BASING RATE ADJUSTMENTS FOR MOTOR CARRIERS ON STATISTICAL EVIDENCE

INTRODUCTION Freight carriers, operating in a deregulated business environment, engage in a form of value-based pricing. They set their base rates and then negotiate individual customer discounts while considering the costs of providing service, competitive pressures, and the anticipated value of the customer relationship. They strive to reach different market segments with differentiated service characteristics and with flexible pricing mechanisms, thus deriving revenues from some premium services, capturing business from competitors and achieving a higher utilization of corporate assets. Airlines, hotels and rental cars engage in a similar form of "yield management" as they set spot rates for restricted fares and offer weekend specials, perhaps with greater consideration to customers' willingness to pay. In such competitive environments with their peculiar pricing mechanisms, freight carriers need periodically to examine the results of their rate structures and discounting practices to determine the net effects of their pricing and service decisions and to adapt corporate strategies "accordingly. In doing so, they must systematically address key questions such as: 1. How has the organization's business evolved throughout the transportation network? 2. Are there imbalances in the use of facilities and equipment? 3. How do rates vary throughout the service system? How are they related to market characteristics? 4. Are the effective rates at specific terminal origins, terminal destinations, or for specific customers, commensurate with the services delivered? 5. How should rates he adjusted at certain locations, on particular shipping lanes, or for particular customers or groups of customers? In this article, the authors describe the development and use of analytical tools that were created to help a motor carrier address such questions. The company provides time-definite delivery services for less-than-truckload (LTL) shipments among a network of terminals located throughout the U.S. and parts of Canada. Although the focus is on the operations of a large North American motor carrier, the basic approaches employed and the issues confronted are relevant to companies in many competitive service industries. The presentation illustrates the use of standard statistical tools to extract information from computer records of bills of lading in order to: 1. Present a comprehensive picture of carrier activities and sources of revenue 2. Establish benchmarks for rates and revenues commensurate with services delivered 3. Identity terminals, shipping lanes and customers that may require managerial attention or intervention 4. Design a program of customer support and rate adjustments to improve corporate performance. The process represents a form of data mining for pricing decisions. It involves the production of comprehensive statistical summaries that provide overviews of corporate performance in several dimensions, the creation of statistical (regression) models for explaining variation in performance, and the use of the resulting information to develop strategies for rate adjustments. The work can be accomplished with standard statistical software and data management tools. BACKGROUND In the two decades since deregulation of the U.S. interstate trucking industry, an array of alternative services has emerged for less-than-truckload (LTL) shipments involving traditional LTL carriers; truckload (TL) carriers who "top-off partially filled trailers on a contract basis; private carriers who contract for use of backhaul capacity; freight forwarders and consolidators; express package deliverers; railroads and airlines with trucking alliances, etc. (Elzinga, 1994). Shippers weigh numerous characteristics of the terms and quality of service when selecting a carrier (Lambert et al., 1993). On one hand, larger carriers use sophisticated information technology and stronger credit lines to competitive advantage, resulting in greater industrial concentration (Rakowski, 1988; Boyer, 1993). …