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Chapter 2: Selecting Benchmarking Partners to contract out work, the number of crews to assign to various maintenance activities and different locations, the types and configurations of resources to apply, and when the work should be performed. 3. As a rule of thumb, you should benchmark at the lowest level at which you can survey customers practically and economically to obtain statistically valid measures of their satisfaction with a product or service. In a state agency, this is likely to be an area approximately the size of a county (however, today most state agencies do not have statistically valid customer satisfaction data below the district level, such as at the county or area level). The same practical and economic considerations apply to condition or level-of-service data obtained from a random sample of roadway sections used in a Maintenance Quality Assurance Process described under NCHRP Project 14-12 (published as NCHRP Web Document 8). 4. Generally, a crew or a roadway section should not be the benchmarking unit; the benchmarking unit should encompass the activities of a number of crews. There are exceptions. For example, it is reasonable to benchmark at the crew level if benchmarking is going to focus on the performance of specialized crews serving a broad area. Examples of specialized crews are sign or striping crews. Sometimes, it is also reasonable for a roadway section to be a benchmarking unit if it is part of a tollway or if it is a road that a contractor is maintaining. NUMBER OF BENCHMARKING PARTNERS How many benchmarking partners will you need? The number of partners is not the issue; the number of benchmarking units is the important number. You want enough benchmarking units among the partners to be able to differentiate levels of performance and to identify best practices within different environments (i.e., rural and urban areas). If you apply mathematical or statistical procedures, the techniques may have properties that depend on the number of observations (i.e., benchmarking units) to achieve a desired resolution, accuracy, or statistical significance. 36