Exploring Performance Based Logistics Predictors of Earned Value Management Outcomes: A Quantitative Study

The study addressed the problem that it is not known to what extent interdependency exists between earned value management and performance-based logistics service contracts. The purpose of the quantitative correlation study was to examine relationships between performance-based logistics metrics of operational readiness rate, reliability growth rate, and depot mean down time with the earned value management metrics of schedule performance index and the cost performance index. The study analyzed the census data from the Shadow 200 performance-based logistics program from November 2004 through February 2012. A quantitative hypothesis testing methodology comprised of intercorrelation and multiple linear regression to compare data from earned value management and performance-based logistics service contract metrics. Spearman's coefficient of correlation was used to identify the predictor variable's high versus low probability of correlation. A three-predictor multiple linear regression model using operational readiness rate, reliability growth rate, and depot mean downtime was used to examine the relationships with all three predictors as analyzed concurrently with the outcome variables. Three significant correlations were identified from the correlation analysis. A significant correlation was found between operational readiness rate and the schedule performance index (rs=-0.212; p=0.048). The relationship between operational readiness rate and the reliability growth rate was found to be significant (rs=-0.280; p=0.019), and the relationship between depot mean downtime and the cost performance index was also significant (rs=0.497; p<.05). The operational readiness rate, reliability growth rate, and depot mean downtime combined did significantly predict the cost performance index (p=0.001). There was a significant interaction effect existed between schedule and cost performance indices. There was a significant difference between the population means of the schedule and cost performance indices within the Shadow 200 performance-based logistics program. Additional research to quantify the influence of performance-based logistics monetary incentives may help researchers understand how these incentives effect performance metrics. Further research may also provide a better understanding for researchers examining the effect of incentives on performance metrics as comparative studies between performance-based logistics and traditional lifecycle support logistics programs.