Benchmarking the Operational Efficiency of Major U.S. Trucking Firms Using Data Envelopment Analysis

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INTRODUCTION

The trucking industry in the United States has historically operated on profit margins as low as 3 to 4 cents on every dollar of sales after taxes, compared to the 7 to 9% average profit margin experienced by the heavy manufacturing industry (Dun and Bradstreet, 1999; Lambert and Min, 2000). Recently, the profit margin of the industry declined further, from 3.08% in 1994 to 2.60% in 1999 (American Trucking Associations Economics and Statistic Group, 2001). With tight profit margins and increasing competition, a key to a trucking firm's survival is its ability to keep trucking operations "lean." Sustaining lean operations, however, is not easy given mounting cost pressures from rising fuel costs, taxes, insurance, and labor. For example, the national average price of diesel fuel spiked to $1,491 per gallon in 2000 from $1,044 per gallon in 1998. In addition, for-hire carriers paid 8.4% more in federal highway-user taxes in 1999 than in 1998 (American Trucking Associations Economics and Statistics Group, 2001). Those trucking firms that could not handle steep cost increases outpacing revenue growth failed to survive in the end. In 2000 alone, 3,670 trucking firms went out of business. This alarming statistic represents an increase of 205.8% in trucking business failures from the previous year (American Trucking Associations Economics and Statistics Group, 2001).

One way of improving the operational efficiency of trucking firms is to learn from best practice firms that can be identified by setting a reliable financial performance standard. Examples of such a standard are a financial audit, an industry norm, and a benchmark. Since a trucking firm needs to measure its financial performance relative to its competitors to constantly strengthen its market position, benchmarking seems to be the most effective way of setting a reliable financial standard and then measuring the operational efficiency of the trucking firm.

In general, benchmarking is a continuous quality improvement process by which an organization can assess its internal strengths and weaknesses, evaluate comparative advantages of leading competitors, identify the best practices of industry leaders, and incorporate these findings into a strategic action plan geared to gain a position of superiority (Min and Galle, 1996). The main goals of benchmarking are to:

* Identify key performance measures for each function of a business operation;

* Measure one's own internal performance levels as well as those of the leading competitors;

* Compare performance levels and identify areas of comparative advantages and disadvantages;

* Implement programs to close a performance gap between internal operations and the leading competitors (Furey 1987, p.30).

In setting the benchmark, this paper will measure the operational efficiency of trucking firms relative to prior periods and their competitors. The operational efficiency measured by input/output ratios can reflect the true overall productivity of trucking firms better than traditional financial ratios that tend to focus on myopic aspects of financial performance. As a way of comparatively assessing the productivity of trucking firms with multiple inputs and outputs, this research uses data envelopment analysis (DEA), which was successfully explored in measuring the operational efficiency of banks (e.g., Thanassoulis, 1999), hospitals (Valdmanis, 1992), nursing homes (Kleinsorge and Karney, 1992), purchasing departments (Murphy et al., 1996), cellular manufacturing (Talluri et al, 1997), travel demand (Nozick et al., 1998), information technology investments (Shafer and Byrd, 2000), customer service performances of less-than-truckload (LTL) motor carriers (Poli and Scheraga, 2000) and international ports (Tongzon, 2001). For further details on other DEA applications, interested readers should refer to Seiford (1990).

In general, DEA is referred to as a linear programming (non-parametric) technique that converts multiple incommensurable inputs and outputs of each decision-making unit (DMU) into a scalar measure of operational efficiency, relative to its competing DMU's. Herein, DMU's refer to the collection of private firms, non-profit organizations, departments, administrative units, and groups with the same (or similar) goals, functions, standards and market segments. DEA is designed to identify the best practice DMU without a priori knowledge of which inputs and outputs are most important in determining an efficiency measure (i.e., score), and assess the extent of inefficiency for all other DMU's that are not regarded as the best practice DMU's (e.g., Charnes et al., 1978). Since DEA provides a relative measure, it will only differentiate the least efficient DMU from the set of all DMU's. Thus, the best practice (most efficient) DMU is rated as an efficiency score of one, whereas all other less efficient DMU's are scored somewhere between zero and one. To summarize, DEA determines the following (Sherman and Ladino, 1995):

* The best practice DMU tha t uses the least resources to provide its products or …