CognitOps Insights

Warehouse Picking Strategies:

Your Ultimate Guide to Boosting Efficiency

Warehouse picking is vital to the global supply chain and company success. Efficient and accurate picking ensures orders are fulfilled quickly and correctly, boosting customer satisfaction. Streamlined picking reduces delays, minimizes errors, improves inventory management. This efficiency meets modern consumer demands for fast, reliable delivery. Optimized picking also reduces costs, increasing profitability and competitiveness. In fact, order picking typically accounts for about 55% of warehouse operating costs with picking travel time accounting for the highest proportion, according to the Georgia Institute of Technology.

Investing in advanced picking technologies helps companies strengthen supply chains, adapt to changes, and achieve sustainable growth. This blog provides warehouse supervisors and facility managers with the latest trends, technologies, and strategies to improve picking processes in today’s performance-driven warehouse.

Understanding Warehouse Picking

Warehouse picking is the process of selecting and collecting items from a warehouse to fulfill customer orders. This crucial operation involves locating the specific products ordered, retrieving them from their storage locations, and preparing them for shipment.

Efficient warehouse picking is essential for maintaining a smooth supply chain, ensuring accurate and timely order fulfillment, and minimizing operational costs. The process can vary from simple, manual picking of individual items to complex, automated systems involving robots and advanced software.

The Picking Process

1. Order Reception

Orders are received from customers through various channels such as e-commerce platforms, EDI systems, or manual input. The warehouse management system (WMS) processes these orders, and depending on the sophistication of the system, can automatically prioritize them based on predefined criteria.

Example: An e-commerce platform sends orders to the WMS in real time. The system prioritizes next-day delivery orders to ensure timely fulfillment.

2. Item Location

The WMS identifies the storage locations of the items in the order. This information is used to generate pick tasks or instructions for the pickers.

Example: For an order containing a laptop, charger, and mouse, the WMS provides the exact bin locations of each item, optimizing the picker’s route.

3. Item Retrieval

Pickers retrieve the items from their storage locations according to the pick list. Technologies such as barcode scanners, RFID readers, or voice-picking systems assist in accurate retrieval.

Example: Using a handheld scanner, a picker scans the barcode of each item to confirm its retrieval. The scanner provides immediate feedback if an incorrect item is picked.

4. Order Verification

After picking, items are verified to ensure accuracy. This step may involve scanning barcodes, checking quantities, and matching items against the order list.

Example: A picker places the items in a verification station, where an automated system scans and verifies the items against the order list, flagging any discrepancies.

5. Packing and Shipping

Verified items are packed securely for shipment. Shipping labels are generated, and the order is prepared for dispatch to the customer.

Example: A completed order is placed in a shipping box along with packing materials to prevent damage. The WMS generates a shipping label, and the box is sent to the shipping dock.

Different Types of Picking

Various picking models exist to accommodate different types of orders, inventory characteristics, and efficiency goals and optimize specific aspects of the picking process. The diversity arises from the need to balance factors such as order volume, item variability, warehouse layout, and labor efficiency. There can also be some overlap between pick methodology and processing methodology.

Piece picking or case/pallet picking can all be performed using discrete order picking or batch picking. Waving is a strategy for releasing work that can include all of the above. By selecting the appropriate picking model, warehouses can enhance accuracy, reduce picking time, and improve overall operational effectiveness, ensuring that they meet the unique demands of their business and customers.

Piece Picking

Piece picking involves retrieving individual items from storage to complete a customer order. Each order is picked separately, item by item.

E-commerce: Online retailers like Amazon and small to medium-sized e-commerce businesses often use piece picking due to the diverse range of products and small order sizes.
Pharmaceutical: Pharmacies and healthcare providers use piece picking for precise and accurate fulfillment of prescriptions.

Example: An online clothing retailer processes an order for a single shirt. The picker retrieves the shirt from its storage location, verifies it, and sends it to packing.

Batch Picking

Batch picking involves picking items for multiple orders simultaneously. Orders are grouped into batches based on commonality of SKUs to minimize travel time. This usually requires a separate downstream sortation/consolidation process.

Retail: Large retailers like Walmart use batch picking to efficiently handle high volumes of orders with similar items.
Grocery: Supermarkets employ batch picking for online grocery orders, picking common items for multiple customers at once.

Example: A warehouse receives 10 orders that include toothpaste. Instead of picking toothpaste 10 times, the picker retrieves 10 tubes in one trip and sorts them by order later.

Zone Picking

In zone picking, the warehouse is divided into zones, with each picker responsible for a specific zone. Orders are picked within these zones and then consolidated.

