CognitOps Insights


December 18th, 2020

Autosotre V5

Austin, TX – December 18, 2020

Automated Storage and Retrieval Systems (ASRS) and Goods to Person (GTP/G2P) solutions have proven to be two of the most significant technological advancements in the warehouse over the last two decades.   Learning how to best take advantage of these systems and achieve maximum ROI, however, remains a challenge for the warehouse operator, many of which are going through their first technological transformation in the workplace. 

Advanced software drives the operation inside these systems – optimizing the available work at the highest throughput and lowest possible cost.  Despite this automated internal tasking, achieving optimal rates requires active management of external factors and decisions such as the work available, the number of staffed stations, and timing of release. 

AutoStore has been one of the most popular dense storage and goods to person picking systems deployed.  While simple to operate at first review, incorporating it into a complex fulfillment flow and achieving the last 20% of performance gains necessary to prove ROI requires sophisticated understanding and dynamic management. 

While experienced operators may occasionally reach peak operational throughputs, consistently achieving the goal of total flow optimization, will require leveraging AI-based tools. We’ve identified three key factors to optimize the effectiveness of an AutoStore system, and how the CognitOps Warehouse Operating System (WOS) can drive the final performance threshold.

1.Load The Right SKUs

Best Practice

While AutoStore provides high-volume goods to person picking, its biggest lever is its high storage density, potentially 4x the density of traditional shelving. Often, operators will push for maximum utilization of the AutoStore at go-live with the fastest SKUS to maximize ROI, which can actually reduce the largest density gains, create bottlenecks, and impact the velocity of all SKUs. Careful analysis of bins required, the cost of decanting, and pick station capacity should guide the slotting of SKUs to the AutoStore, typically not the highest velocity SKUs.

Operational Guidance

To maximize the efficiency of the AutoStore, Operations Managers need to keep a close eye on the performance of their AutoStore, including:

  • The velocity and inventory levels of slotted SKUs
  • Overall bin utilization
  • Appropriate Decant to Pick Volumes

AI Performance Lift

Instead of monitoring and reacting, AI-driven forecasting can predict when demand will exceed AutoStore inventory, make recommendations to move fulfillment to an alternate engine, or prioritize replenishment. CognitOps Warehouse Operating System (WOS)  connect ERP, WMS, WCS/WES, and Labor Management Systems  to identify which SKUs are driving the most order shortages or exceptions; calculate the complex decanting, bin utilization and picking gain trade-offs in real-time to make recommendations on whether/when slot to other fulfillment engines.

2. Know Your Throughput Limitations

Best Practice

AutoStore provides the flexibility for multiple order fulfillment models, pick station designs, or operational activity at each port. Each port has a mechanical maximum bin presentation capacity, as well an operational capacity. Knowing these capacities is critical to understand your building’s potential by functional area.

Throughput Limitations

Operational Guidance

Operations managers should develop rule of thumb calculations to determine the number of stations, hours, and resources necessary to meet demand. When demand shifts (ex. from a pandemic), or there are multiple order profiles being filled simultaneously, planning and execution become incredibly complex.

AI Performance Lift

AI analysis and forecasting can automate decision making of tactical execution for the AutoStore. Based on priority, shipping cutoff time, and order profiles, CognitOps can recommend the correct sequence of order release to ensure shipping SLAs are met and machine throughput is maximized.  AI-driven labor and resource planning automatically balances staff, minimizing unnecessary overtime and delays.

3. Execute The Right Work At The Right Time

Best Practice

AutoStore makes the most out of shared resources (ports, robots, and bins) to serve multiple functions:

  • Decanting

    • From Reserve
    • From Receiving
  • Inventory Control:

    • Cycle Counting
    • Empty Bin Retrieval
    • Bin Consolidation
  • Picking

Each of these functions have theoretical maximum throughput rates, while running them in parallel creates a complex multi-variable function to determine the total productivity.

While picking usually is the most critical task, it is also the least predictable, as order volume, priorities, and profile are unknown. Operational managers must plan for variable demand against both a fixed machine throughput and static cut-off times.

Operational Guidance

Operational managers should map out upstream / downstream processes, expected activity windows within AutoStore, and sufficient transportation buffers to create an operational clock for the building. Leveraging this operational clock will provide guidance on labor planning, fulfillment status, and if the building is ahead or behind schedule. 

operational guidance

AI Performance Lift

AI-driven warehouse operating systems dynamically forecast and plan the operating clock of the building, directing resources across all areas from receiving through shipping at the right times, with the right sized teams. 

Additionally, while a fixed operational clock is the tool of experienced operators, leveraging AI to dynamically adjust based on forecasted orders, order profiles, and labor fluctuations, ensures that SLAs are met despite the level of unpredictability or fulfillment complexity.

AI performance lift


Advanced automation has enabled companies to sustain their aggressive growth by allowing supply chains to deliver exacting customer expectations.  Achieving the next levels of growth however,  will require effective and holistic workflow management, vs. individually optimized solutions.

By connecting multiple independent data silos, applying constantly trained AI algorithms, and guiding decision making, CognitOps puts facility operations in the best position to achieve or exceed daily goals. With the CognitOps Warehouse Operating System, the decision making lens is holistic, adjustments can be made proactively, and total facility performance can be optimized.