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Hubert, P.B.; Virtos, H.P.B.; Savage, Christopher J.; Maden, Will; Slater, W.; Bamford, Colin (2010)
Publisher: The Chartered Institute of Logistics and Transport UK
Languages: English
Types: Part of book or chapter of book
Subjects: HB, HD
Purpose\ud Road fleets’ profitability has been long correlated to fuel efficiency (McKinnon, 1993). In effect, fuel\ud expenditures are one of road transport operations’ biggest budgets (FBP, 2005) as well as an area\ud where improvements are generally possible (Wilson, 1987). In order to improve vehicles’ fuel\ud efficiency, fleet managers need methods which can accurately measure vehicles’ fuel performance.\ud Regardless whether fuel information is obtained from fuel cards or vehicles’ electronic solutions such\ud as CANbus, fuel efficiency is generally measured in miles per gallons (mpg). Yet, mpg does not\ud include all the factors necessary to its interpretation such as vehicle weight or age. Furthermore, other\ud aspects of fuel efficiency, such as fuel costs, are not directly reflected by mpg but instead by other\ud measures such as pence per mile (ppm). These limitations can potentially lead to situations where a\ud vehicle can be mpg efficient but ppm inefficient (and vice versa) making it hard for fleet manager to\ud understand how efficient their vehicles are. Thus there is a need for a method which can address\ud these limitations. Data Envelopment Analysis (DEA) – an advanced benchmarking technique – can\ud potentially address these limitations. Thus, this paper will discuss an application of DEA to van fuel\ud efficiency measurement.\ud Research Approach\ud The fuel efficiency DEA model originally included fuel volume, fuel cost, vehicle’s weight and vehicle’s\ud age as inputs while mileage was the only output. The fuel information, obtained from fuel cards\ud records, was cleansed using a cleansing algorithm partly relying on telematics information. Another\ud algorithm was also used to appraise the volume used during the measurement period (the smoothing\ud algorithm). Data from three different companies’ fleets was used in this study.\ud Findings and Originality\ud The results indicate that DEA can address mpg’s limitations while effectively measuring van fuel\ud efficiency. No vehicle was found to be simultaneously mpg efficient and ppm inefficient (and vice\ud versa); thus using either volume or cost, provided similar efficiency levels. Vehicle weight was kept in\ud the model as it proved to have a significant impact on fuel efficiency while age seemed to further\ud segment the results in a way which fleet operators defined as incoherent with the notion of fuel\ud efficiency. Vehicle age was thus excluded from the models. Results from the smoothing algorithm\ud suggest smoothing the volume used is indispensable when using fuel cards.\ud Although DEA has been widely used in transport operations, the literature mainly concentrates on\ud ports or airports (Cullinane et al., 2006, SangHyun, 2009, Yoshida and Fujimoto, 2004, Pestana\ud Barros and Dieke, 2007) rather than directly on road transport (Yang and Pollitt, 2009). Only a limited\ud number of papers can be found dealing with the use of DEA to measure road operations efficiency\ud (Hjalmarsson and Odeck, 1996, Odeck and Hjalmarsson, 1996, Kerstens, 1996, Cowie and Asenova,\ud 1999) and, except for this study, none could be found on van operations or fuel efficiency\ud measurement. This lack of published research brings originality to this study.\ud Research Impact\ud This case study demonstrates that it is possible to use DEA to incorporate ‘vehicle weight’ in the fuel\ud efficiency model in order to provide a better and more comparable vans’ fuel efficiency measure than\ud with simple mpg measurement.\ud Practical Impact\ud Fleet operators understood the model results and appreciated the fact the measure incorporated\ud vehicle weight. However, the debriefing discussions seemed to indicate that fleet managers were\ud more concerned about spotting very bad drivers and fuel theft rather than accurate fuel efficiency\ud measurement per se. These concerns were partially addressed by the fuel card data cleansing and\ud smoothing algorithm. Finally, recent success of driver competitions (Masternaut Three X, 2010) seem\ud to indicate there is a latent need in the industry for accurate driver performance measurement which\ud suggests that methods such as the one developed in this study could be of greater use in a near\ud future.
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