Automotive: Auto parts warehouses use zone picking to manage the extensive variety of parts and accessories efficiently.
Electronics: Warehouses handling electronic components benefit from zone picking to reduce picker congestion and streamline the picking process.

Example: A customer order includes items from three different zones: Zone A for small electronics, Zone B for cables, and Zone C for packaging materials. Each zone’s picker retrieves the items, and they are consolidated before shipping.

Wave Picking

Wave picking combines orders into waves based on criteria such as order priority, shipping schedules, and picker availability. Wave picking can be done via batch picking or discrete order pick where the picker works a single order at a time. Multiple pickers can pick separate orders simultaneously, balancing workloads and optimizing throughput.

Apparel: Fashion retailers use wave picking to manage seasonal peaks and promotional events efficiently.
Distribution Centers: Large distribution hubs use wave picking to coordinate shipments and meet delivery schedules.

Example: A warehouse schedules wave picking to fulfill orders for the morning shipping batch. Pickers retrieve items for all orders in the wave, which are then packed and shipped together.

Warehouse Picking Challenges and Solutions

Warehouse picking operations face several challenges that can impact efficiency, cost, and accuracy. Some common issues include:

Inventory Inaccuracies: Discrepancies between recorded and actual inventory levels can lead to picking errors, stockouts, and delays. Inventory inaccuracies often arise from prior picking errors, manual data entry errors, misplaced items, or unaccounted shrinkage.
Human Error: Mistakes made by pickers, such as selecting the wrong item or quantity, can result in order inaccuracies and customer dissatisfaction. Factors contributing to human error include inadequate training, fatigue, and poor working conditions.
Space Constraints: Limited warehouse space can lead to inefficient layouts and congestion, making it difficult for pickers to navigate and retrieve items quickly. This challenge is particularly acute in urban warehouses or facilities with rapidly expanding inventory.
Labor Costs: High labor costs can significantly impact the overall profitability of warehouse operations. This challenge is exacerbated by the need for skilled labor to manage complex picking processes and the growing demand for faster order fulfillment. Labor costs can be affected by factors such as inefficient labor usage by having staff assigned incorrectly and overtime needed to meet customer on time delivery.

Innovative Solutions

Addressing these challenges requires innovative solutions and the adoption of new technologies and strategies.

Real-Time Inventory Tracking: Implementing real-time inventory tracking systems, such as RFID or IoT-enabled sensors, can provide accurate and up-to-date inventory data. This technology reduces the risk of inventory inaccuracies and enables more efficient picking.
Dynamic Slotting: Dynamic slotting involves continuously adjusting the location of items based on demand and picking patterns. By placing high-demand items in easily accessible locations, warehouses can minimize picker travel time and enhance efficiency.
Predictive Analytics: Using predictive analytics, warehouses can forecast demand and optimize picking strategies accordingly. This approach helps anticipate order volumes, identify bottlenecks, and adjust resources proactively.
Automation and Robotics: Implementing automation and robotics can reduce reliance on manual labor, thus lowering labor costs. Automated systems such as AGVs, robotic arms, and conveyor belts can handle repetitive tasks with high precision and speed, allowing human workers to focus on more complex activities.
Labor Visibility and Optimization: Leveraging the data generated by warehouse activity to deliver insights into available staffing, historical performance, and daily trends with a tool like CognitOps Align, gives warehouse and department level leadership the ability to staff proactively into the future, coordinate workers with customer SLA cutoff priorities, and adjust on the fly when daily performance isn’t meeting goals.

Measuring and Improving Warehouse Picking Performance

To effectively measure and improve warehouse picking performance, it’s crucial to track specific Key Performance Indicators (KPIs) that provide insights into operational efficiency, accuracy, and productivity. Some essential KPIs include:

Picking Accuracy: This KPI measures the percentage of orders picked correctly without errors. High picking accuracy indicates efficient processes and reduced rework. For example, a company might aim for a picking accuracy rate of 99.9%, meaning only 0.1% of items are picked incorrectly.
Picking Speed: This measures the average time taken to pick an order. Faster picking speeds indicate more efficient operations. Picking speed can be measured in units per hour or orders per hour. For instance, an efficient warehouse might achieve a picking speed of 120 units per hour.
Order Cycle Time: This KPI tracks the total time from when an order is received to when it is shipped. Reducing order cycle time enhances customer satisfaction and operational efficiency. An optimal order cycle time could be less than 24 hours.
Labor Productivity, Utilization, and Efficiency These labor KPIs provide a multifaceted view of labor effectiveness. Productivity equals how much volume each worker processed during the time they were assigned to a task. Utilization means how much time each worker actually spends doing work while clocked into a department. Efficiency compares the actual work rate for an individual against the expected work rate for a given group of tasks.

