An assessment of performance measures in child nutrition programs

School Child Nutrition Program (CNP) directors are expected to operate efficient, productive, and effective meals programs (Boehrer, 1993; Decker, et al. 1992; USDA, 2008). A variety of performance measures can be used to assess CNPs\u27 operational effectiveness. The purposes of this research were to investigate management and financial attributes perceived by foodservice directors of medium-size public school districts in the United States to impact performance measures for CNPs and investigate whether widely used standards for these performance measures, based on previous research, were relevant due to a variety of changes that had occurred in CNPs.;Two electronic questionnaires were sent to the population of 2,534 foodservice directors in medium-sized school districts (enrollments between 2,500 and 10,000). Respondents (n = 740) assessed attitudes about and use of productivity measures in their programs, use of convenience foods and disposable student-use items, factors that affect meals per labor hour (MPLH), director\u27s demographic information, and district characteristics. A convenience sample (n = 34) of foodservice directors provided detailed information about revenues, expenses, participation, and staffing. This information was used to calculate current MPLH for onsite kitchens, production kitchens serving more than one site, and satellite sites and revenue per dollar expense (RPDE).;Study results indicate increased usage of convenience foods and student-use disposable items. This is consistent with findings from previous studies that also indicated increased usage of convenience foods and disposables. Using a 5-point Likert-type scale, foodservice directors rated importance of 32 performance measures in evaluating their programs. These performance measures were grouped into nine factors: financial data (M = 4.9), financial statements (M = 4.8), food cost (M = 4.7), participation data (M = 4.7), meal cost ( M = 4.7), productivity ratios (M = 4.5), accounting data (M = 4.5), operating ratios (M = 4.4), and inventory data (M = 4.3). Directors also indicated frequency of use of these 32 performance measures. MPLH was the most frequently used productivity ratio. Male foodservice directors who worked for contract management companies used financial data more frequently than did female directors, whereas female foodservice directors who worked in self-operated programs used productivity performance measures more than did male directors. Directors perceived onsite supervisor\u27s effectiveness and experience highly influenced the kitchen\u27s productivity (ratings of 5.3 and 5.1, respectively on a 7-point scale).;The calculated mean MPLH (21.7 +/- 11.1) from all kitchens in the convenience sample was higher than MPLH from previous studies. There were slight increases in MPLH for onsite kitchens (17.1 +/- 7.7) and satellite sites (29.9 +/- 12.1), whereas production sites with more than one service site (18.5 +/- 5.4) had MPLH lower than established performance measures. School foodservice directors can evaluate kitchen productivity using MPLH as a performance measure.;There were no significant relationships determined between a partial-factor (MPLH) and a total-factor (RPDE) productivity measure. Findings from this study indicate the need for school foodservice directors to use two tools, one for kitchen productivity and one for overall financial profitability, in managing their programs. This study requested information from the 2006-07 school year. Since then, there have been a number of economic changes with impacts on school food costs, such as increased fuel costs, a decrease in availability of certain foods due to global trade issues, and increased production and processing costs due to growing concerns for food safety. With the new National School Lunch Program meal pattern expected by 2010 and the need for school foodservice programs to stay solvent, further research is needed to validate MPLH performance measures and explore the relationship between partial-factor and total-factor productivity measures