Controlling and organizing the complex forest-to-consumer supply chain of wood fuels is a challenging task, especially for the chipping and transport processes. Truck mounted chippers and transport trailer-trucks must be scheduled to minimize delay to be profitable. Job management within the supply chain, including machine activity based controlling, offers a new way to increase efficiency and productivity. However, detailed data are required to detect and analyze potential gaps and improve forest fuel supply. Generally, data regarding the wood fuel supply chain process are obtained from extensive time studies that are based on a specific process step. Although time studies can detect details during the production of forest fuels, they only describe certain time frames. Long-term data that are recorded during the entire year could encompass seasonal and short term effects. This study aims to monitor the forest fuel supply processes (semi-automated), specifically regarding time and fuel consumption. Large data sets were automatically and efficiently gathered with little effort by drivers and operators. Data were recorded with fleet management equipment for more than 14 months. Vehicle data, including GPS data, were logged at an interval of one minute. Data management was conducted in a pre-configured database that contained pre-defined reports and were run by the Institute of Forest Engineering, Vienna. Work step assignments were implemented with Structured Query Language (SQL)-routines by using the raw machine activities data and GPS. The chipping and transport activities of more than 240 loads were analyzed by focusing on fuel consumption, time needed and traffic. The average distance between chipping sites and plants was approximately 54 kilometers. Fuel consumption from transport reached 50 l/100 km. The chipping unit reached a productivity of 12.8 odt/PSH15 and had a fuel consumption of 58 liters per operating hour.