SECOND SEMESTER

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5311 HC RESEARCH METHODS: DESIGN AND ANALYSIS

5325 STRATEGIC MANAGEMENT

5392 MACRO ORGANIZATIONAL BEHAVIOR

Predictors of Requisition Wait Time for Medical Supplies and Equipment

Study

This paper reports the results found when applying a multivariate approach to analyzing factors that influence requisition wait time (RWT) and its individual component: depot processing time (DPT), customer processing time (CPT), and in-transit time (ITT).  The study examines the relationship between traditional measures of logistics performance, stockage policies and requisition priority, and RWT.  The other variables in the study, location, commodity type, and time period, provide the context of the study.  The study involved medical supplies and equipment requisitioned by medical supply support activities engaged in Operations Enduring Freedom and Iraqi Freedom during December 2002 and January 2003.  A sample of 6,278 receipt transactions processed by the supply support activities (SSA) in Karshi, Uzbekistan and Doha, Qatar were compared against their supporting SSA in Pirmasens, Germany to analyze on average differences in RWT and variance accounted for by five constructs of RWT:  stockage policies, requisition priority, location, commodity type, and time-period. The study employed was retrospective in design and employed a hierarchical multivariate regression analysis.           The descriptive statistics revealed that on average, requisition wait time for an item ordered during OEF or OIF was nearly 20 days, half of which were accounted for by in transit time.  The components of RWT (DPT, CPT, and ITT) all has a significant degree of association with RWT and that as they increased, RWT also increased.  Stockage policies had the correlation with respect to RWT and DPT.  The average RWT for accommodated items was approximately half that of non-accommodated items.  This indicates that an increase in RWT could potentially affect a unit?s operational readiness.           The results of the hierarchical multivariate regression yielded a low R2, which indicated that the model failed to account for a large part of variance.  When the components of RWT were tested, the results indicated that stockage policy had the strongest effect on DPT.  DPT was the most stable element of RWT time and it was found that both CPT and ITT dilute the effects of DPT.  Therefore, when assessing metrics that affect overall RWT, emphasis should be placed on determining variables that affect DPT.           Three important findings were identified in the study.  The first involves the relationship between stockage policies and RWT and DPT.  It is imperative that managers ensure the correct materiel is maintained on hand in the SSAs.  If supported units order items maintained on hand, the SSAs will be able to reduce the overall RWT.  The second finding indicated that the average difference in RWT between accommodated and nonaccomodated supply requests decreased confidence in supply system and worked against the modular support strategy of the Army?s Combat Service Support (CSS) transformation.  The third finding of the study indicated that the Army?s traditional logistical performance measures influenced RWT more that the variables used in the study.  Therefore, the Army should consider developing new logistical performance measures that correlate better with RWT if wait times measures are to define the success of the distribution based logistics strategy for the CSS transformation.  Limitations to the study were that the not all the variables that affect RWT, DPT, CPT, and ITT were tested in the study.