AWS helping customers optimize warehouse design

Amazon has developed a systematic approach for designing its fulfillment centers. To do this, Amazon uses digitalization software powered by Amazon Web Services (AWS), industrial engineering experience, simulation and digital twins.

AWS now provides a professional service offering to help its customers design and optimize fulfillment centers. AWS said its warehouse automation and optimization (WAO) is designed to help its customers uncover increased efficiencies, reduce costs, and improve performance. According to McKinsey, there is plenty of opportunity for warehouses in a variety of industries, including up to 13% in automotive and up to 30% in 3PL and pharma.

AWS recently outlined its three-phase plan for WAO that is based on Amazon’s experience optimizing its own fulfillment centers.

1. Survey and Modeling

Survey and Modeling is leveraged for customers who are looking to improve or re-imagine an existing (brownfield) facility. For customers looking to design new sites (greenfield), the process begins in Phase 2.

In the Survey step, an Amazon engineering team walks and maps your existing warehouse using a LIDAR scanner to capture an accurate 3D image of the current layout. Amazon said the scanner it uses captures items with a precision of approximately 3 centimeters, including the physical layout elements like walls and outbound gates, and equipment such as conveyor belts, forklifts, and storage systems. The LIDAR scanner outputs the results into a point cloud format.

The next step is modeling. This generates a digital twin – an accurate digital representation of a physical space, which includes the physical structure, digital assets, and data related to the physical space that enables simulation and scenario modeling. During modeling, AWS said the point cloud file is converted into a 3D model.

The final step in this phase involves the creation of a digital asset library to identify relevant assets inside the facility. Common examples include workstations, storage systems, forklift charging stations, conveyors, and other automation and material handling equipment. Each asset is given a unique ID number, and upfit with metadata. The metadata can include design-relevant elements such as performance data, carbon footprint, cost data, geolocational data, vendor information, and more.

These assets can be re-positioned to review and test different design ideas and optimize layout options. They can also be connected to real-time data from IoT sensors (when installed) or other sources like warehouse applications to enhance the digital twin.

2. Design

AWS customers requiring new facility design start at the design phase. The design phase creates conceptual warehouse designs to showcase what the new warehouse could possibly look like. AWS said it includes customer constraints such as budget, timeline, security, infrastructure, safety and regulation during the design Phase. The results are conceptual layouts in AutoCAD, which include all design aspects such as detailed engineering and performance data for any proposed material handling equipment, automation, robotics, and storage solutions.

AWS said it also reviews all “generally available solutions” in the automation and robotics phase of the design. It can also support request for proposal (RFP) creation and vendor selection for customers as an output of the design and simulation process. Typical solutions here may include, but are not limited to: ASRS, AGVs, conveyor, sortation, storage solutions, cobots, computer vision solutions, and automated packing and label application.

3. Simulate and Analyze

In this phase, simulation models are created to allow users to assess the quantified impact of new design and automation. AWS said this allows understanding of impacts to cost, throughput, labor, equipment, and space. Additionally, AWS said it can track sustainability data from utility usage or CO2 emissions. Ultimately the model guides return on investment (ROI) analysis for different solution options.

AWS said simulation models are built to cover the entire warehouse end-to-end from receiving to shipping. This provides for accurate simulation of an entire operation, instead of one process at a time. AWS said this allows it to overcome potential over-estimation of performance for automation, robotics, or other stand-alone processes.

The result of a LIDAR scan that captured the details of a warehouse layout and equipment placement. | Credit: AWS

For example, a vendor may advertise a certain performance level of an ASRS that assumes perfect and static inputs (putaways) and outputs (picks). In reality, this is rarely the case as inputs and outputs are dynamic and influenced by warehouse downtime, order drop schedules, maintenance windows, and other variables. End-to-end simulation models allow AWS to account for these variables, thereby improving accuracy of prediction and removing bias from vendors or other stakeholders.

AWS said its customers can continue using the models built in their day-to-day operations. For example, to run simulations on how an unforeseen increase of inbound trucks will affect end to end throughput, and how to best reallocate labor.

Amazon said this process has decreased the time it takes it to build digital twins by up to 80%. AWS and Amazon have access to industry benchmarking, best practices, suppliers. It is now looking to leverage these to benefit AWS customers through WAO.

Written by

Automated Warehouse staff