Exotec Overview and Features – A Deep Dive into the Skypod System and Astar Software

Deploy a focused pilot of the Skypod system to validate throughput before scaling. Seeing real-world results across a single unit helps you evaluate sequencing, and it provides a concrete baseline for continuous management. often reveals bottlenecks that static simulations miss, especially in ai-driven control environments. This pilot will show how sequencing and handling steps influence overall performance, seeing gains in real-time.

The Skypod hardware stacks goods on tall, compact bays beneath elevated aisles, while the ai-driven Astar software coordinates movement across the entire system. Each unit operates with a decentralized control model that enables real-time sequencing of picks and replenishments, reducing deadheading and storage travel. When deployed across a broader network, you gain consistent status visibility through a single pane of glass. news about updates and improvements flow into the system, enabling continuous improvement in management. they enable operators to compare results across shifts and facilities.

Astar’s planning engine ingests ERP and WMS feeds, delivering ai-driven optimization for inbound and outbound flows. It stores historical data to support thorough evaluation of performance, with dashboards showing status by pod and by item. Increase throughput by tuning replenishment windows, optimizing storage policies, and strengthening exception handling. Thanks to cross-site integration and a modular API, you can scale across campuses while maintaining a common control logic.

Plan a phased rollout: begin with one zone, then expand to adjacent bays, and finally scale across sites. Build continuous monitoring and set clear targets for utilization of stored inventory and reduction of handling steps. The team should evaluate performance monthly, adjusting sequencing rules and replenishment thresholds as you receive news from field operations. Track unit uptime and management practices to drive reliability, and document root causes of recurring issues beneath the surface for ongoing learning.

Exotec Overview and Features

Start with a 90-day pilot in a single zone to quantify capacity gains and validate the solution before scaling. Map the rail layout to your fastest-moving SKUs and connect the Astar software to your WMS to automate the order workflow and reduce touches.

Skypod packs high-density storage on a rail-guided platform that minimizes walking and lets robotics retrieve stored items with high precision. The solution pairs elevated bays with a compact footprint, delivering capacity gains in tight facilities and enabling multi-order workflows. In practice, enterprises often see 2x-3x throughput for standard e-commerce orders and a 40-60% reduction in travel distance for pickers, which accelerates growth and reduces labor costs.

From a perspective of reliability, the system is enterprise-grade with fault-tolerant automation and a technical foundation designed to handle peak demand with consistent performance. Astar coordinates each task with real-time visibility, pushing following orders to the closest pod and balancing load to maximize capacity. The result is predictable performance under high-volume conditions.

Scalability across industries becomes straightforward with a modular design: retail, grocery, 3PL, and manufacturing can tailor the workflow to their needs. The system stores inventory data locally and on the cloud, enabling rapid growth from pilot to full deployment. In each industry, the ability to reuse standard components accelerates time-to-value and supports rapid capacity expansion.

Best practices to maximize value: start with a small batch of SKUs, synchronize with your ERP, and monitor key metrics like order fill rate, cycle time, and pick accuracy. Use next-gen analytics to measure throughput and performance, and adjust the routing rules in Astar. Through regular validation, your workflow becomes faster, and you can become the fastest option in your space for high-demand SKUs.

Skypod System and Astar Software: Visibility to Automated Action

Enable real-time visibility by linking Skypod feeds to Astar and take automated action on exceptions within 60 seconds, ensuring their task queues stay aligned with SLA targets.

The Skypod System provides an overview of operations with a cutting-edge monitoring layer and a thorough configuration that ties availability to automated responses. Information flows from the origin of each event through a unified form, delivering well-structured data that guides decisions. Operators can track peaks and rates, measure highest and lower throughput, and respond before delays cascade. The interface remains intuitive, and the signals isnt buried in noise, supporting a quick return to normal conditions as changes occur. The approach helps teams remain proactive rather than reactive.

Configuration and optimization enable optimizing task routing by linking the windows where data is fresh to the windows where actions occur. This approach enhances efficiency and reduces latency, requiring managing thresholds, alerting, and escalation rules. You can take action across their fleets, or, when needed, act alone to isolate the root cause. The form of data and the information provided become the basis for proactive adjustments that have changed operating rhythms to meet demand.

In practice, this setup lets teams manage operations with confidence–data informs action, action returns control, and changes stay aligned with what matters most: uptime, throughput, and task completion.

Skypod Hardware Architecture: Parcel Handling, Lifts, and Stow/Dispatch Workflows

Recommendation: deploy a zone-based Skypod layout with dedicated lifts per area and automated stow/dispatch triggers. The users then assign tasks to a robot via Astar, reducing travel and enabling an optimized outbound flow that keeps operators productive.

Parcel handling architecture centers on a clear origin to form path. Parcels arrive tagged with origin and destination, pass sensors to confirm form and size, and enter an inbound queue. A lift moves the parcel to the correct pod level, and the robot gripper places it into the assigned bay, ready for stow or later dispatch. Points along the path provide status updates that feed the control loop and improve accuracy.

Stow/Dispatch workflows: The system executes two linked flows. Stow pre-positions items in assigned bays during idle cycles, while dispatch aggregates outbound items by address and places them on the outbound line. The uses of rules consider workload, location, and carrier requirements. If a condition fails, manual override is available and logged for certified review.

