip. The layout of a rectangular warehouse with parallel aisles and cross aisles, including two cross aisles (a front cross aisle and a back cross aisle), significantly affects the batching and picking process by facilitating efficient navigation and route planning. Warehouse selection is also crucial in optimizing order batching and routing, as the right layout can enhance picker movement and reduce travel time. Teams often start with a seed order, followed by others that fit capacity, such as a fourth-order. Seed methods use a seed order and then incorporate other orders to complete the batch, serving as a heuristic approach to batch formation. When forming batches, different orders are grouped together in the batching process to maximize efficiency and resource utilization.

Smart systems release a new batch only after the first batch is complete. The release program plays a key role in managing batch releases, ensuring that workers process multiple orders efficiently and avoid congestion. Doing so avoids clutter and ensures smooth flow. The algorithm consists of batching rules like priority, SKU proximity, and volume limits to avoid an optimization problem. Order processing is optimized through batching, as orders are systematically grouped and managed for picking.

Using batching strategies supports operational efficiency and improves the fulfillment process. Batching orders enables more orders to be batched together, increasing throughput and reducing picking complexity. It works best when businesses process combined orders daily and must maintain efficiency under time pressure and rising customer expectations. Minimizing travel distance is a primary advantage, as it directly impacts labor costs and fulfillment speed. Metaheuristic algorithm approaches, including genetic algorithms and tabu thresholding, are often used to solve complex batching problems. Routing strategies frequently draw on the traveling salesman problem to optimize picker paths. Testing these algorithms on real warehouse instances ensures their effectiveness in practical scenarios. Joint order batching combines batching, routing, and assignment tasks for integrated optimization.

Order batching problems are a key area of optimization, involving challenges in grouping, sequencing, and routing. Comparative study, recent approach, most recent approach, state of the art, and authors proposed are all referenced in the literature to highlight advancements in order batching. The focus of current research is on improving efficiency, and future paths include exploring new methodologies and open research questions in order batching optimization.

Common Order Batching Methods

Every operation requires a unique batching strategy based on layout, order volume, and delivery windows. Effective warehousing strategies, including layout optimization and order batching, are essential for efficient picking operations. Below are proven methods used in real-time picking operations:

• Discrete Batching: Group batch orders picked together while keeping each order separate during packing for faster sorting and accurate order status tracking using predefined order-adding rules. These methods are key components of the pick pack and ship process in modern fulfillment operations.
• Wave Picking: Orders get picked in waves, based on shift schedules, release programs, or shipment deadlines, to handle time windows and workload balance while avoiding sequencing problems in high-volume environments.
• Zone Batching: A picker assigned to a zone grabs all needed items for customer orders in that zone to simplify zone picking and cut time while maintaining enough capacity for efficient routing.
• Cluster Picking: One picker uses a cart to collect items for several orders, following an optimized path that minimizes distance using a time-saving algorithm designed to solve the vehicle routing problem.
• Batch by SKU: Items with the same batch or SKU are picked together to avoid repeating trips to the same location, saving time and maximizing maximum capacity for the route.

Choosing the right batching strategies depends on warehouse layout, order volume, and overall business goals, especially when managing combined orders or variable batch sizes.

The Order Batching Problem

The order batching problem is a classic optimization problem in operational research, focused on grouping customer orders into batches to minimize total picking time. Solving this problem requires careful consideration of factors such as item locations within the warehouse, picker capacity, and order due dates. To address these complexities, order batching algorithms like the genetic algorithm and the attribute based hill climber have been developed. These algorithms use heuristic approaches, including variable neighborhood search and tabu search, to explore different batch groupings and identify solutions that reduce travel time and improve efficiency. The order batching problem remains a key area of study, as optimizing batch formation can lead to significant gains in warehouse productivity and customer satisfaction.

Sequencing Problem in Order Batching

Once orders are grouped into batches, determining the optimal sequence for picking those batches becomes crucial. The sequencing problem in order batching involves deciding the order in which batches should be picked to maximize efficiency and minimize delays. Different algorithms can be applied to solve this challenge, such as the first-come-first-served algorithm, which processes batches in the order they arrive, and the seed algorithm, which starts with a seed batch and adds similar batches to it. Advanced techniques like iterated local search and variable neighborhood descent are often used to further refine the picking process, exploring various batch sequences to find the most effective path. Addressing the sequencing problem ensures that the picking process runs smoothly, reducing bottlenecks and supporting timely order fulfillment.

