Purpose -Airplane technology is undergoing several exciting developments, particularly in avionics, material composites, and design tool capabilities, and, though there are many studies conducted on subsets of airplane technology, market, and economic parameters, few exist in forecasting new commercial aircraft model introduction. In fact, existing research indicates the difficulty in quantitatively forecasting commercial airplanes due in part to the complexity and quantity of exogenous factors which feed into commercial airplane introduction decisions. This paper seeks to address this gap.Design/methodology/approach -The analysis is based on a literature review, supplemented by a collection of secondary data. The study then focuses on applying three technology forecasting techniques: multiple regression; linear regression; and the Pearl growth curve. Findings -The results provide a valid model for multiple regression and linear regression on range and composite material percentage for use in commercial airplane forecasting. However, growth curve analysis, comparatively, appears to provide the most intriguing and flexible forecast outlook in alignment with industry dynamics.Research limitations/implications -Research implications include a caution for forecasters in support of the difficulty of commercial aircraft forecasting due in part to the quantity of exogenous factors, particularly compared with a related industry, military aircraft. Future work could include: utilizing other forecasting techniques that allow for greater numbers of forecast factors, additional future models, additional range aircraft and/or analyzing the impact that competing transportation modes in mid-range aircraft could have on long-range aircraft introduction.Originality/value -The study provides value in extending a previous descriptive paper on airplane parameters. Additionally, it appears to be one of the first quantitative examples supporting previous research indicating the complexity of forecasting airplane new product introduction, but it overcomes some of this complexity by providing a valid model for forecasting with range and composite material percentage as inputs.

The aim of this study is to provide a foundation for researchers and managers to discuss and resolve difficulties associated with research and development (R&D) target-setting. While multiple studies mention the difficulty of R&D target-setting, few have compiled reasons for these difficulties or addressed this issue in detail. This paper provides one of the first studies outlining reasons for R&D target-setting difficulties through a literature review. It also provides an initial set of analyses and results after applying an emerging quantitative method, Technology Forecasting Using Data Envelopment Analysis (TFDEA), to illustrate these difficulties step-by-step to commercial airplanes. Results include determining the state of art in commercial airplane technology and technological rate-of-change variants in setting R&D targets.is '. . . not yet well-established' (Lee and Song, 2007). Other studies mention R&D target-setting difficulties faced in attempting to set policy targets for science and technology in Israel (Trajtenberg, 2002) and Thailand (Sabhasri and Yuthavong, 1983). Several studies mention that the need to address R&D target-setting issues is timely and expected to increase in importance (Edler et al., 2002;. developed one of the more comprehensive frameworks which translates technol-bs_bs_banner R&D Management 42, 4, 2012.Note: If predicted efficiency at time of release = 1; then that model is expected to be efficient given the model output parameters used.Fcst, forecast; MAD, mean average ; PFE, passenger fuel efficiency; RoC, rate of change. R&D target-setting difficulties

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