Optimising efficiency in the warehouse is the Holy Grail that drives operators to constantly seek new systems, upgrades and innovations in the never-ending drive to get better, says Chandru Palaniyandi, Business Analyst at Lucas Systems. Fighting the battle to eliminate wasted time and unproductive steps will always be an unreachable journey towards perfection as customer needs and expectations continue to always redefine what, “as soon as possible” means.
Optimisation can take many forms, and what is important to understand is that many improvements, and even transformative changes can typically be made without changing back-end systems. Things like zone picking, dual-pallet picking instead of picking a single pallet at a time or, batching work assignments intelligently to optimise pick density and reduce travel can make a huge difference in productivity, on-floor travel, time savings and basically just streamlining many tasks. Let’s explore five ways that can help you optimise your warehouse operations.
1. Dual pallet picking – In the dual pallet scenario, the picker receives a work assignment for two pallets (may be multiple orders) to be picked simultaneously. This serves to consolidate picks and limit travel, which helps increase productivity by making double the number of picks in each pass of the warehouse.
Smart pallet matching algorithms can help with the following, thereby reducing travel time significantly:
•Ensure the second pallet has picks in the same aisles as the first pallet (improving pick density).
•Ensure the staging lanes for both pallets are close to each other.
These algorithms can also help achieve a fine balance between priority and pick density. For instance, these algorithms can be configured to give preference to pick density at the start of a day (when the carrier cut-off times are still several hours away); this will allow dense batches to be created but the highest priority orders may not necessarily be picked first. And as we near the carrier cut-off times, preference will be given to completing highest priority orders first over increasing pick density.
2. Zone picking – Zone picking can be thought of as the warehouse picking version of an assembly line, in which parts of an order are picked in different zones by different workers. The zones in question can be sections within a pick module, or different floors in the warehouse, or larger pick areas that may be defined by product type (bulk, flammable, grocery), racking type, environmental features (refrigerator, cooler), SKU velocity, and/or picking processes. For example, a static shelving area, a high velocity each pick module, or a case-pick zone, etc.
In large DCs with diverse products and large numbers of SKUs, orders containing products from different pick areas may be broken up and picked by zone, and the items picked in the various zones are then consolidated prior to shipping.
Depending on the layout of the warehouse, an order (or a batch of orders) can also be passed from one zone to the other so they are fully picked, eliminating the need for a separate consolidation process.
In each zone, multiple orders can also be batch picked into the same totes, and a sortation process (e.g., put wall sortation) can be used to segregate and pack the items required for each order.
Zone picking is extremely useful for warehouses that have a large number of SKU’s, with diverse product characteristics. Zoning products based on SKU velocity allows DCs to design different picking processes that are tailored to optimise the process for the particular velocity of products in different zones.
Dividing products by velocity or other characteristics allows DCs to optimise their picking processes across all item types rather than employing one pick process for all products. Zone based picking will also typically reduce the number of workers per zone, reducing congestion.
Because of the zone-based concept, zone based picking reduces the area covered by each picker, so travel time is significantly reduced. Since travel may account for 30-70 percent of travel time in picking, this represents a significant additional productivity boost.
3. Intelligent batching – This process utilises software to apply multiple AI-based algorithms to all available orders in the system to create optimised batches for users. For example, Lucas AI-based software, Jennifer™ uses order, inventory, and location information from WMS and other systems and applies real-time optimisation algorithms to create batch assignments. Unlike simple rule-based batching used in a WMS (such as FIFO or product and location overlap), Jennifer™ considers order priority, pick location, travel cost, product attributes, and other factors to create optimal batches of work. Jennifer™ evaluates millions of potential combinations to determine the “best batch” or grouping of work from among the available orders. The math behind this is daunting. If 1000 orders are available for batching, and you are trying to create batches of four orders, there are more than 41 million possible combinations. Jennifer™ runs through the combinations in less than a second as users on the floor request work.
4. Bucket brigade – The bucket brigade process balances workflow and distributes work evenly amongst the pickers, maximising productivity and throughput. In a bucket brigade, the person at the end of the pick module will complete the last pick in their order, push the tote off the line, and then walk up the line to where the previous worker is picking and take over that order. After the last worker “steals” the assignment, the previous worker will do the same to their predecessor, and so on. The effect is that every worker has work at all times, and no two workers are competing to pick items in the same bay at the same time.
Bucket brigades create a self-regulating flow of orders through the pick module, distributing pickers where there is work. To further boost productivity and throughput in the pick module, workers can pick multiple orders at a time in a “train” of multiple totes or cartons.
Bucket brigades also introduces an element of gamification into the picking process. Since pickers are working closely with each other in the same area, they tend to challenge each other to improve both individual as well as collective productivity.
5. Pick path optimisation – Traditional picking systems use simple pick sequences that direct workers up one aisle and down the next in a snaking pattern. AI-based path optimisation algorithms use a virtual map of a facility to compute an ideal travel path that does not follow a strict location sequence. The algorithms take account of a user’s starting and end points, aisle travel restrictions (one-way aisles, for example) and other factors. Path optimisation is applicable to picking, replenishment and other activities where individual work assignments span widely dispersed locations. Path optimisation can also be used when interleaving multiple applications (for example, picking and putaway)
Depending on many factors, the applicability and impact of some of these types of solutions will certainly vary. But using any of them, or combinations that most closely fit your needs and use cases can have a significant positive impact on your operations. And what’s best is that these can be flexible technologies and processes that can allow you to continuously adapt to changing needs and market demands.
Lucas Systems
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