Single Picker vs. Multiple Pickers

Choosing between single picker and multiple picker systems is a key decision in designing efficient order batching procedures. In a single picker system, one worker is responsible for collecting all items in a batch, which simplifies coordination but may not be as efficient for large warehouses or high order volumes. Multiple picker systems, on the other hand, assign several pickers to collect items for a batch simultaneously, which can significantly reduce total picking time and increase throughput. However, coordinating multiple pickers requires more sophisticated algorithms, such as variable neighborhood search, neighborhood search, and tabu search heuristics, to optimize routes and prevent overlap. While single picker systems are easier to implement, multiple picker systems offer greater scalability and efficiency, especially in complex warehouse environments where maximizing productivity is essential.

Benefits of Order Batching

Implementing order batching boosts speed, reduces waste, and supports smarter workflows throughout the fulfillment center. Key benefits are:

Increased Picking Efficiency

Pickers move through the warehouse once per batch instead of per order. That cuts total travel time, improves the picking process, and helps teams handle large batches using a smart savings algorithm without wasting steps or repeating routes in left and right aisle paths.

Reduced Operational Costs

Combining multiple orders reduces walking and duplicate handling. That means fewer labor hours and lower labor costs overall. Streamlined workflows cut expenses, especially in high-volume warehouse operations where gains from the optimization problem directly affect performance across all labor activities.

Faster Order Fulfillment

Using a batching process eliminates delays caused by repeated item handling. Batch picking clears congestion, improves flow, and lets teams complete even the last batch before cut-off. Orders leave faster, meeting due date demands and rising customer expectations for fast delivery.

Improved Accuracy

Grouping tasks in such a way allows fewer interruptions during picking. When supported by warehouse management tools, the process reduces human error, supports batch order tracking, and improves customer satisfaction, whether it’s the first batch or the seventh order in the queue.

Enhanced Labor Management

Supervisors use order data and workload visibility to assign teams more effectively. Defined batch sizes, routing logic, and the average number of picks per shift help solve the sequencing problem and align with business targets for consistent performance.

Drawbacks and Considerations

Managing large batches or mixed orders can create challenges during execution. Some items may require a separate pick, especially when batching rules overlook SKU grouping or the single seed rule used to anchor routes.

A misaligned batching process can delay shipments. If the system assigns low-priority orders into a batch ahead of urgent ones, delays grow. That issue often relates to a weak algorithm that consists of skipped seed order logic, harming customer satisfaction.

Accurate execution demands real-time data. Without reliable warehouse management or ERP systems, teams can’t manage batch orders, update order status, or avoid routing problem delays. The wrong next batch may trigger a vehicle routing problem, increasing congestion.

Strong results come from clear protocols and responsive tools. Solving the optimization problem requires clean order data, solid rules, and batching logic that accounts for enough capacity to support peak order volume and keep the fulfillment process running smoothly.

Best Practices for Effective Order Batching

Order batching works best when supported by strong systems, smart planning, and well-trained teams aligned on goals and execution processes. Here are the best practices:

• Use a Modern WMS: Choose warehouse management software that updates inventory in real time and runs batching strategies based on volume, zones, or order status.
• Analyze Order Patterns: Study order data regularly to match batch sizes and routing models to actual volume, location density, and product types.
• Train Staff Thoroughly: Ensure pickers are allowed to follow optimized paths. Help teams recognize batch-picking signals and avoid wasteful routing strategy errors.
• Monitor KPIs: Track key performance indicators like pick time, accuracy rate, and batch cycle time to measure impact and optimize the fulfillment process.
• Adjust as Needed: Review customer expectations and seasonal trends. Adapt rules when order volume spikes or time-based batching becomes inefficient. For a deeper understanding of warehouse processes and how to improve efficiency, explore proven strategies.

Keep order batching flexible. Treat it as a tool that must evolve with business needs, not a fixed process for every season or cycle.

Conclusion

Order batching improves speed, accuracy, and cost control across the fulfillment process. Teams cut travel time, reduce labor costs, and handle multiple orders more efficiently inside the warehouse. Smart batching strategies maximize every trip and improve overall operational efficiency.

Fulfilling customer orders faster requires flexible systems. Using order data, real-time tools, and the right batching process, businesses can scale their fulfillment center while still meeting customer expectations in today’s high-pressure, fast-shipping world.