Continuous Improvement Techniques

warehouse picking Implementing continuous improvement techniques can significantly enhance picking performance. These methodologies focus on incremental changes that lead to substantial long-term benefits.

Lean Practices: Lean principles aim to eliminate waste in all forms (time, motion, inventory) and enhance value-added activities. For instance, using a 5S framework (Sort, Set in order, Shine, Standardize, Sustain) can create an organized and efficient picking environment.
Six Sigma: This data-driven approach focuses on reducing variability and defects in processes. By employing Six Sigma techniques, warehouses can identify root causes of picking errors and implement corrective actions. For example, using DMAIC (Define, Measure, Analyze, Improve, Control) can help streamline picking operations.
Kaizen: Kaizen emphasizes continuous, small-scale improvements involving all employees. Encouraging pickers to suggest and implement minor changes can lead to significant performance enhancements over time. Regular Kaizen events or workshops can foster a culture of continuous improvement.

The Future of Warehouse Picking

As the landscape of logistics and supply chain management evolves, the future of warehouse picking is being shaped by innovative technologies and shifting consumer demands. Warehouses are increasingly adopting advanced solutions such as artificial intelligence, the Internet of Things (IoT), robotics, and predictive analytics to streamline operations, enhance accuracy, and meet the growing expectations for speed and customization. warehouse picking

Emerging Trends

The future of warehouse picking is being shaped by several emerging trends and technological advancements.

Artificial Intelligence and Machine Learning: AI and machine learning algorithms can analyze vast amounts of data to optimize picking routes, predict demand, and automate decision-making. These technologies enable warehouses to operate more efficiently and respond quickly to changing conditions.
Internet of Things (IoT): IoT devices, such as smart sensors and connected equipment, provide real-time data on inventory levels, equipment status, and environmental conditions. This connectivity enhances visibility and control over warehouse operations, leading to more accurate and efficient picking.
Advanced Robotics: The use of advanced robotics, including collaborative robots (cobots) and autonomous mobile robots (AMRs), is revolutionizing warehouse picking. These robots can work alongside human pickers, handle repetitive tasks, and navigate complex warehouse environments with precision.
Labor Forecasting: Leveraging AI and ML to analyze the masses of activity data surrounding warehouse picking activities allows for an almost prescient view of future warehouse staffing needs. With cutting-edge tools that allow warehouse leaders to determine what metrics they want their warehouse to target − the amount of work, dropped work, optimizing scheduled workers, and meeting customer SLAs − labor needs can be planned proactively.

The Evolution of Customer Expectations

Customer expectations are continuously evolving, driven by the rise of e-commerce and the demand for faster, more personalized services.

Same-Day Delivery: The growing demand for same-day delivery requires warehouses to optimize their picking processes to fulfill orders quickly and accurately. Implementing automated picking solutions and efficient order processing workflows is essential to meet these expectations.
Customization and Personalization: Customers increasingly expect personalized products and services. Warehouses must adapt their picking processes to handle customized orders efficiently, ensuring accuracy and timely fulfillment.

Preparing for the Future

To stay competitive and meet future challenges, warehouses must invest in technology, training, and scalable solutions.

Investment in Technology: Prioritizing technology upgrades, such as AI, IoT, and robotics, will enable warehouses to enhance efficiency, accuracy, and flexibility. Continuous investment in cutting-edge technologies is crucial for staying ahead in the rapidly evolving industry.
Training and Development: Ensuring staff are skilled in using new technologies and processes is vital. Implementing comprehensive training programs and fostering a culture of continuous learning will empower employees to adapt to technological advancements and drive operational excellence.
Scalability and Flexibility: Designing processes that can scale with growing demand and adapt to changing market conditions is essential. Implementing modular and flexible solutions will allow warehouses to expand capacity, adjust workflows, and meet evolving customer expectations effectively.

Conclusion

Regardless of emerging technology and consumer trends, warehouse picking will continue to be a lynchpin in global supply chains. Whether it’s pulling twice daily orders of critical medical supplies for hospitals, daily direct to consumer e-commerce packages or weekly bulk store inventory replenishment orders, selecting the right items for the right order at the right time will always be a priority for manufacturing and distribution companies focused on customer satisfaction and their bottom lines.

Warehouse Picking Strategies: Your Ultimate Guide to Boosting Efficiency