LMARS Reporting of Logistics Response Time (i.e., Total Pipeline Time) Research for Assigned Action at Pipeline PRC Meeting on 15 December 2016

Logistics Response Time (LRT) DNA Definition Diagnostic Metric: A measurement of the total elapsed time (in days) from customer requisition to receipt of materiel ordered from a DoD organic or commercial source of supply. Supply Chain Attribute Responsiveness: Timely receipt of materiel demanded from wholesale sources of supply to retail activities and end-use customers is key to DoD supply chain performance. Business Value This metric: indicates timeliness of the wholesale echelon of supply and distribution system and responsiveness to their customers – the largest segment being retail activities relates to negotiated standards for responsiveness (e.g., TDD Standards) Metrics Guide Pages 39-41 Status of SCMG Actions Completed Pending Completion Problem Definition in Policy Definition is in both draft DODM 4140.01-V10 and Supply Chain Metrics Guide Computation LRT is measured from the date the retail supply activity requisition is submitted to a DoD or designated commercial source of supply until the date the requisitioned materiel is received and posted in the materiel management system of the requisitioner. Data Source Logistics Metrics Analysis Reporting System (LMARS) - DLA Transaction Services submits LMARS records to SCI. OSD Data Requirement Frequency: Monthly Content: LMARS records contain the order entry and departure dates as shipments move through segments that comprise the total pipeline time (LRT) and also contain other details of shipments. Goals and Trend Analysis Goals: No goal exists for this metric due to dependency on other variables (TDDs, Supply Availability Goals, etc.). Trend: A downward trend in logistics response time indicates faster service, which is positive, while an upward trend is negative. Display LRT by source of supply over time with drilldowns into: CONUS segment times by source of supply Geographic COCOM Various other miscellaneous displays for analysis Challenges and Way Ahead None

Research Question Should LMARS change its reporting of the 95% mean for Total Pipeline Time (TPT)? Focal questions What are we trying to measure? Does the 95% mean allow us to do that or should it be changed?

Data Overview The data suggests that 50% or more of total requisitioned materiel is received within 2 days of the order. Over 70% of materiel is received within the first 10 days from order date

Data Overview When we measure LRT, we are taking the number of records and the date and computing an average and a 95% average (i.e., the average of the observed times less those times that make up the top 5%). These averages are heavily influenced by the records within 2 days and by records considered to be outliers (i.e., 180 days).

Methods to finding a time that is representative of the data Traditional Method Statisticians often use measures of central tendencies. (mean, median, or mode) to represent observed data. When mean, median, and mode are relatively the same, they are representative of a normal distribution. In This Case – Mode Definition: The number that appears most often in a data set Finding for TPT: 2 Median Definition: The midpoint number of a data set Finding for TPT: 2 Mean The aggregate sum divided by the count of the data set. Finding for TPT: variable

List of possible uses for an aggregate LRT If the current averages aren’t representative, what can they be used for? The question of how to analyze the data cannot truly be addressed without determining what we are trying to accomplish with the use of LRT data. List of possible uses for an aggregate LRT Measuring how quickly all requisitions are filled Identifying trends Measuring differences between the average at different percentiles can help to determine if there are significant shifts in the data

Comparison of Percentiles (Slide 1 of 2) Graphs illustrate average LRT by month Trends seem to be consistent regardless of percentile chosen Largest difference is seen in LRT average itself

Comparison of Percentiles (Slide 2 of 2)

Comparison of Percentiles Even differences of 90% of the data and all of the data show a similar trend, even with drastically different average LRT times.

Illustration of trends not changing by breakdown Looking into IPG 1

Illustration of trends not changing by breakdown Looking into breakdown by military service (Army) Average days Army 98%

Illustration of trends not changing by breakdown Looking into breakdown by CONUS

Illustration of trends not changing by breakdown Looking into breakdown by OCONUS

Use of LRT Percentiles While LRT analysis generally look for trends showing increasing times or anomalies or jumps in averages, one use of different percentile averages is to access differences in the tails of time distributions. For example, if the monthly LRT averages for the 95 percentile and the 99 percentile are increasing towards the 99 percentage, this indicates that the times between the 95 percentile and the 99 percentile are increasing towards the times in the top 1% of the times. If the averages for the 99 percentile are decreasing towards the averages for the 95%, this indicates that the top 1% times are decreasing.

Conclusion Even differences of 90% of the data and all of the data show a similar trend, even with drastically different average LRT times. The trends that will be noticed occur regardless of the percentile.

LRT Percentile Recommendation We do not believe that changing the percentiles used will lead to better analysis or identification of a new trend. Recommendation: No change