Freight Rate Benchmarking



Freight Rate Benchmarking Case Study


How do you know that your freight rates are the lowest possible? Division management, with their eyes on the bottom line, demanded an answer to the question. This client, a division of a large corporation, enjoys the benefits of corporate buying power in transportation services and expected competitive rates. In this case, the preferred carriers (and rates) are defined and available through corporate, but the carriers and routing are not strictly imposed on the divisions. “Local” decisions on carrier selection and routing are allowed, and compliance with corporate routing is not measured. Benchmarking and analysis were to provide the answer and identify any opportunity to reduce cost and improve profitability. Expectations were that the exercise would confirm that freight rates were good.

The client recognized that overall transportation cost benchmarking would not answer the question. Both overall freight cost as a percent of sales and cost per hundredweight are the result of the rate structure, as well as a number of other significant factors that could not be ignored. The plan was to benchmark average rates within small groups of comparable benchmark data. These benchmark data were from manufacturers who shipped from one or two shipping locations to the 48 United States. This creates a comparison group where the distances shipped are similarly long, ranging from local to over 1,000 miles. Benchmarks were to be prepared for each shipment size range corresponding to the LTL weight breaks in the tariff. This technique eliminates any differences in average shipping size of the participating companies from the benchmarks. Companies with identical freight classes were selected.

The database for comparison included freight bill detail for all shipments made over a six-month time period. The base data included origin, destination, weight and freight charges. The specific carriers used for each shipment were known but not used in the benchmarking analysis. In addition, the mileage for each freight bill was posted to the database. Bills were coded for LTL and truckload and separate comparisons were done for each. The comparison metric was cents per hundredweight per mile ($/lb./mile).

The analysis reported the lowest rate, average rate and a calculation of the savings potential by using the lowest rate for all shipments in the comparison cell, a section of the database corresponding to comparable shipping regions and destination areas. Once the overall picture was assembled by combining these cells into a summary report, an overall assessment examined rate levels and produced maps to illustrate the geography of the rate comparisons. Picture a U.S. map image with red, blue and green “pushpins” placed at each shipping destination. Red pins are cities where average rates are 20 percent or more over the lowest rate. Green pins are 5 to 20 percent of the lowest rate. Blue pins are those where average rates are close to the lowest rate. These comparisons and maps can be prepared by shipping size and region of origin.

In this case, there were hardly any red pins. However, the overall rate savings potential on LTL was 12 percent and something less for TL. Regionally there were significant differences. Higher rate savings were possible for outbound shipments that were made from the plant in the “rust belt” region than from the more centrally located plant. There is an additional benefit from continuing carrier selection and rate negotiation work.