Order Response Time Mike McClure, 402 SCMS/GUSB

1. Order Response Time Mike McClure, 402 SCMS/GUSB 12 Mar 2013

2. Order Response Time Agenda • Review AFGLSC monthly ORT charts • Currently distributed to OSD, AFMC, Depot Mx, AFGLSC, DLA • Mainly at the working level, with some going up to leadership as requested • Presentation normally contains 16 slides of performance, today only 5 slides • Review some recent ORT analysis • What we are doing with ORT and what is it telling us? • Back-up Slides - Improving the CWT Metric • ORT developed by AFGLSC to overcome measurement and analysis issues with average CWT • Uses both closed and open orders ….. combines two traditional measures, CWT with open BOs • Uses customer order date, not closed date ….. real time, not delayed • Uses percentile buckets ….. much more accurate than the average of a non-normal distribution

3. Order Response Time Mike McClure, 402 SCMS/GUSB 13 Feb 2012

4. Measuring Enterprise Performance • Responsivenessto all customer demands • AFGLSC, DLA, and Other SOS provides spares to customers • Best option is to have the part locally for immediate issue • Not all parts available locally, so backorder response time is key to deliver part within customer expectations • Order Response Time (ORT) • Measures immediate issue rate (same day) and backorder response times AFGLSC 402nd, LIMS-EV Pipeline Analysis

5. Measuring Enterprise Performance Order Response Time (ORT), an improved CWT look • Order Response Time (ORT) • previously called Calculated Issue Effectiveness (CIE) • What’s the data source? • LIMS-EV CWT data • What’s different than CWT? • Open orders included • Performance grouped by open date, not closed date • Why ORT? • Real-time responsiveness measure • Much more accurate indicator of current support • Customer focused by measuring percentage of immediate issues and how long backorders are taking • True “Tier” measure can be applied at multiple Organizational levels • How to read the chart? • The date axis represents customer order date • Black line represents total customer orders • Solid colors represent % of orders closed within time period • White % numbers represents % immediate issues • What about the goal? • TDD like standard (customer gets x% of parts in y days) • Calculated cumulative wait time (by order) • What does this metric tell us? • How often does a mechanic get a part when they order it? • If not, how long does it take to fill an order? • How many orders are placed in a given month? • ORT shows the real time, direct impact of supply support to the customer

6. Depot Mx(available by MXW, MXG, Mx shops, workload & supply codes)Order Response Time AFGLSC 402nd, LIMS-EV Pipeline Analysis, DLA Emall

7. Operational Bases (available by WS, location & supply codes)Order Response Time AFGLSC 402nd, LIMS-EV Pipeline Analysis, DLA Emall

8. Order Response TimeTDD goal applied • ORT measure the percent of customer orders meeting their TDD standards • ORT includes both open & closed orders in the month they were ordered • The dotted black line shows the ORT goal (read on the left axis) • The white diamonds & values show the percent of orders filled within their TDD standard (read on the left axis) • Solid black line shows the count of orders received (read on the right axis) • Colors show the percent of orders that either passed or failed during the month • Dark Green/Pass – Base Issue = Immediately filled • Light Green /Pass – Closed = Filled during the month within standard • Light Red/Failed – Closed = Filled, but exceeded their TDD standard • Dark Red/Failed – Open = Not filled and have already passed their TDD standard • Yellow/Pass – Open = Not filled, but have not exceeded their TDD standard • TDD Standards: These standards are specific to the geographic location of the customer, the priority of the order, and the mode of transportation with one exception: AFGLSC uses a 2-day standard for all depot maintenance orders

9. Depot MxOrder Response Time Depot TDD std set to 2 days AFGLSC 402nd, LIMS-EV Pipeline Analysis, DLA Emall

10. Operational BasesOrder Response Time – IPG 1 DoD Operational TDD std AFGLSC 402nd, LIMS-EV Pipeline Analysis, DLA Emall

11. Operational Bases Order Response Time – IPG 2 DoD Operational TDD std AFGLSC 402nd, LIMS-EV Pipeline Analysis, DLA Emall

12. Order Response Time Additional Analysis Examples

13. Additional Analysis ExamplesMICAPs Analysis Source: SMART & LIMS-EV

14. Additional Analysis ExamplesTier down to MD/MDS, Command, Base, SOS, etc. Analysis Source: SMART & LIMS-EV

15. Additional Analysis ExamplesBad Actors (identifyNIINs, analyze, work root causes, chart)

16. Questions?

17. Improving the CWT Metric Mike McClure Operations Research Analyst AFGLSC 402 SCMS/GUSB

18. BLUF • Customer Wait Time (CWT) is an established metric, but is sensitive to extreme-value effects, volume bias and is lagging in nature • Order Response Time (ORT) has been designed to be more illustrative and actionable of real-time customer support ORT was designed to overcome the shortfalls of CWT

19. Customer Wait Time (CWT) Customer Wait Time (CWT) • Defined in DoD 4140.61.3.2…a measurement of the total elapsed time between the issuance of a customer order and satisfaction of that order • Data is restricted to closed orders • Excludes open orders, cancellations, and partial fills – significant INFORMATION loss! • Presented in the month the order was closed, therefore reflects problems after the fact • Typically, top-driver issues have already been fixed Order 1 Order 2 Order 3 Order 4 M2 M3 M1 When CWT is calculated in M2, both order 3 and 4 are omitted from CWT calculation

20. Customer Wait Time (CWT) Typical CWT distribution Customer Wait Time (CWT) • Typically reported as an average with a target • Average is skewed by extremes – Penalty for closing old orders! • Missing a target could be a function of a single bad actor out of 100,000+ orders • Average is skewed by volume change, immediate issues offset longstanding backorders • Significantly more meaningful in percentile buckets Volume Extreme values Average is a poor representation of the CWT distribution

21. Contribution of Closed Orders to CWT + + Next to impossible to interpret real trends Volume skew Extreme value skew Issue date aggregation

22. Order Response Time (ORT) Order Response Time • The percent of orders falling within pre-designated wait time buckets, DoD’s real intent? • LIMS-EV CWT data source • Both open and closed orders • Data attributed to the customer order date • Open orders seen as they age, data will update until the 90+ day population establishes itself • Performance directly attributed to the period in which it happened, making trend analysis valid • Not skewed by extreme values or by large volume changes • Goals established using a TDD like standard (customer gets x% of parts in y days) or by calculating a cumulative wait time (by order) • Actionable, top-drivers can be restricted to aging open orders, the future drivers of CWT • True “Tier” measure can be applied at multiple Organizational levels • Customer-focused, real-time responsiveness measure Order 1 Order 2 Order 3 Order 4 M2 M3 M1 When ORT is calculated in M2, M3 is still unknown, but order 3 and 4 current is used

23. Order Response TimeExamples

24. ORT Top DriversExample Bad actors • ORT Analysis • Found the worst performers for last 18 months for AF SOS ORT • Upper left chart is looking at just the bad actors • Lower left chart are when bad actors are filtered out • AF SOS constraints analyzed from the bad actors are below Best performers