FLIGHT-X can calculate the Direct Operating Costs (DOC) of the airplane, on the basis of over 40+ cost parameters, which are established in a configuration file that can be found in the airplane model sub-folder. The parameters that are considered in the DOC analysis include:

Currency, aircraft cost, current fuel price, fuel price inflation, fuel shock price and corresponding fiscal year, insurance costs, spare parts on category I (airframe, landing gear, tyres), spare parts on category II (engines, APU, lubricants), spare parts on category III (avionics, systems, modernisation, refurbishment); aircraft life-time, depreciation, financing, interest rates, mortgage life-time, crew costs on category I (pilots and co-pilots), crew costs on category II (flight attendants), off-station costs (overnight out of position); labour rates on category I (power plant), labour rates on category II (in house), labour rates on category III (outsourced); flight services costs, ground services costs, average stage length. FLIGHT-X estimates the following:

• Number of pilots required for operating the aircraft year round.
• Number of flight attendants required for operating the aircraft year round.
• Number of cycles and airplane utilisation.
• Off-station crew nights.
• Fuel burn per cycle.
• Block time per cycle.
• Cost per available seat mile (CASM).
• DOC/n-mile flown.
• Ticket price required to break even, with and without commercial cargo.

FLIGHT-X accounts for the following externalities:

• Increase in fuel consumption due to deterioration and engine wash.
• Any other eventuality, which can be included in a mission scenario.

The figure below shows the estimated ticket price required to break even for a hypothetic aircraft costing 27 million dollars, acquired with internal financing (20%) and external financing (80%) over a period of 10 years, assuming that the aircraft operates as a commuter over stage lengths of 200 n-miles without significant commercial freight. A shock oil price of 25% is forecast on year 2. A risk analysis is possible by using stochastic data of the parameters. Figure 1 shows a typical result.