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

2. 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 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

3. 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?

4. 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

5. 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).

6. 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

7. 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

8. 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

9. Comparison of Percentiles (Slide 2 of 2)

10. 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.

11. Illustration of trends not changing by breakdown
Looking into IPG 1

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

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

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

15. 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.

16. 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.

17. 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