Modified C-17 taxi procedures: a fuel cost savings exploration

Abstract

Purpose – This study aims to explore the viability of using C-17 reduced-engine taxi procedures from a cost savings and capability perspective. Design/methodology/approach – This study model expected engine fuel flow based on the number of operational engines, aircraft gross weight (GW) and average aircraft groundspeed. Using this model, the research executes a cost savings simulation estimating the expected annual savings produced by the proposed taxi methodology. Operational and safety risks are also considered. Findings – The results indicate that significant fuel and costs savings are available via the employment of reduced-engine taxi procedures. On an annual basis, the mobility air force has the capacity to save approximately 1.18 million gallons of jet fuel per year ($2.66m in annual fuel costs at current rates) without significant risk to operations. The two-engine taxi methodology has the ability to generate capable taxi thrust for a maximum GW C-17 with nearly zero risks. Research limitations/implications – This research was limited to C-17 procedures and efficiency improvements specifically, although it suggests that other military aircraft could benefit from these findings as is evident in the commercial airline industry. Practical implications – This research recommends coordination with the original equipment manufacturer to rework checklists and flight manuals, development of a fleet-wide training program and evaluation of future aircraft recapitalization requirements intended to exploit and maximize aircraft surface operation savings. Originality/value – If implemented, the proposed changes would benefit the society as government resources could be spent elsewhere and the impact on the environment would be reduced. This research conducted a rigorous analysis of the suitability of implementing a civilian airline’s best practice into US Air Force operations.

Keywords

• air transportation
• simulation
• aircraft procedures
• cost savings
• fuel savings
• least-squares regression