long-standing challenges of machine data fragmentation and unreliable connectivity in harsh environments.

The Problem We Solve Traditionally, digitalization in worksites has been hindered by fragmented machine data, unstable connectivity, and the difficulty of tracking costs in real-time. Conventional GPS trackers and CANbus systems often fail in remote areas where GSM coverage is non-existent. Tingz addresses these pain points with a holistic, hardware-enabled software solution designed for the most demanding operational conditions.

Our Technology and Infrastructure Tingz provides a GSM-independent infrastructure by deploying LoRaWAN base stations directly on worksites. This allows us to maintain a stable network even in deep mines or remote construction zones. We utilize specialized, rugged IoT sensors and electronic hardware tailored for construction machinery. Our capabilities include comprehensive data collection such as active, idle, or stop status, total engine hours, speed, and location. We also provide advanced telemetry by monitoring fuel levels, engine temperature, RPM, and specific mechanical activities like tipper positions or loader bucket movements. Our platform integrates with third-party systems for fuel management and personnel tracking, providing a unified data set. While using Thingsboard for IoT device management, we utilize Apache Superset for deep data analytics and reporting.

Value Proposition Tingz transforms raw machine data into actionable financial and operational insights. Through cost mapping, we match machine activities with specific cost codes used by our clients, allowing for precise calculation of machine costs per construction activity. By monitoring idling times and fuel consumption, we significantly reduce fuel waste and carbon emissions, promoting sustainability. We also track machine utilization rates and workshop visit frequencies to optimize fleet health. Furthermore, by correlating engine hours with fuel refueling data, we can detect potential fuel theft or technical malfunctions, ensuring operational security.

Proven Impact and Achievements Tingz has already demonstrated significant success in large-scale operations. In the Kemerkoy Lignite Mine project, one of Turkeys largest open-pit mines, Tingz connected over 350 machines to a LoRaWAN network. This implementation resulted in a 6 percent increase in fuel savings, more than 16,000 additional truck trips per month through optimization, and a reduction of 80 tons of CO2 emissions per month. Our work has been recognized by the Ministry of Environment and Urbanization, and our case studies have been featured globally by the LoRaWAN community.

Future Vision: AI-Driven Worksites Building on our robust data collection infrastructure, Tingz is now evolving to integrate Artificial Intelligence into our solution. Our goal is to move from descriptive analytics to predictive and prescriptive models, enabling autonomous site management and further maximizing operational efficiency. We are committed to leading the next generation of smart construction sites.