As third-party logistics providers modernize, real-time location systems (RTLS) have become essential for revealing operational inefficiencies—from forklift congestion to order-picking delays. Yet deploying an RTLS in a large, active facility presents significant challenges: harsh RF environments, limited power and network infrastructure, and the need to surface actionable insights from raw positioning data.
This whitepaper examines those challenges, compares prevailing RTLS technologies, and presents a reference design for a Bluetooth Low Energy (BLE)-based system that delivered a 20 percent reduction in average equipment traversal time. The design and lessons described here are intended to guide future RTLS initiatives in warehouse and industrial settings.
A major third-party logistics operator sought to increase pallets moved per hour but lacked concrete data to pinpoint bottlenecks. Anecdotal evidence suggested that forklift travel distances and aisle congestion were suppressing throughput, but without a location-tracking solution, meaningful improvement proved elusive.
The engagement began as a co-innovation venture: logistics subject-matter experts joined forces with technology specialists to address cross-industry challenges in warehouse efficiency. The primary objective was to unveil equipment movement patterns and deliver insights that could inform layout, slotting, and routing optimizations.
The deployment site spanned more than 500,000 sq ft. of high-racked storage, featuring dense metal shelving, mixed forklift types, and limited floor-level power or network connectivity. Order pickers operated walk-behind pallet jacks, inventory specialists used reach trucks, and stand-up forklifts handled heavier loads—all sharing narrow aisles.
Traditional RTLS platforms based on Wi-Fi or cellular infrastructure were ruled out due to infrastructure costs, uncertain coverage, and high power requirements. A low-power, long-range solution was required to support distributed sensors and mobile receivers throughout the facility.
An early proposal relied on computer vision (CV) to track forklifts via ceiling-mounted cameras. Although CV promised a non-intrusive footprint, identity persistence fell short once vehicles exited camera view. Attempts to scale camera coverage proved cost-prohibitive, and the delivery timeline could not accommodate extended trial periods.
Upon identifying CV limitations, the project shifted to a BLE-based RTLS. Approximately 150 BLE beacons were installed across the facility, strategically placed for maximum coverage. Material handling equipment was equipped with ESP32 microcontrollers, which scanned for beacon signals, measured RSSI (signal strength), and transmitted data over LTE-M (Cat-M) cellular to a cloud processing platform.
Key architectural elements:
Moving the positioning engine to the cloud—rather than relying on smartphone-based algorithms—enabled a lightweight, scalable edge design that minimized field maintenance and maximized data throughput.
RSSI-based positioning demands careful calibration. Deployment began with digital floorplans annotated with beacon locations. Field teams then adjusted placements for unplanned obstacles—misaligned walls, inaccessible rafters, or unusually high ceilings. A calibration phase involved walking predefined paths with reference devices to collect ground-truth signal patterns, refining the algorithm’s accuracy.
The live RTLS revealed several critical patterns:
Armed with these insights, the operator implemented one-way aisles, re-slotting of fast-moving SKUs, and adjusted routing logic. The result was a 20 percent reduction in average traversal time—a significant boost in throughput and labor efficiency.
| Technology | Accuracy | Infrastructure Cost | Maintenance Overhead | Notes |
|---|---|---|---|---|
| BLE RSSI | 3–5 m | Moderate (beacons only) | Low | Calibration-intensive, cost-effective at scale |
| BLE AoA (Angle of Arrival) | 1–2 m | Moderate–High | Medium | Improved accuracy, requires AoA-capable gateways |
| Wi-Fi RTT (802.11mc) | 1–2 m | High (Wi-Fi 6 APs) | Medium | Native to many devices, demands modern APs |
| UWB (Ultra-Wideband) | 10–30 cm | High (tags + anchors) | High | Best precision, higher total cost of ownership |
| Computer Vision | Variable | Medium–High (cameras) | Medium–High | Passive, but identity persistence and occlusion are challenges |
Emerging RTLS options such as BLE AoA and Wi-Fi RTT offer better accuracy while maintaining manageable infrastructure requirements. UWB remains the gold standard for precision but carries higher costs. Selection should align with the use-case’s tolerance for error, budget constraints, and infrastructure readiness.
Identify the exact performance indicators—e.g., travel time, throughput—and design the RTLS to deliver those insights.
Validate technology choices in the target environment with rapid, small-scale trials before full rollout.
Offloading complex algorithms to the cloud simplifies edge devices and accelerates future enhancements.
High-accuracy positioning hinges on detailed ground-truth data and iterative RF tuning.
Build infrastructure that can adapt to new standards—BLE AoA, Wi-Fi RTT, or UWB—without full hardware replacement.
This BLE-based RTLS reference design demonstrates that large-scale indoor positioning can be achieved without extensive infrastructure upgrades or prohibitive costs. By selecting technologies and architectures that align with real-world constraints—battery life, RF complexity, and operational timelines—organizations can unlock valuable insights and drive substantial efficiency gains in warehouse and logistics operations.
