The adoption of Automated Storage and Retrieval Systems (AS/RS) stands as a testament to innovation and efficiency. AS/RS technology has earned its place as the go-to solution for automating warehouses, offering unparalleled benefits. Yet, even in its trusted and stable form, challenges are inevitable, and they demand solutions that adhere to a standardized framework.
This article embarks on a journey through the world of AS/RS solutions, recognizing their status as the foremost choice for warehouse automation. However, in the pursuit of efficiency, warehouse solution designers often encounter a range of recurring issues. These challenges necessitate a comprehensive and standardized framework to address them effectively.
The framework suggested is the result of experience, offering a structured approach to tackle the complexities of AS/RS implementation. As we dig deeper into the article, we will uncover these essential factors that guide decision-making. Whether it's optimizing storage depth, fine-tuning throughput calculations with and without batching probabilities, we'll establish a road map for addressing challenges in AS/RS design.
By presenting this framework, my aim is to empower warehouse solution designers to effectively overcome the issues that may arise.
Typically, there are two types of picking requirements in a pallet AS/RS: full pallet picks and full pallet plus partial pallet picks. Full pallet picks are relatively straightforward and follow a path 1-2-3-4-5-6-7-9-12-15. However, partial pallet picks may encompass the entire path with every decision making point.
The storage density, measured in pallets per batch or pallets per SKU, determines whether the storage mode will be up to 2 deep, >2 deep, or multi-deep, which can involve AS/RS stacker cranes, stacker cranes with shuttles, or shuttle-based storage systems. Since stacker cranes and shuttle-based systems have different vertical height utilization capacities, this decision can also be based on optimizing area utilization.
Throughput calculation largely depends on the number of pallet equivalents to be inbound and to be picked. In the case of partial pallets to be picked, it involves orderlines per hour, unique SKUs per day, or wave, which provides the batch factor (Orderlines/Unique SKUs). A higher batch factor results in lower throughput, with a greater percentage of pallets becoming empty per presentation for pick and fewer partial pallets returning to the system.
Ideally, if there is enough space available for a forward pick area to accommodate all the unique SKUs to be picked per day, the effective throughput will be reduced to only pallet equivalents to be picked per hour. In the absence of enough space, the required throughput will be pallet equivalents per hour plus the SKUs that need to be rotated in the forward pick area from the AS/RS due to limited space. Without a forward pick area, picking is most suitable for full pallet picks only, especially if the percentage of partial pallet picks is low.
The picking strategy step provides all the throughput that an AS/RS needs to handle to meet the business requirement. Now, it depends on the equipment's capacity to deliver the required productivity. AS/RS productivity relies on equipment specifications and potential bottlenecks, such as lifts in shuttle-based systems or reshuffling requirements if multi-SKU locations are defined in the storage. If the throughput requirement doesn't match the chosen AS/RS's capacity, the storage configuration analysis may need to be revisited to account for this factor in the decision-making process.
Pallet AS/RS technology stands as a beacon of innovation and efficiency in warehousing. The challenges it presents demand a standardized framework for solution designing. This article has unveiled a comprehensive approach to address recurring issues in pallet AS/RS solution design, empowering warehouse solution designers to optimize storage, calculate throughput effectively, and strategically align picking requirements with business needs.