For Trasteel, we developed a digital monitoring platform to ensure production plans are executed on time and in full. The system leverages image processing to track on-site activities in real-time, measuring adherence to production schedules. Built with Angular, Node.js, and Python, this solution digitized planning and task tracking, significantly increasing transparency and traceability in their production processes.
To optimize the vehicle flow between TOFAŞ's paint shop and assembly lines, we engineered a custom optimization application. The software is designed to maximize sequencing efficiency at switch points, dynamically adapting to complex production rules. Developed end-to-end using an Angular frontend, a .NET API backend, and Python-based optimization algorithms, our solution boosted vehicle throughput from a 55-65% efficiency rate to an impressive 95-100%.
We digitized the manual laboratory analysis processes at Organik Kimya. By implementing a camera-assisted measurement system, we prevented incorrect sample evaluations and designed the process for real-time integration with their ERP system. Our team handled the development of both the hardware cabinet and the software using Angular, .NET, and Python. This transformation made analyses repeatable and traceable, minimizing human error by digitally recording all data.
For Volt Elektrik Motorları, we developed a camera-assisted software solution that enables real-time quality control in stator production. The system instantly detects manufacturing defects and sends immediate notifications to operators. This image processing-based quality control system, built with Angular and Python, reduced production errors by 90%, leading to a significant increase in operational efficiency and quality levels.
We built a predictive maintenance system for TOFAŞ's CNC machines that forecasts the remaining lifespan of cutting tools and detects potential failures in advance. By analyzing sensor data in real-time, the system allows for proactive maintenance scheduling. Developed using ThingWorx IIoT, .NET, and Python, the solution reduced unplanned downtime and optimized tool lifecycle management.
For Hyundai, we created a flexible production planning platform that operates based on real-time order and inventory data. Its integrated design provides end-to-end traceability throughout the entire production process. This custom planning software, developed with .NET and Python, increased planning success rates from 60% to over 85%, resulting in fewer production delays and higher delivery accuracy.
We enabled post-sales digital monitoring of motor performance and health for Volt Elektrik Motorları by embedding sensors in their products. The system provides customized analysis dashboards to enhance customer satisfaction. Our team developed both the hardware and the analysis software using Angular, .NET, and Python. This infrastructure lowered post-sales service costs and led to a noticeable increase in customer satisfaction.
For OPET, we developed a system that uses a thermal camera to inspect the heat-sealing process on lubricant bottle caps. By analyzing thermal data in real-time, the system identifies faulty seals during production. The project involved integrating the thermal camera system with custom analysis software built using Angular, .NET, and Python, which improved sealing quality and reduced the scrap rate.
We ensured brand-specific quality standards for OPET by implementing a camera-based system that inspects the orientation, position, and integrity of product labels during production. Visual data is automatically analyzed to remove incorrectly labeled products from the line. The project involved developing an image processing control system and installing automation hardware, using Angular, .NET, and Python. This significantly reduced labeling errors and protected brand perception.
