Returnable Small Load Carriers (SLCs) are a cornerstone of sustainable logistics, yet their multi-site cycles often lack transparency due to data scarcity and technological constraints. This counts at least for SLCs without label. Our latest research introduces a novel, sensor-based approach to collect, extrapolate, and simulate location data for SLCs—enabling realistic cycle simulations and data-driven decision-making without requiring full-scale infrastructure investments.

Most SLC cycles with unlabeled SLCs suffer from:

• No real-time tracking due to the high volume and low cost of individual SLCs.

• Reluctance to share data among cycle partners, limiting simulation accuracy.

• Technological gaps in indoor/outdoor localization for small, unlabeled containers.

Our Idea - Sensor-Based Data Collection & Extrapolation

We tested three localization technologies (GSM, BLE/GPS, and A-GPS) in real-world SLC cycles to identify the most cost-effective, scalable, and accurate solution. See, how we did this:

1. Technology Comparison

• GSM tags (low-cost, outdoor-only) struggled with indoor localization and battery life.

• Apple AirTags (BLE/GPS) offered high precision but required manual data extraction and lacked industrial scalability.

• Industrial A-GPS tags (with WiFi sniffing) provided seamless indoor/outdoor transitions but needed additional infrastructure for full IPS (Indoor Positioning System) functionality.

2. Data Extrapolation with PERT & Monte Carlo Simulation

• Combined sensor data with navigation APIs (Google/Apple Maps) to estimate minimum, maximum, and most likely (mode) throughput times.

• Used PERT distributions (instead of triangular distributions) to reduce bias from extreme values and improve simulation accuracy.

• Ran 10,000 Monte Carlo simulations per tag to identify underperforming routes and optimization opportunities.

3. Key Insights from Simulation:

• Finding underperformance and overperformannce cycle runs.

• Possible planning adjustment.



Why This Matters for Industry & Research

• For Logistics Services Providers: Our approach enables low-cost, scalable tracking of SLCs without full-cycle sensor deployment, reducing risks and costs.

• For Sustainability: Better data leads to optimized SLC cycles, increasing reuse rates and reducing single-use packaging waste.

• For Academics: We provide a reproducible methodology for simulating multi-site SLC cycles—bridging the gap between theoretical models and real-world data.



What we will analyze next:

• How can seasonal patterns (e.g., demand fluctuations) be integrated into simulations?

• What’s the optimal sample size for sensor-based SLC tracking?

• Can AI/ML models further refine throughput time predictions?



Our work is a step toward smart, data-driven SLC management—proving that even small data samples can unlock big insights for sustainability and efficiency.



🔗 Read the full paper for technical details and results:  [Full paper]