1 OSD Assessment of the Performance of the DoD Supply Chain Using the Strategic Distribution Database (SDDB) November 14, 2013

2 OSD Supply Chain Performance Assessment Is the mission capability of weapon systems degraded due to declining supply chain support? Is the supply chain supporting readiness and satisfying its customers in a timely manner? Is the supply chain providing consistent support to its customers and sustaining or improving readiness? Is inventory management within the supply chain improving? Is the supply chain providing cost- effective support to its customers and managing inventory in a cost- effective manner? Pipeline measurement is key to assessing the responsiveness and reliability of the DoD supply chain. Outcome/Attribute Construct Hypothesis: The health of the supply chain involves doing well in four critical performance areas while supporting the readiness outcome. Hypothesis: For an outcome or attribute to be “healthy”, its associated metrics must perform as expected. Assessment Approach: A metric is performing as expected if it is meeting its goal or is performing at an acceptable level.

3 LMARS and Responsiveness and Reliability Metrics LMARS Responsiveness: Logistics Response Time Reliability: Perfect Order Fulfillment SDDB Reliability: TDD Compliance Logistics response time and perfect order fulfillment measurements are based solely on LMARS data. SDDB uses LMARS data as a starting pointing but does some record filtering, data supplementing, and re-computing of pipeline segment times. TDD compliance measurements are based on SDDB data.

4 How the Assessment is Conducted Monthly Data Update Review of Metrics for Trends and Anomalies Deep Data Dives Monthly Metrics PMR Component Consultations Besides the metrics themselves, LMARS supports deep dive capability in the online LRT tool. The tool allows us to look at LRT over different times, to use dimensions to focus on specific sets of orders, and to collect statistics beyond mean times. In addition, the LRT tool allows us to work with LMARS data or SDDB data.

5 SDDB Plays a Key Role in the LRT Tool  We get SDDB from DORRA along with an “allsvc” file, which we use to include the customer’s country based on its DoDAAC.  We combine SDDB records with LMARS to get:  Joint LMARS/SDDB records  LMARS only records  SDDB only records LMARS Monthly Feed SDDB Monthly Feed DLIS/DORRA Feeds LMARS Enhanced File LRT-LMARS Cubes Combined LMARS/SDDB File LRT-SDDB Cubes LRT Tool

6 Joint LMARS/SDDB Records For joint records, we use SDDB segment times. Most of the segment time changes are in these OCONUS segments Port of Embarkation (POE) processing time In-transit from POE to POD time Port of Debarkation (POD) processing time In-theater in-transit time The next highest changes is in requisition submission times.

7 LMARS Only Records  Includes  Perishable records (54.9%)  Prime Medical Vendor records (26.6%)  Local Clothing Issues (5.4%)  Maintenance, Repair, and Operations records (0.8%)  Initial Outfitting records (0.3%) [excluded from LRT computations]  Local issues to maintenance (0.0%)  The other 11.1%  Orders placed on USSOCOM  Orders placed by civilian agencies (not DoD)  Subsistence orders not part of an LMARS special feed  Unshipped records  Other SDDB exclusions

8 SDDB Orders Only  SDDB closes records the LMARS has open (Army data).

9 In Summary  We use SDDB to drill down into  Pipeline segment data.  Customer command, base, country/state  Type of shipment (Air, surface, inter-theater, CONUS)