Capacity and accuracy: With several lifts operating in parallel, capacity can reach 40–60 parcels per hour per pod; overall capacity scales with the number of pods. Typical cycle times range from 25 to 35 seconds from pickup to storage or release, delivering parcels within target windows. System accuracy stays above 99% in steady runs, helping to minimize misroutes and rehandling.

Operations and maintenance: Routine checks by certified technicians occur quarterly; maintenance fees apply for parts and service. The software provides alerts and witnessing events for safety audits, and the architecture supports manual interventions when needed. A go-live plan includes training, data validation, and staged deployment to minimize downtime.

Go-live and studies: Before go-live, run several pilots to observe behavior under real demand. Studies and mccown tests show the workflow addresses demand effectively, improving throughput and witnessing fewer bottlenecks. Recognizing operator feedback helps fine-tune stow rules and addresses any misalignment between zones and bays.

Astar Software: Task Routing, Scheduling, and API Integration

Adopt real-time task routing to assign inbound and outbound tasks to amrs, reducing capex and training time; weve seen next-gen deployments improve throughput while preserving tote handling efficiency.

• Real-time task routing: The Astar engine continuously monitors queues, AMR availability, and tote locations, assigning high-priority tasks first and balancing load to maintain flatness across shifts. It dynamically reoptimizes when delays occur, minimizing operator idle time and improving accuracy on outbound flows.

• Scheduling and right-size planning: Dynamic scheduling respects right-size capacity by considering current inbound volume, case mix, and operator coverage. This approach reduces limited over- or under-utilized resources, preventing unnecessary capex and ensuring tasks complete within planned windows. It also supports eventual change management with clear sequencing of tasks and buffers for spikes.

• API integration and data flow: RESTful APIs enable seamless connections to WMS, ERP, and analytics suites. Webhooks push real-time status updates, while programmatic task creation and updates automate workflows across outbound, inbound, and returns. This enables completely automated task orchestration and comprehensive visibility.

• Case and outcomes: In a case with Skypod-enabled operations, customers report faster outbound cycles, improved tote-level accuracy, and smoother operator handoffs. Astar supports multi-site deployments, helping the companys network stay synchronized while keeping change management lightweight.

• Implementation tips: Begin with a limited task set to validate routing rules and then scale. Use real-time dashboards to monitor queue length, AMR utilization, and throughput. Plan a concise training path for operators and provide ongoing support to sustain adoption.

Real-time Visibility: Dashboards, KPIs, and Alerting for Warehouse Ops

One concrete recommendation: deploy a single, unified real-time dashboard that ingests data from Skypod, WMS, and carrier feeds, paired with a smart alerting layer. This setup minimizes response time, reduces manual checks, and supports faster decision-making across shifts. Data flows through edge and cloud layers to keep dashboards current.

Key design choices:

• Facet-based views filter by zone, SKU, carrier, payload type, and customer to surface the most relevant metrics.
• KPIs include on-time shipping rate, dock-to-stock time, order fill rate, picking accuracy, payload utilization, equipment stands readiness, and data latency, with buffering to smooth gaps in feed.
• Alerts use a two-tier model: warning alerts for near-threshold events and critical alerts for line stoppages, ensuring teams meet SLA without noise.
• Traditional dashboards often lack real-time context; this approach ties event rates to operation outcomes and supports iterative improvements.

Starting with a minimal KPI set, teams will measure results and adjust thresholds over time. The assessment will reveal gaps, inform projects across shifts, and show how the system drives error reduction and improved throughput.

Implementation steps:

1. Define a minimal KPI set that ties directly to shipping outcomes and operation throughput.
2. Design facet-based dashboards across zones, SKUs, carriers, and payload types.
3. Implement smart alerts with buffering to balance timeliness and noise.
4. Establish training and assessment cycles to train staff and measure results.
5. Track ROI through ongoing projects and cross-functional reviews.

Data quality and buffering:

• Stream connectors should deliver live data with buffering to absorb bursts and minimize missed events.
• Validate data integrity through regular assessment against warehouse events and error rates, then adjust rules and thresholds.

Automation Scenarios: Replenishment, Order Consolidation, and Exception Resolution

Start with automated replenishment, linking the Skypod suite to a proprietary rules engine to automatically trigger unit-level replenishment when on-hand materials fall below a short threshold. These measures enable you to automate replenishment decisions across different product classes, delivering replenishment quickly and with minimal human intervention, saving activities and labor, and reducing replenishment cycle time by 30%–40%. Data travels over the internal transportation network, keeping aisles stocked and ensuring requirements are met and slas are respected.

Enable order consolidation by applying sorting at the pick face to group products for the same stops, then generate consolidated orders per unit. This reduces traveling distance and utilization of equipment, shortens travel time, and increases throughput on the first pass, with traveling distance typically cut by 20%–35%. The suite coordinates with transportation routes and the store layout, minimizing trips across aisles and delivering picks in a single, efficient run.

Configure exception resolution to run automatic checks that compare picks against order requirements and flag discrepancies in real time. When issues arise, the system suggests actions and routes them to a human for quick validation, while continuing automatically on other orders. This approach reduces delays, preserves delivering slas, and maintains clear activity logs. All activities are logged to ensure traceability and continuous improvement, reinforcing adherence to requirements and enabling faster corrective actions.